A pre-drilling EUR prediction method based on the fracture network model of shale gas horizontal wells
Through the pre-drill EUR prediction method based on the shale gas horizontal well fracturing net model, the problem of low accuracy in shale gas reserve prediction is solved, and more accurate prediction of the total gas content resource of shale gas is achieved, supporting the pre-drill economic benefit evaluation.
Patent Information
- Application Number
- CN202411139933.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-20
- Publication Date
- 2025-05-16
- Estimated Expiration
- 2044-08-20
AI Technical Summary
In the prior art, the prediction of the final recoverable reserves of shale gas reservoirs has problems of low accuracy and large error, which affects the evaluation of economic benefits before drilling of shale gas horizontal wells.
The pre-drilling EUR prediction method based on the shale gas horizontal well fracturing net model was adopted. By obtaining geological information, the fracturing net model was constructed, the seam control transformation volume and geological parameter distribution were determined, and the total gas-containing resource of shale gas was calculated.
It improves the accuracy of geological reserve prediction of shale gas reservoirs, reduces errors, provides more accurate prediction of total shale gas gas resources, and supports pre-drill economic benefits evaluation.
Smart Images
Figure CN119047171B_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of gas reservoir engineering, and in particular to a pre-drilling EUR prediction method based on a shale gas horizontal well hydraulic fracture network model. Background Art
[0002] EUR (Estimated Ultimate Recovery) is the estimated ultimate recoverable volume. It is used to predict the total amount of oil and gas that an oil or gas well can produce during its entire life cycle. This indicator is crucial for evaluating the commercial value and development potential of an oil well. The estimation of EUR involves a variety of methods, including analytical modeling, numerical simulation, material balance, modern production decline method, empirical production decline method, and probability method.
[0003] The effect of fracturing directly affects the ultimate recoverable reserves of shale gas. In the existing technology, the prediction of the ultimate recoverable reserves of shale gas reservoirs faces many technical problems and challenges. For example, the calculation method of the geological reserves of shale gas reservoirs is usually a rough estimate, resulting in low accuracy and large errors. Such research is of reference value for the pre-drilling economic benefit evaluation of shale gas horizontal wells, thereby providing technical guidance for the efficient development of shale gas. Summary of the invention
[0004] To solve the above problems, the present invention provides a pre-drilling EUR prediction method based on a shale gas horizontal well hydraulic fracture network model, comprising the following steps:
[0005] Acquire first geological information and second geological information of a target mining area;
[0006] Based on the first geological information, a shale gas horizontal well hydraulic fracture network model is constructed;
[0007] Based on the first geological information, determining the fracture-controlled transformation volume of the target mining area;
[0008] Determine a first geological parameter distribution point set in a target mining area based on the geological parameters and hydraulic fracture height in the second geological information;
[0009] Determining a second geological parameter point set in the target mining area based on a first geological parameter distribution point set in the target mining area;
[0010] Determining a second geological information variation function based on a second geological parameter point set in the target mining area;
[0011] Based on the second geological information variation function and the shale gas horizontal well hydraulic fracture network model, determining the average value of the second geological information in the shale gas horizontal well hydraulic fracture network model;
[0012] The total shale gas resources are determined based on the fracture-controlled transformation volume and the average value of the second geological information.
[0013] In some embodiments, the first geological information includes: the maximum lateral extension length of the fracture, the maximum longitudinal distance of the volume element from the wellbore, and the length of the fracture section.
[0014] In some embodiments, the calculation method of the shale gas horizontal well hydraulic fracture network model is:
[0015]
[0016] (1), Y is the longitudinal distance between the volume element and the wellbore; M is the maximum transverse extension length of the fracture; N is the maximum longitudinal distance between the volume element and the wellbore; and X is the transverse extension length of the fracture at the height Y position from the wellbore.
[0017] In some embodiments, the calculation method of the seam-controlled transformation volume is:
[0018]
[0019] (2), V is the fracture control stimulation volume, n is the number of fracturing stages, and L is the length of the fracturing stage.
[0020] In some embodiments, the geological parameters in the second geological information include: shale porosity, shale adsorbed gas content, gas saturation and shale mass density.
[0021] In some embodiments, the second geological parameter point set in the target mining area is obtained after interpolation processing based on the first geological parameter distribution point set in the target mining area;
[0022] The interpolation processing method is to interpolate through a Gaussian random function to generate random values that conform to the Gaussian distribution.
[0023] In some embodiments, determining the second geological information variation function comprises:
[0024] Obtain multiple geological parameter prediction models;
[0025] For each of the plurality of geological parameter prediction models, determining a degree of fit between the second geological information variation function and the geological parameter prediction model;
[0026] Determining a target geological parameter prediction model from a plurality of geological parameter prediction models based on the fit between the second geological information variation function and each geological parameter prediction model;
[0027] Based on the target geological parameter prediction model, a second geological information variation function is determined.
[0028] In some embodiments, the second geological information average value is an average value of geological parameters in the second geological information in the shale gas horizontal well hydraulic fracture network model;
[0029] The second geological information average value includes: an average value of shale mass density, an average value of shale porosity, an average value of gas saturation and an average value of shale gas volume coefficient.
[0030] In some embodiments, the total shale gas resources are calculated as follows:
[0031] G X =Den*V*C X
[0032] G Y =S g *Φ*V / B g
[0033] G Z =G X +G Y (3)
[0034] (3) In G Z is the total shale gas resources; G X is the amount of shale gas adsorbed gas resources; G Y is the free gas resources of shale gas; Den is the average mass density of shale; Φ is the average porosity of shale; S g is the average value of gas saturation; B g is the average value of shale gas volume coefficient.
[0035] A pre-drilling EUR prediction method system based on a shale gas horizontal well hydraulic fracture network model is also provided, which is used to execute the aforementioned pre-drilling EUR prediction method based on a shale gas horizontal well hydraulic fracture network model, comprising:
[0036] An acquisition module, for acquiring first geological information and second geological information of a target mining area;
[0037] The first geological information includes: the maximum lateral extension length of the hydraulic fracture, the maximum longitudinal distance of the volume element from the wellbore, and the length of the hydraulic fracture section;
[0038] The geological parameters in the second geological information include: shale porosity, shale adsorbed gas content, gas saturation and shale mass density;
[0039] A construction module is used to construct a shale gas horizontal well fracture network model;
[0040] The first determination module is used to determine the fracture control transformation volume of the target mining area;
[0041] A second determination module is used to determine the average value of the second geological information in the shale gas horizontal well hydraulic fracture network model;
[0042] The third determination module is used to determine the total gas resources of shale gas.
[0043] By adopting the above technical solution, the present invention mainly has the following technical effects:
[0044] By calculating the amount of adsorbed gas resources and free gas resources of shale gas to calculate the total gas resources of shale gas, the technical problems of low accuracy and large errors in the existing technology for roughly estimating the geological reserves of shale gas reservoirs are solved, and the technical effect of predicting the total gas resources of shale gas is achieved. BRIEF DESCRIPTION OF THE DRAWINGS
[0045] Figure 1 This is a flow chart of a pre-drilling EUR prediction method based on a shale gas horizontal well hydraulic fracture network model of the present invention;
[0046] Figure 2 It is a schematic diagram of a shale gas horizontal well hydraulic fracture network model in a pre-drilling EUR prediction method based on a shale gas horizontal well hydraulic fracture network model of the present invention. DETAILED DESCRIPTION
[0047] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.
[0048] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
[0049] As shown in this specification, unless the context clearly indicates an exception, the words "a", "an", "a kind" and / or "the" do not refer to the singular, but also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.
[0050] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.
[0051] See also Figure 1-Figure 2 The present invention provides a pre-drilling EUR prediction method based on a shale gas horizontal well hydraulic fracture network model, comprising the following steps:
[0052] S1. Acquire first geological information and second geological information of the target mining area;
[0053] Figure 2 is a cross-sectional view of a target mining area according to some embodiments of this specification;
[0054] like Figure 2 As shown, the target production area is a fracture extending along the wellbore; in some embodiments, the first geological information includes: the maximum lateral extension length of the fracture, the maximum longitudinal distance of the volume element from the wellbore, and the length of the fracture section.
[0055] In some embodiments, the first geological information can be obtained after exploration by combining with Kinetix shale software simulation.
[0056] S2. constructing a shale gas horizontal well hydraulic fracture network model based on the first geological information;
[0057] The calculation method of the shale gas horizontal well fracture network model is as follows:
[0058]
[0059] (1), Y is the longitudinal distance between the volume element and the wellbore; M is the maximum transverse extension length of the hydraulic fracture; N is the maximum longitudinal distance between the volume element and the wellbore; X is the transverse extension length of the hydraulic fracture at the height Y position from the wellbore;
[0060] S3. Determine the fracture-controlled transformation volume of the target mining area based on the first geological information;
[0061] In some embodiments, the calculation method of the seam-controlled transformation volume is:
[0062]
[0063] (2), V is the fracture control stimulation volume, n is the number of fracturing stages, and L is the length of the fracturing stage.
[0064] In some embodiments, the fracture-controlled stimulation volume is a parameter characterizing the volume of the oil and gas layer controlled by the fracture network formed by fracturing means.
[0065] S4, determining a first geological parameter distribution point set in the target mining area based on the geological parameters and the hydraulic fracture height in the second geological information;
[0066] In some embodiments, the geological parameters in the second geological information include: shale porosity, shale adsorbed gas content, gas saturation and shale mass density. Among them, the shale porosity is a parameter that characterizes the proportion of pore space in the rock, which has a direct impact on the permeability and fluid storage capacity of shale gas wells; the shale adsorbed gas content is a parameter that characterizes the content of adsorbed natural gas in the shale reservoir, reflecting the amount of natural gas stored in the shale reservoir in the form of adsorption; the gas saturation is a parameter used to describe the state of gas occurrence in the oil and gas reservoir, characterizing the degree to which the pore space of the reservoir is filled with gas; the shale mass density is a parameter used to characterize the physical properties of shale rock, reflecting the mass of shale per unit volume.
[0067] In some embodiments, the geological parameters in the second geological information may be obtained based on adjacent wells or logging exploration. In some embodiments, the hydraulic fracture height is a parameter that characterizes the vertical distance reached by the fracture extending upward or downward from the wellbore.
[0068] S5. Determine a second geological parameter point set in the target mining area based on the first geological parameter distribution point set in the target mining area;
[0069] In some embodiments, the second geological parameter point set in the target mining area is obtained after interpolation processing based on the first geological parameter distribution point set in the target mining area;
[0070] In some embodiments, the interpolation process may be performed by interpolating a Gaussian random function on a set of known data points to generate random values that conform to the Gaussian distribution.
[0071] S6. Determine a second geological information variation function based on a second geological parameter point set in the target mining area;
[0072] In some embodiments, the second geological information variation function is a variation curve of geological parameters in the second geological information in relation to hydraulic fracture height.
[0073] In some embodiments, the second geological information change function can be obtained by processing the second geological parameter point set by mathematical fitting, artificial intelligence, etc. In some embodiments, the second geological parameter point set can be determined based on the second geological parameter point set by a geological parameter prediction model. The specific description of the geological parameter prediction model will be further described below.
[0074] In some embodiments, the geological parameter prediction model may be a machine learning model. For example, the input of the geological parameter prediction model may include a second geological parameter point set, and the output may include a second geological information change function.
[0075] In some embodiments, the geological parameter training model can be obtained through training. For example, the parameter training model can be trained based on a large number of training samples with identifiers. The training sample may include a second geological parameter point set of a well logging or an adjacent well. The identifier may be a corresponding second geological information change function. The identifier may be obtained by manual annotation. The second geological parameter point set in the training sample is input into the geological parameter training model; the second geological information change function output by the geological parameter training model is obtained. A loss function is constructed based on the identified second geological parameter point set and the second geological information change function output by the geological parameter training model, and the parameters of the geological parameter training model are synchronously updated. By updating the parameters, a trained geological parameter training model is obtained.
[0076] Through the parameter prediction model described in some embodiments of this specification, intelligent prediction of the second geological information change function can be achieved.
[0077] In some embodiments, determining the second geological information variation function comprises:
[0078] S601, obtaining multiple geological parameter prediction models;
[0079] The geological parameter prediction model may be a model that reflects the change of the geological parameters in the second geological information with the height of the hydraulic fracture. For example, the geological parameter prediction model may include a linear prediction model, a nonlinear prediction model (such as a power law prediction model, an exponential prediction model, a logarithmic prediction model), etc. In some embodiments, the geological parameter prediction model may be obtained through a network, or by calling from a storage device, a database, etc.
[0080] S602, for each of the multiple geological parameter prediction models, determining the degree of fit between the second geological information variation function and the geological parameter prediction model;
[0081] The degree of fit can represent the degree of fit between the second geological information change function and the geological parameter prediction model. For example, the higher the degree of fit between the regression curve of the geological parameter prediction and the observed value of the second geological information change function, the higher the degree of fit can be.
[0082] In some embodiments, the more geological parameters there are in the interpolated second geological parameter point set, the more complete the data of the geological parameter point set is, and the greater the corresponding confidence level may be, wherein the confidence level may be a parameter reflecting the credibility of the second geological information change function.
[0083] S603, determining a target geological parameter prediction model from multiple geological parameter prediction models based on the fit between the second geological information change function and each geological parameter prediction model;
[0084] In some embodiments, the geological parameter prediction model with the highest fitting degree may be selected from a plurality of geological parameter prediction models as the target geological parameter prediction model.
[0085] S604: Determine a second geological information variation function based on the target geological parameter prediction model.
[0086] In some embodiments, the second geological parameter point set may be introduced into the target geological parameter prediction model, and the second geological information variation function may be determined by model fitting.
[0087] S7, based on the second geological information variation function and the shale gas horizontal well hydraulic fracturing network model, determining the average value of the second geological information in the shale gas horizontal well hydraulic fracturing network model;
[0088] In some embodiments, the second geological information average value is an average value of geological parameters in the second geological information in a shale gas horizontal well hydraulic fracturing network model.
[0089] In some embodiments, the second geological information average value can be determined by obtaining the target function segment in which the second geological information variation function falls into the shale gas horizontal well hydraulic fracture network model, and then calculating the average value of the geological parameters in the target function segment. Exemplarily, the maximum longitudinal distance from the wellbore can be used as the endpoint of the interval, and the target function segment in which the second geological information variation function falls into the maximum longitudinal distance from the wellbore can be obtained, and then the average value of the geological parameters in the target function segment can be calculated to determine the second geological information average value. Exemplary calculation methods for the second geological information average value can be calculated by calculating the average value formula of a continuous function over an interval.
[0090] In some embodiments, the second geological information average value includes: an average value of shale mass density, an average value of shale porosity, an average value of gas saturation, and an average value of shale gas volume coefficient.
[0091] S8. Determine the total gas resources of shale gas based on the fracture-controlled transformation volume and the average value of the second geological information;
[0092] In some embodiments, the total shale gas resources are calculated as follows:
[0093] G X =Den*V*C X
[0094] G Y =S g *Φ*V / B g
[0095] G Z =G X +G Y (3)
[0096] (3) In G Z is the total shale gas resources; G X is the amount of shale gas adsorbed gas resources; G Y is the free gas resources of shale gas; Den is the average mass density of shale; Φ is the average porosity of shale; S g is the average value of gas saturation; B g is the average value of shale gas volume coefficient.
[0097] The second aspect of the present invention provides a pre-drilling EUR prediction method system based on a shale gas horizontal well hydraulic fracture network model, which is used to execute the aforementioned pre-drilling EUR prediction method based on a shale gas horizontal well hydraulic fracture network model. It includes:
[0098] Acquisition module 1, acquiring first geological information and second geological information of a target mining area;
[0099] The first geological information includes: the maximum lateral extension length of the hydraulic fracture, the maximum longitudinal distance of the volume element from the wellbore, and the length of the hydraulic fracture section;
[0100] The geological parameters in the second geological information include: shale porosity, shale adsorbed gas content, gas saturation and shale mass density;
[0101] Construction module 2, used to construct a shale gas horizontal well hydraulic fracture network model;
[0102] The first determination module 3 is used to determine the fracture control transformation volume of the target mining area;
[0103] The second determination module 4 is used to determine the average value of the second geological information in the shale gas horizontal well hydraulic fracture network model;
[0104] The third determination module 5 is used to determine the total gas resources of shale gas.
[0105] A third aspect of the present invention provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the aforementioned pre-drilling EUR prediction method based on a shale gas horizontal well hydraulic fracture network model.
[0106] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.
[0107] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.
[0108] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.
Claims
1. A pre-drilling EUR prediction method based on a shale gas horizontal well hydraulic fracture network model, characterized in that: The following steps are involved: Acquire first geological information and second geological information of a target mining area; Based on the first geological information, a shale gas horizontal well hydraulic fracture network model is constructed; Based on the first geological information, determining the fracture-controlled transformation volume of the target mining area; Determine a first geological parameter distribution point set in a target mining area based on the geological parameters and hydraulic fracture height in the second geological information; Determining a second geological parameter point set in the target mining area based on a first geological parameter distribution point set in the target mining area; Determining a second geological information variation function based on a second geological parameter point set in the target mining area; Based on the second geological information variation function and the shale gas horizontal well hydraulic fracture network model, determining the average value of the second geological information in the shale gas horizontal well hydraulic fracture network model; Determine the total shale gas resources based on the fracture-controlled transformation volume and the average value of the second geological information; Determining the second geological information change function includes: Obtain multiple geological parameter prediction models, The geological parameter prediction model is a model that reflects the change of geological parameters in the second geological information with the height of the hydraulic fracture; For each of the plurality of geological parameter prediction models, determining a degree of fit between the second geological information variation function and the geological parameter prediction model; Determining a target geological parameter prediction model from a plurality of geological parameter prediction models based on the fit between the second geological information variation function and each geological parameter prediction model; Based on the target geological parameter prediction model, a second geological information variation function is determined.
2. A pre-drilling EUR prediction method based on a shale gas horizontal well hydraulic fracture network model according to claim 1, characterized in that: The first geological information includes: the maximum lateral extension length of the fracture, the maximum longitudinal distance between the volume element and the wellbore, and the length of the fracture section.
3. A pre-drilling EUR prediction method based on a shale gas horizontal well hydraulic fracture network model according to claim 2, characterized in that: The calculation method of the shale gas horizontal well fracture network model is: （1） In (1), Y is the longitudinal distance between the volume element and the wellbore; M is the maximum transverse extension length of the fracture; N is the maximum longitudinal distance between the volume element and the wellbore; and X is the transverse extension length of the fracture at the height Y position from the wellbore.
4. A pre-drilling EUR prediction method based on a shale gas horizontal well hydraulic fracture network model according to claim 2, characterized in that: The calculation method of the seam control transformation volume is: （2） (2), V is the fracture control stimulation volume, n is the number of fracturing stages, and L is the length of the fracturing stage.
5. The method for predicting EUR before drilling based on a shale gas horizontal well hydraulic fracture network model according to claim 1, characterized in that: The geological parameters in the second geological information include: shale porosity, shale adsorbed gas content, gas saturation and shale mass density.
6. A pre-drilling EUR prediction method based on a shale gas horizontal well hydraulic fracture network model according to claim 5, characterized in that: The second geological parameter point set in the target mining area is obtained by interpolation processing based on the first geological parameter distribution point set in the target mining area; The interpolation processing method is to interpolate through a Gaussian random function to generate random values that conform to the Gaussian distribution.
7. The method for predicting EUR before drilling based on a shale gas horizontal well hydraulic fracture network model according to claim 1, characterized in that: The second geological information average value is the average value of geological parameters in the second geological information in the shale gas horizontal well hydraulic fracture network model; The second geological information average value includes: an average value of shale mass density, an average value of shale porosity, an average value of gas saturation and an average value of shale gas volume coefficient.
8. A pre-drilling EUR prediction method based on a shale gas horizontal well hydraulic fracture network model according to claim 7, characterized in that: The calculation method of the total shale gas resources is: （3） (3) In is the total shale gas resources; is the amount of shale gas adsorbed gas resources; is the free gas resources of shale gas; is the average value of shale mass density; is the average value of shale porosity; is the average value of gas saturation; is the average value of shale gas volume coefficient.
9. A pre-drilling EUR prediction method system based on a shale gas horizontal well hydraulic fracture network model, characterized in that: The method for predicting EUR before drilling based on the shale gas horizontal well hydraulic fracture network model according to any one of claims 1 to 8 comprises: An acquisition module, for acquiring first geological information and second geological information of a target mining area; The first geological information includes: the maximum lateral extension length of the hydraulic fracture, the maximum longitudinal distance of the volume element from the wellbore, and the length of the hydraulic fracture section; The geological parameters in the second geological information include: shale porosity, shale adsorbed gas content, gas saturation and shale mass density; A construction module is used to construct a shale gas horizontal well fracture network model; The first determination module is used to determine the fracture control transformation volume of the target mining area; The second determination module is used to determine the average value of the second geological information in the shale gas horizontal well hydraulic fracture network model. The second geological information average value is determined based on the second geological information variation function and the shale gas horizontal well hydraulic fracture network model. Determining the second geological information change function includes: Obtain multiple geological parameter prediction models, The geological parameter prediction model is a model that reflects the change of geological parameters in the second geological information with the height of the hydraulic fracture; For each of the plurality of geological parameter prediction models, determining a degree of fit between the second geological information variation function and the geological parameter prediction model; Determining a target geological parameter prediction model from a plurality of geological parameter prediction models based on the fit between the second geological information variation function and each geological parameter prediction model; Determining a second geological information variation function based on a target geological parameter prediction model; The third determination module is used to determine the total gas resources of shale gas.
Citation Information
Patent Citations
Shale gas reservoir final recoverable reserve prediction method and system and medium
CN118657259A