Shale gas well production dynamic prediction method

Abstract

The invention discloses a shale gas well production dynamic prediction method which comprises the following steps: setting a research area of shale gas exploitation, obtaining production data of an exploited shale gas well in the research area, and collecting geologic characteristic parameters of the exploited shale gas well in the exploitation process; fitting a gas production quantity influence model of the influence effect of the geologic characteristic parameters on the gas production quantity; a new shale gas production point is taken in the research area, the horizontal well is divided into a plurality of fracturing sections according to the designed length of the horizontal well, and the theoretical gas production amount of the first fracturing section is calculated from the vertical well; the gas production amount of the second fracturing section is calculated and predicted; the gas production amount of the third fracturing section is calculated and predicted; and the total gas production amount of the designed horizontal well can be predicted until the gas production amounts of all the predicted fractured sections in the designed horizontal well are calculated in sequence. In the prediction process of a new shale gas well to be developed, dynamic prediction is sequentially carried out step by step according to the designed fracturing sections, and the prediction accuracy can be further improved.

Description

Technical Field

[0001] The present invention relates to the field of shale gas exploitation, and particularly to a method for predicting the production dynamics of shale gas wells. Background Art

[0002] Shale gas reservoirs are unconventional oil and gas reservoirs occurring in organic-rich mudstones, mainly in adsorbed and free states. This is the result of the natural gas gathering near the source rock formation after generation, showing a typical "in-situ" reservoir formation mode. This "in-situ" storage method also brings great trouble to exploitation. Therefore, for a long time, it has not entered the scope of being exploitable. In recent years, with the continuous expansion of social demand for clean energy, the continuous increase in natural gas prices, and the continuous deepening of the understanding of shale gas reservoirs, the scientific and energy communities have begun to understand shale gas from a new perspective and developed a series of technologies for exploiting shale gas.

[0003] Due to the high development cost and great development difficulty of shale gas, it is particularly important to predict the gas production before shale gas development. However, most of the existing predictions of shale gas production are in the primary stage, the results of shale gas productivity analysis are not accurate enough, and there is little historical production data of shale gas wells for reference. With the increasing intensity of shale gas development in recent years, more and more shale gas wells have entered the commercial development stage, providing support for the prediction of newly designed shale gas wells. Therefore, it is urgent to develop a method for predicting the production dynamics of shale gas wells. Summary of the Invention

[0004] Aiming at the above deficiencies of the prior art, the present invention provides a method for predicting the production dynamics of shale gas wells, effectively improving the accuracy of predicting the gas production of shale gas wells.

[0005] To achieve the above invention purpose, the technical solution adopted by the present invention is as follows:

[0006] Provide a method for predicting the production dynamics of shale gas wells, which includes the following steps:

[0007] S1: Set the research area for shale gas exploitation, obtain the production data of the exploited shale gas wells in the research area, and collect the geological characteristic parameters of the exploited shale gas wells during the exploitation process. The geological characteristic parameters include the organic matter content data y, porosity data K, permeability data s, and gas content data h corresponding to the staged fracturing exploitation of horizontal wells;

[0008] S2: Fit the gas production influence model of the influence effect of geological characteristic parameters on the gas production;

[0009] S3: Select a new shale gas extraction point within the study area, and design the depth of the vertical well and the drilling direction and length of the horizontal well. Split the horizontal well into several fracturing sections according to the designed length of the horizontal well, and calculate the theoretical gas production of the first fracturing section starting from the vertical well.

[0010] S4: Survey the organic matter content data, porosity data, permeability data, and gas content data of the second fracturing section, calculate the fluctuation values Δy1, ΔK1, Δs1, Δh1 of the organic matter content data, porosity data, permeability data, and gas content data between the second fracturing section and the first fracturing section, and calculate the predicted gas production Q2 of the second fracturing section.

[0011] S5: Repeat step S4, survey the organic matter content data, porosity data, permeability data, and gas content data of the third fracturing section, and replace the theoretical gas production of the first fracturing section with the gas production Q2 of the second fracturing section. Calculate the predicted gas production Q3 of the third fracturing section.

[0012] S6: Until the gas production of all predicted fracturing sections within the designed horizontal well is calculated in sequence, and the sum of the gas production of all predicted fracturing sections is the predicted total gas production of the designed horizontal well.

[0013] Further, step S2 includes:

[0014] S21: Use the horizontal well length L and gas production Q of the already exploited shale gas wells within the study area to calculate the unit gas production q per unit horizontal well length: Obtain the unit gas production data (q1, q2,..., q n ), where q n is the unit gas production of the nth already exploited shale gas well, and n is the number of the already exploited shale gas wells;

[0015] S22: Screen out the maximum value q n from the unit gas production data (q1, q2,..., q max ), and extract the geological characteristic parameters of the already exploited shale gas well corresponding to the maximum value q max as the standard geological characteristic parameters (y′, K′, s′, h′). The already exploited shale gas well corresponding to the maximum value q max is used as the optimal shale gas extraction well, and the others are used as ordinary shale gas extraction wells;

[0016] S23: Calculate the difference between the unit gas production of each ordinary shale gas extraction well and the maximum value q max to obtain the unit gas production error data (Δq1, Δq2,..., Δq i ) corresponding to each ordinary shale gas extraction well; Δqi = q max -q i , i = n - 1, q i is the gas production per unit volume of a conventional shale gas well, i is the number of the conventional shale gas well, and Δq i is the difference between the gas production per unit volume of the i-th conventional shale gas well and the maximum value q max ;

[0017] S24: According to the geological characteristic parameters (y i , K i , s i , h i ) of the conventional shale gas well, calculate the error data from the standard geological characteristic parameters (y′, K′, s′, h′), and establish a relationship model between the geological characteristic parameters and the error of gas production per unit volume;

[0018]

[0019] where k1, k2, k3, k4 are the influence coefficients of organic matter content data, porosity data, permeability data, and gas content data on gas production respectively, η is the error coefficient during the detection of organic matter content, v is the Poisson's ratio, E is the elastic modulus of the shale layer, p is the initial pressure of the horizontal well, p0 is the pressure at the end of gas production of the horizontal well, γ is the gradient coefficient of porosity data with pressure fluctuation, and r i is the radius of the horizontal well;

[0020] S25: Input the gas production per unit volume error data (Δq1, Δq2,..., Δq i ) corresponding to each conventional shale gas well and the geological characteristic parameters (y i , K i , s i , h i , r i ) into the relationship model respectively, fit out the m influence coefficients k1, k2, k3, k4, and take the average value of the influence coefficients to obtain the fitted gas production influence model:

[0021]

[0022] where is the theoretical value of gas production, l is the unit length of the horizontal well, and Δy, ΔK, Δs, Δh are the fluctuation values of organic matter content data, porosity data, permeability data, and gas content data respectively.

[0023] Furthermore, the method for calculating the theoretical gas production of the first fracturing stage in step S3 is:

[0024]

[0025] Wherein, Z1 is the compressibility factor under standard conditions, H is the gas reservoir thickness surveyed for the designed horizontal well, T1 is the temperature under standard conditions, ψ1 is the pseudopressure corresponding to the formation pressure surveyed in the designed horizontal well, ψ2 is the pseudopressure corresponding to the bottom-hole flowing pressure, P1 is the pressure under standard conditions, T is the original shale temperature of the first fracturing stage, s″ is the shale permeability surveyed in the first fracturing stage, x″ is the optimal half-length of the fracture designed in the first fracturing stage, x′ is the reservoir width surveyed in the first fracturing stage, s′ is the permeability of the optimal fracture, r1 is the radius of the first fracturing stage, and L is the length of the first fracturing stage.

[0026] Further, step S4 includes:

[0027] S41: Survey the organic matter content data, porosity data, permeability data, and gas content data of the second fracturing stage, and calculate the fluctuation values Δy1, ΔK1, Δs1, Δh1 of the organic matter content data, porosity data, permeability data, and gas content data between the second fracturing stage and the first fracturing stage;

[0028]

[0029] Wherein, y1, K1, s1, h1 are respectively the organic matter content data, porosity data, permeability data, and gas content data of the first fracturing stage, y2, K2, s2, h2 are respectively the organic matter content data, porosity data, permeability data, and gas content data of the second fracturing stage, v′ is the Poisson's ratio of the shale in the second fracturing stage, E′ is the elastic modulus of the shale layer in the second fracturing stage, Δp is the theoretical gas pressure fluctuation value during the gas production process in the second fracturing stage, and r2 is the radius of the second fracturing stage;

[0030] S42: Input the theoretical gas production volume of the first fracturing stage and the corresponding fluctuation values Δy1, ΔK1, Δs1, Δh1 of the second fracturing stage into the fitted gas production volume influence model, and calculate the predicted gas production volume Q2 of the second fracturing stage:

[0031]

[0032] The beneficial effects of the present invention are as follows: Based on the data of the production of shale gas wells developed in the study area and the production volume, a production volume influence model is established. According to the differences in the geological characteristic parameters and the production volume of multiple shale gas wells, each coefficient of the production volume influence model is fitted. Following the characteristic that the gas storage volume and the geological characteristic parameters of the shale layer in the same study area have small differences, the accuracy of fitting the production volume influence model can be effectively improved, ensuring the accuracy of the production volume influence model in predicting the production volume of newly planned shale gas wells. At the same time, during the prediction process of newly planned shale gas wells, dynamic prediction is carried out step by step according to the designed fracturing sections, which can further improve the prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flowchart of a method for dynamically predicting the production of shale gas wells. DETAILED DESCRIPTION OF THE INVENTION

[0034] The following describes the specific embodiments of the present invention to facilitate those skilled in the art of the present technology to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art of the present technology, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions made using the concept of the present invention are within the scope of protection.

[0035] As Figure 1 shown, a method for dynamically predicting the production of shale gas wells includes the following steps:

[0036] S1: Set the research area for shale gas production, obtain the production data of the shale gas wells that have been produced in the research area, and collect the geological characteristic parameters of the shale gas wells that have been produced during the production process. The geological characteristic parameters include the organic matter content data y, porosity data K, permeability data s, and gas content data h corresponding to the staged fracturing production of horizontal wells;

[0037] The geological characteristics of shale gas mainly include aspects such as the lithological characteristics of shale, pore structure, permeability, gas content, and geological structure and sedimentary environment. The lithological characteristics of shale have an important impact on the reservoir characteristics of shale gas. The lithology of shale should have a high organic matter abundance so that a large amount of natural gas can be released after fracturing operations. The pore structure of shale also has an important impact on the permeability of the shale gas reservoir. Generally speaking, the larger the porosity, the better the permeability, which is more beneficial for the production of shale gas. The gas content in shale is also one of the important parameters for evaluating the potential of shale gas. Generally speaking, the higher the gas content, the greater the potential of shale gas. Geological structure and sedimentary environment have an important impact on the distribution and accumulation of shale gas. Comprehensive analysis of geological structure and sedimentary environment can help determine the distribution law and sweet spot range of shale gas.

[0038] S2: A gas production impact model that fits the impact of geological characteristic parameters on gas production; Step S2 specifically includes:

[0039] S21: Using the horizontal well length L and gas production Q of the already exploited shale gas wells in the research area, calculate the unit gas production q per unit horizontal well length: Obtain the unit gas production data (q1, q2,..., q n ) of all the already exploited shale gas wells in the research area, where q n is the unit gas production of the nth already exploited shale gas well, and n is the number of the already exploited shale gas wells;

[0040] S22: Screen out the maximum value q n from the unit gas production data (q1, q2,..., q max ), and extract the geological characteristic parameters of the already exploited shale gas well corresponding to the maximum value q max as the standard geological characteristic parameters (y′, K′, s′, h′). The already exploited shale gas well corresponding to the maximum value q max is used as the optimal exploited shale gas well, and the rest are used as ordinary exploited shale gas wells;

[0041] S23: Calculate the difference between the unit gas production of each ordinary exploited shale gas well and the maximum value q max to obtain the unit gas production error data (Δq1, Δq2,..., Δq i ) corresponding to each ordinary exploited shale gas well; Δq i = q max - q i , i = n - 1, where q i is the unit gas production of the ordinary exploited shale gas well, i is the number of the ordinary exploited shale gas well, and Δq i is the difference between the unit gas production of the ith ordinary exploited shale gas well and the maximum value q max ;

[0042] S24: According to the geological characteristic parameters (y i , K i , s i , h i ) of the ordinary exploited shale gas wells, calculate the error data with the standard geological characteristic parameters (y′, K′, s′, h′), and establish a relationship model between the geological characteristic data and the unit gas production error;

[0043]

[0044] Among them, k1, k2, k3, and k4 are the influence coefficients of organic matter content data, porosity data, permeability data, and gas content data on gas production volume respectively, η is the error coefficient during the detection of organic matter content, v is the Poisson's ratio, E is the elastic modulus of the shale layer, p is the initial pressure of the horizontal well, p0 is the pressure at the end of gas production from the horizontal well, γ is the gradient coefficient of porosity data fluctuating with pressure, and r i is the radius of the horizontal well;

[0045] S25: Input the unit gas production error data (Δq1, Δq2, …, Δq i ) corresponding to each ordinary shale gas production well and the geological characteristic parameters (y i , K i , s i , h i , r i ) into the relationship model respectively, fit out m influence coefficients k1, k2, k3, k4, and take the average value of the influence coefficients to obtain the fitted gas production volume influence model:

[0046]

[0047] Among them, is the theoretical value of gas production volume, l is the unit length of the horizontal well, and Δy, ΔK, Δs, and Δh are the fluctuation values of organic matter content data, porosity data, permeability data, and gas content data respectively.

[0048] S3: Select new shale gas production points within the research area, design the depth of the vertical well and the drilling direction and length of the horizontal well, split the horizontal well into several fracturing segments according to the designed length of the horizontal well, and calculate the theoretical gas production volume of the first fracturing segment starting from the vertical well The calculation method of the theoretical gas production volume of the first fracturing segment is as follows:

[0049]

[0050] Among them, Z1 is the standard state compressibility factor, H is the gas reservoir thickness surveyed by the designed horizontal well, T1 is the standard state temperature, ψ1 is the pseudo-pressure corresponding to the formation pressure surveyed in the designed horizontal well, ψ2 is the pseudo-pressure corresponding to the bottom hole flowing pressure, P1 is the standard state pressure, T is the original shale temperature of the first fracturing segment, s″ is the shale permeability surveyed within the first fracturing segment, x″ is the optimal fracture half-length designed within the first fracturing segment, x′ is the reservoir width surveyed within the first fracturing segment, s′ is the permeability of the optimal fracture, r1 is the radius of the first fracturing segment, and L is the length of the first fracturing segment.

[0051] S4: Survey the organic matter content data, porosity data, permeability data, and gas content data of the second fracturing stage, calculate the fluctuation values Δy1, ΔK1, Δs1, Δh1 of the organic matter content data, porosity data, permeability data, and gas content data between the second fracturing stage and the first fracturing stage, and calculate the gas production volume Q2 of the predicted second fracturing stage;

[0052] Step S4 specifically includes:

[0053] S41: Survey the organic matter content data, porosity data, permeability data, and gas content data of the second fracturing stage, and calculate the fluctuation values Δy1, ΔK1, Δs1, Δh1 of the organic matter content data, porosity data, permeability data, and gas content data between the second fracturing stage and the first fracturing stage;

[0054]

[0055] Among them, y1, K1, s1, h1 are respectively the organic matter content data, porosity data, permeability data, and gas content data of the first fracturing stage, y2, K2, s2, h2 are respectively the organic matter content data, porosity data, permeability data, and gas content data of the second fracturing stage, v′ is the Poisson's ratio of the shale in the second fracturing stage, E′ is the elastic modulus of the shale layer in the second fracturing stage, Δp is the theoretical gas pressure fluctuation value during the gas production process in the second fracturing stage, and r2 is the radius of the second fracturing stage;

[0056] S42: Input the theoretical gas production volume of the first fracturing stage and the corresponding fluctuation values Δy1, ΔK1, Δs1, Δh1 of the second fracturing stage into the fitted gas production volume influence model, and calculate the predicted gas production volume Q2 of the second fracturing stage:

[0057]

[0058] S5: Repeat step S4, survey the organic matter content data, porosity data, permeability data, and gas content data of the third fracturing stage, and replace the theoretical gas production volume of the first fracturing stage with the gas production volume Q2 of the second fracturing stage and calculate the predicted gas production volume Q3 of the third fracturing stage; Using the gas production volume Q2 of the second fracturing stage as the theoretical gas production volume value for predicting the gas production volume of the third fracturing stage can effectively improve the overall prediction accuracy. The third fracturing stage is close to the second fracturing stage and is more similar in geological characteristics.

[0059] S6: Until the gas production volumes of all predicted fracturing stages in the designed horizontal well are calculated in sequence, and the sum of the gas production volumes of all predicted fracturing stages is the predicted total gas production volume of the designed horizontal well.

[0060] The present invention establishes a gas production volume influence model based on the data of the exploitation of shale gas wells developed historically in the research area, fits the coefficients of the gas production volume influence model according to the differences in the geological characteristic parameters and the exploitation volumes of multiple shale gas wells, and follows the characteristic that the gas storage volumes and geological characteristic parameters of shale layers in the same research area have small differences, which can effectively improve the fitting accuracy of the gas production volume influence model and ensure the accuracy of the gas production volume prediction of the newly planned shale gas wells to be developed. At the same time, during the prediction process of the newly planned shale gas wells to be developed, sequential dynamic prediction is carried out step by step according to the designed fracturing sections, which can further improve the prediction accuracy.

Claims

1. A shale gas well production dynamic prediction method, characterized in that: The following steps are involved: S1: Set the research area for shale gas exploitation, obtain the production data of the shale gas wells that have been exploited in the research area, and collect the geological characteristic parameters of the shale gas wells that have been exploited during the exploitation process. The geological characteristic parameters include organic matter content data y, porosity data K, permeability data s and gas content data h corresponding to the horizontal well staged fracturing exploitation; S2: Gas production impact model that fits the effect of geological characteristic parameters on gas production; S3: Select new shale gas production points in the study area, design the depth of vertical wells and the drilling direction and length of horizontal wells, split the horizontal wells into several fracturing stages according to the designed length of the horizontal wells, and calculate the theoretical gas production of the first fracturing stage starting from the vertical wells S4: Survey the organic matter content data, porosity data, permeability data and gas content data of the second fracturing stage, calculate the fluctuation values ​​Δy1, ΔK1, Δs1, Δh1 of the organic matter content data, porosity data, permeability data and gas content data of the second fracturing stage and the first fracturing stage, and calculate the predicted gas production Q2 of the second fracturing stage; S5: Repeat step S4 to survey the organic matter content data, porosity data, permeability data and gas content data of the third fracturing stage, and replace the theoretical gas production of the first fracturing stage with the gas production Q2 of the second fracturing stage Calculate the predicted gas production Q3 of the third fracturing stage; S6: until the gas production of all predicted fracturing sections in the designed horizontal well is calculated in sequence, the sum of the gas production of all predicted fracturing sections is the predicted total gas production of the designed horizontal well.

2. The shale gas well production dynamic prediction method according to claim 1, characterized in that: The step S2 comprises: S21: Using the horizontal well length L and gas production Q of the shale gas wells that have been mined in the study area, calculate the unit gas production q per unit horizontal well length: Obtain the unit gas production data (q1, q2, ..., q n ), q n is the unit gas production of the nth produced shale gas well, and n is the number of the produced shale gas well; S22: Filter out unit gas production data (q1, q2, ..., q n ) max , and extract the maximum value q max The corresponding geological characteristic parameters of the shale gas well that has been mined are used as standard geological characteristic parameters (y′, K′, s′, h′), with a maximum value of q max The corresponding shale gas wells that have been mined are regarded as the optimal shale gas wells for mined production, and the others are regarded as ordinary shale gas wells for mined production; S23: Calculate the unit gas production and maximum value q of each common shale gas well max The difference between the two values ​​is used to obtain the unit gas production error data (Δq1, Δq2, …, Δq i );Δq i =q max -q i ,i=n-1,q i is the unit gas production of a common shale gas well, i is the number of the common shale gas well, Δq i is the unit gas production of the i-th common shale gas well and the maximum value q max The difference between S24: According to the geological characteristic parameters of common shale gas wells (y i ,K i ,s i ,h i ), calculate the error data with the standard geological characteristic parameters (y′, K′, s′, h′), and establish the relationship model between the geological characteristic parameters and the unit gas production error; Among them, k1, k2, k3, k4 are the influence coefficients of organic matter content data, porosity data, permeability data and gas content data on gas production, η is the error coefficient of organic matter content detection, v is Poisson's ratio, E is the elastic modulus of the shale layer, p is the initial pressure of the horizontal well, p0 is the pressure at the end of gas production in the horizontal well, γ is the gradient coefficient of porosity data with pressure fluctuation, r i is the radius of the horizontal well; S25: The unit gas production error data (Δq1, Δq2, …, Δq i ) and geological characteristic parameters (y i ,K i ,s i ,h i ,r i ) are input into the relationship model respectively, and m influence coefficients k1, k2, k3, k4 are fitted, and the average value of the influence coefficients is taken The fitted gas production impact model is obtained: in, is the theoretical value of gas production, l is the unit length of the horizontal well, Δy, ΔK, Δs, and Δh are the fluctuation values ​​of organic matter content data, porosity data, permeability data, and gas content data, respectively.

3. The shale gas well production dynamic prediction method according to claim 2, characterized in that: The theoretical gas production of the first fracturing stage is calculated in step S3. The method is: Among them, Z1 is the standard state pressure factor, H is the gas reservoir thickness of the designed horizontal well survey, T1 is the standard state temperature, ψ1 is the pseudo-pressure corresponding to the formation pressure surveyed in the designed horizontal well, ψ2 is the pseudo-pressure corresponding to the bottom hole flow pressure, P1 is the standard state pressure, T is the original shale temperature of the first fracturing stage, s″ is the shale permeability surveyed in the first fracturing stage, x″ is the optimal fracture half-length designed in the first fracturing stage, x′ is the reservoir width surveyed in the first fracturing stage, s′ is the permeability of the optimal fracture, r1 is the radius of the first fracturing stage, and L is the length of the first fracturing stage.

4. The shale gas well production dynamic prediction method according to claim 3, characterized in that: The step S4 comprises: S41: Surveying the organic matter content data, porosity data, permeability data and gas content data of the second fracturing stage, and calculating the fluctuation values ​​Δy1, ΔK1, Δs1, Δh1 of the organic matter content data, porosity data, permeability data and gas content data of the second fracturing stage and the first fracturing stage; Among them, y1, K1, s1, h1 are respectively the organic matter content data, porosity data, permeability data and gas content data of the first fracturing stage, y2, K2, s2, h2 are respectively the organic matter content data, porosity data, permeability data and gas content data of the second fracturing stage, v′ is the Poisson's ratio of the shale in the second fracturing stage, E′ is the elastic modulus of the shale layer in the second fracturing stage, Δp is the theoretical gas pressure fluctuation value during the gas production process in the second fracturing stage, and r2 is the radius of the second fracturing stage; S42: The theoretical gas production of the first fracturing stage The fluctuation values ​​Δy1, ΔK1, Δs1, and Δh1 corresponding to the second fracturing stage are input into the fitted gas production impact model to calculate the predicted gas production Q2 of the second fracturing stage:

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