CN109190218B - Dynamic identification method for effective seam net of tight reservoir - Google Patents
Dynamic identification method for effective seam net of tight reservoir Download PDFInfo
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- CN109190218B CN109190218B CN201810952019.4A CN201810952019A CN109190218B CN 109190218 B CN109190218 B CN 109190218B CN 201810952019 A CN201810952019 A CN 201810952019A CN 109190218 B CN109190218 B CN 109190218B
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Abstract
The embodiment of the application discloses a dynamic identification method of a tight reservoir effective stitch net, which comprises the following steps: acquiring dynamic inversion parameters from geological engineering parameters; preprocessing the dynamic inversion parameters; selecting a well testing dynamic inversion parameter from the preprocessed dynamic inversion parameter to perform effective network fracture parameter inversion, and obtaining a well testing dynamic inversion result; selecting yield pressure coupling dynamic inversion parameters from the preprocessed dynamic inversion parameters to perform effective network-seaming parameter inversion, and obtaining yield pressure coupling inversion results; and obtaining an effective fracture network parameter identification result according to the well test dynamic inversion result and the yield pressure coupling inversion result.
Description
Technical Field
The application relates to the technical field of oil and gas development, in particular to a dynamic identification method for an effective stitch net of a tight reservoir.
Background
In recent years, with successful exploitation of dense oil and gas in the united states, canada and china, the world is increasingly concerned about exploration and development of dense oil and gas. Because the compact oil gas reserves of China are very abundant, the compact oil gas reserves play an indispensable role in the energy field of China in the future.
Compact hydrocarbon reservoirs have complex physical properties of low porosity and hypotonic nature, and economic yields are difficult to obtain under natural conditions. However, with the great application of large-scale fracturing technology, efficient and economic development of a compact oil and gas reservoir is realized. Many students have found that complex slotted networks are extremely prone to forming around the wellbore during volumetric fracturing using various monitoring techniques.
The existence of the complex fracture network provides new challenges for a dynamic identification method of a tight reservoir volume fracturing well. At present, the microseism monitoring technology is a main method for identifying fracture network parameters, but only qualitatively evaluates the volume fracturing transformation scale and the fracture morphology, and cannot quantitatively evaluate the fracture network parameters (flow conductivity, density, length and fracture shape). However, these parameters are important factors affecting volumetric fracturing well productivity, spatial flow field, pressure field distribution, etc., and require quantitative evaluation. Therefore, a technical problem how to realize quantitative evaluation of the seam parameters is urgently needed to be solved.
Disclosure of Invention
The embodiment of the application aims to provide a dynamic identification method for effective fracture network of a tight reservoir, and the technical scheme quantitatively evaluates parameters (flow conductivity, density, length and fracture occurrence) of the effective fracture network so as to be beneficial to parameter identification, fracturing evaluation and dynamic monitoring research of the tight reservoir.
In order to achieve the above object, an embodiment of the present application provides a dynamic identification method for an effective seam network of a tight reservoir, including:
acquiring dynamic inversion parameters from geological engineering parameters;
preprocessing the dynamic inversion parameters;
selecting a well testing dynamic inversion parameter from the preprocessed dynamic inversion parameter to perform effective network fracture parameter inversion, and obtaining a well testing dynamic inversion result;
selecting yield pressure coupling dynamic inversion parameters from the preprocessed dynamic inversion parameters to perform effective network-seaming parameter inversion, and obtaining yield pressure coupling inversion results;
and obtaining an effective fracture network parameter identification result according to the well test dynamic inversion result and the yield pressure coupling inversion result.
Preferably, the geological engineering parameters include: reservoir parameters, fluid parameters, wellbore parameters, fracturing construction parameters, microseismic monitoring data, well test data, and production dynamics data.
Preferably, the dynamic inversion parameters include: reservoir matrix porosity, formation thickness, formation burial depth, rock compressibility, fluid density, fluid viscosity, fluid saturation, fluid compressibility, wellbore radius, wellbore length.
Preferably, the step of preprocessing the dynamic inversion parameter includes:
and analyzing the dynamic inversion parameters according to the reliability of the data to obtain effective dynamic inversion parameters.
Preferably, the step of obtaining the well test dynamic inversion result comprises:
drawing a theoretical well test curve and an actual well test curve in logarithmic coordinates with the same size, and continuously adjusting theoretical model parameters to enable the theoretical well test curve to be optimally matched with the actual well test curve so as to obtain fitting points;
and obtaining a well testing dynamic inversion result by using the fitting point interrelation. The well test dynamic inversion result comprises: reservoir matrix permeability, extrapolated formation pressure, reservoir boundary size, wellbore reservoir coefficient, skin factor, wellbore effective length, reservoir permeability modulus, fracture network morphology, fracture network conductivity, fracture reconstruction area.
Preferably, the step of obtaining yield pressure coupled inversion results comprises:
drawing a theoretical yield pressure coupling curve and an actual yield pressure coupling curve in a logarithmic coordinate with the same size, and continuously adjusting theoretical model parameters to enable the theoretical yield pressure coupling curve and the actual yield pressure coupling curve to achieve optimal matching so as to obtain fitting points;
and obtaining a yield pressure coupling inversion result by utilizing the fitting point interrelation. The yield pressure coupling dynamic inversion results include: reservoir matrix permeability, extrapolated formation pressure, reservoir boundary size, wellbore reservoir coefficient, skin factor, wellbore effective length, reservoir permeability modulus, fracture network morphology, fracture network conductivity, fracture reconstruction area.
Preferably, the step of obtaining the effective stitch network parameter identification result includes:
and when the relative error between the well testing dynamic inversion result and the yield pressure coupling inversion result is smaller than a threshold value, averaging the well testing dynamic inversion result and the yield pressure coupling inversion result to obtain an effective fracture network parameter identification result. The effective net sewing parameter results comprise: fracture network occurrence, fracture network conductivity, fracture transformation area.
Compared with the prior art, the method for identifying the effective fracture network of the large-scale fracturing well of the tight reservoir is provided, and is beneficial to quantitatively identifying the distribution condition of the effective fracture network of the large-scale fracturing reservoir.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a dynamic identification method for effective stitch-bonding of a tight reservoir according to an embodiment of the present application;
FIG. 2 is a schematic diagram of the pressure curve and pressure derivative curve of the tight well pressure recovery test data of the present embodiment on a dual-logarithmic graph version;
FIG. 3 is a schematic diagram of interpretation of the results based on the fit of FIG. 2;
FIG. 4 is a schematic diagram of the instantaneous and cumulative production curves of the tight well production dynamics data of the present embodiment on a Cartesian plate;
FIG. 5 is a schematic diagram of interpretation of the results based on the fit of FIG. 4;
FIG. 6 is a schematic diagram illustrating the result of the dynamic identification method of the effective stitch-bonding of the tight reservoir according to the present embodiment;
Detailed Description
The technical solutions of the embodiments of the present disclosure will be clearly and fully described below with reference to non-limiting example embodiments shown in the drawings and detailed in the following description, more fully explaining example embodiments of the disclosure and their various features and advantageous details. It should be noted that the features illustrated in the drawings are not necessarily drawn to scale. Descriptions of well-known materials, components, and processing techniques are omitted so as to not obscure the example embodiments of the present disclosure. The examples are presented merely to facilitate an understanding of the practice of the example embodiments of the disclosure and to further enable those of skill in the art to practice the example embodiments. Thus, these examples should not be construed as limiting the scope of the embodiments of the disclosure.
Unless specifically defined otherwise, technical or scientific terms used in this disclosure should be given the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The terms "first," "second," and the like, as used in this disclosure, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. Furthermore, in various embodiments of the present disclosure, the same or similar reference numerals denote the same or similar components.
As shown in fig. 1, a flow chart of a dynamic identification method of an effective seam network of a tight reservoir is provided for an embodiment of the application. Comprising the following steps:
step 101): and acquiring dynamic inversion parameters from the geological engineering parameters.
In this embodiment, the geological engineering parameters are collected and consolidated. The geological engineering parameters comprise reservoir, fluid and shaft parameters, fracturing construction parameters, microseism monitoring data, well testing test data and production dynamic data.
Step 102): and preprocessing the dynamic inversion parameters.
In this embodiment, the dynamic inversion parameters are analyzed according to the reliability of the data, so as to obtain effective dynamic inversion parameters. In this technical solution, the effective dynamic inversion parameters include: reservoir matrix porosity, formation thickness, formation burial depth, rock compressibility, fluid density, fluid viscosity, fluid saturation, fluid compressibility, wellbore radius, wellbore length.
Step 103): and selecting the well testing dynamic inversion parameters from the preprocessed dynamic inversion parameters to perform effective fracture network parameter inversion, and obtaining well testing dynamic inversion results.
In this embodiment, based on microseismic monitoring data, the well test dynamic inversion data is subjected to parameter inversion by using well test curve fitting. The well test curve fitting process is to draw a theoretical well test curve and an actual well test curve in the logarithmic coordinates of the same size, and to enable the theoretical well test curve and the actual well test curve to be in optimal matching by continuously adjusting theoretical model parameters, so as to obtain fitting points. And then parameters such as a shaft, a crack, a stratum and the like are obtained by utilizing the fitting point interrelation. These parameters are collectively referred to as: a well test dynamic inversion result comprising: reservoir matrix permeability, extrapolated formation pressure, reservoir boundary size, wellbore reservoir coefficient, skin factor, wellbore effective length, reservoir permeability modulus, fracture network morphology, fracture network conductivity, fracture reconstruction area.
Step 104): selecting yield pressure coupling dynamic inversion parameters from the preprocessed dynamic inversion parameters to perform effective network-seaming parameter inversion, and obtaining yield pressure coupling inversion results;
in this embodiment, the production dynamics data is parameter inverted using a production pressure coupled curve fit. The fitting process of the yield pressure coupling curve is to draw the theoretical yield pressure coupling curve and the actual yield pressure coupling curve in the logarithmic coordinates with the same size, and the theoretical yield pressure coupling curve and the actual yield pressure coupling curve are optimally matched by continuously adjusting theoretical model parameters, so that fitting points are obtained. And then parameters such as a shaft, a crack, a stratum and the like are obtained by utilizing the fitting point interrelation. These parameters are collectively referred to as: yield pressure coupling inversion results, including: reservoir matrix permeability, extrapolated formation pressure, reservoir boundary size, wellbore reservoir coefficient, skin factor, wellbore effective length, reservoir permeability modulus, fracture network morphology, fracture network conductivity, fracture reconstruction area.
Step 105): and obtaining an effective fracture network parameter identification result according to the well test dynamic inversion result and the yield pressure coupling inversion result. The effective net sewing parameter results comprise: fracture network occurrence, fracture network conductivity, fracture transformation area.
In the embodiment, if the yield pressure coupling inversion result is compared with the well test dynamic inversion result and the relative error of any parameter is smaller than a threshold value, the two dynamic inversion results are considered to be reliable, and the inversion result takes the average value of the two dynamic inversion results; if not, returning to the step 103 of the method, and continuing to perform dynamic inversion on the well test data and the production dynamic data until the relative error is smaller than the threshold value requirement.
In the present embodiment, the threshold value is set to 10% and empirically determined.
The following describes the present technical solution by taking a certain dense oil large-sized fracturing well as an example. The basic parameters of the well are shown in table 1.
TABLE 1
The well is tested in 2014 and 9 months, and the pressure derivative shows the characteristic of 'concave-up' in the middle stage from the test data of the well test, and is similar to the phenomenon of channeling among fracture networks, which shows that a complex fracture network is formed and accords with microseism monitoring data.
For a dynamic identification method of an effective fracture network of a tight reservoir, the primary task is to collect and sort parameters such as geological engineering, including reservoir, fluid and shaft parameters, fracturing construction parameters, microseism monitoring data, well test data and production dynamic data.
After the data are collated, analysis is carried out according to the reliability of the data, and dynamic inversion parameters are determined. Wherein the dynamic inversion parameters include: reservoir porosity, formation thickness, formation burial depth, rock compressibility, fluid density, fluid viscosity, fluid saturation, fluid compressibility, wellbore radius, wellbore length.
As shown in fig. 2, a schematic diagram of a pressure curve and a pressure derivative curve of the tight well pressure recovery test data of the present embodiment on a dual-logarithmic graph version is shown. The well test curve fitting process is to draw a theoretical well test curve and an actual well test curve in the logarithmic coordinates of the same size, to enable the theoretical well test curve to be optimally matched with the actual well test curve by continuously adjusting theoretical model parameters, and to obtain parameters of a shaft, a crack, a stratum and the like by utilizing the correlation of fitting points. As shown in fig. 3, a schematic diagram of the interpretation of the results based on the fitting of fig. 2 is shown.
FIG. 4 is a schematic diagram of the instantaneous and cumulative production curves of the dynamic data of tight well production of this example on a Cartesian plate. The yield pressure coupling curve fitting process is to draw a theoretical yield pressure coupling curve and an actual yield pressure coupling curve in the logarithmic coordinates of the same size, enable the theoretical yield pressure coupling curve and the actual yield pressure coupling curve to achieve optimal matching by continuously adjusting theoretical model parameters, and then obtain parameters such as a shaft, a crack, a stratum and the like by utilizing the correlation of fitting points. As shown in fig. 5, a schematic diagram of the interpretation of the results based on the fitting of fig. 4 is shown.
As shown in fig. 6, a schematic diagram of the result of explaining the dynamic identification method of the effective seam network of the tight reservoir according to the present embodiment is shown. The technical scheme is that the yield pressure coupling inversion result and the well testing dynamic inversion result are used for mutual constraint, and finally the effective network parameter identification result is output. As a result, the error between the two parameters is less than 7%, and the net-sewing flow conductivity is 96.7md.m. Meanwhile, as can be seen from fig. 6, after large fracturing, the reservoir is divided into an original reservoir zone and a fracturing modification zone. The fracture remodelling zone is about 1766m long and about 337.3m wide. Further, the fracture modification area is divided into (1) zone I: horizontal wells and hydraulic fracture formation zones; (2) zone II: the fracture is affected by an effective area, namely a stratum physical property change area affected by the backlog fracture. In the region I, the distribution of cracks is as shown in fig. 6, the main cracks are mainly, the secondary cracks are connected with a plurality of main cracks, and the whole is consistent with microseismic monitoring data.
It will be appreciated by those skilled in the art that embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the present specification embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description embodiments may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present embodiments may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments. In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the embodiments of the present specification. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
The foregoing is merely an example of an embodiment of the present disclosure and is not intended to limit the embodiment of the present disclosure. Various modifications and variations of the illustrative embodiments will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of the embodiments of the present specification, should be included in the scope of the claims of the embodiments of the present specification.
Although the present description has been described by way of embodiments, those of ordinary skill in the art will recognize that there are many variations and modifications to the present description without departing from the spirit of the present description, and it is intended that the appended claims encompass such variations and modifications without departing from the spirit of the present description.
Claims (5)
1. A dynamic identification method for an effective seam net of a tight reservoir, which is characterized by comprising the following steps:
acquiring dynamic inversion parameters from geological engineering parameters; the dynamic inversion parameters include: reservoir matrix porosity, formation thickness, formation burial depth, rock compressibility, fluid density, fluid viscosity, fluid saturation, fluid compressibility, wellbore radius, wellbore length;
preprocessing the dynamic inversion parameters;
selecting a well testing dynamic inversion parameter from the preprocessed dynamic inversion parameter to perform effective network fracture parameter inversion, and obtaining a well testing dynamic inversion result; the well test dynamic inversion result comprises: reservoir matrix permeability, extrapolated formation pressure, reservoir boundary size, wellbore reservoir coefficient, skin factor, wellbore effective length, reservoir permeability modulus, fracture network yield, fracture network conductivity, fracture reconstruction area;
selecting yield pressure coupling dynamic inversion parameters from the preprocessed dynamic inversion parameters to perform effective network-seaming parameter inversion, and obtaining yield pressure coupling inversion results; the yield pressure coupling inversion results include: reservoir matrix permeability, extrapolated formation pressure, reservoir boundary size, wellbore reservoir coefficient, skin factor, wellbore effective length, reservoir permeability modulus, fracture network yield, fracture network conductivity, fracture reconstruction area;
obtaining an effective fracture network parameter identification result according to the well test dynamic inversion result and the yield pressure coupling inversion result;
the step of obtaining the effective seam parameters identification result comprises the following steps:
when the relative error between the well testing dynamic inversion result and the yield pressure coupling inversion result is smaller than a threshold value, averaging the well testing dynamic inversion result and the yield pressure coupling inversion result to obtain an effective fracture network parameter identification result; the effective net sewing parameter results comprise: fracture network occurrence, fracture network conductivity, fracture transformation area, and fracture transformation area, wherein the fracture transformation area comprises a horizontal well, a hydraulic fracture formation area and a stratum physical property change area affected by fracture.
2. The method of claim 1, wherein the geological engineering parameters include: reservoir parameters, fluid parameters, wellbore parameters, fracturing construction parameters, microseismic monitoring data, well test data, and production dynamics data.
3. The method of claim 1, wherein the step of preprocessing the dynamic inversion parameters comprises:
and analyzing the dynamic inversion parameters according to the data availability to obtain effective dynamic inversion parameters.
4. The method of claim 1, wherein the step of obtaining the well test dynamic inversion result comprises:
drawing a theoretical well test curve and an actual well test curve in logarithmic coordinates with the same size, and continuously adjusting theoretical model parameters to enable the theoretical well test curve to be optimally matched with the actual well test curve so as to obtain fitting points;
and obtaining a well testing dynamic inversion result by using the fitting point interrelation.
5. The method of claim 1, wherein the step of obtaining yield pressure coupled inversion results comprises:
drawing a theoretical yield pressure coupling curve and an actual yield pressure coupling curve in a logarithmic coordinate with the same size, and continuously adjusting theoretical model parameters to enable the theoretical yield pressure coupling curve and the actual yield pressure coupling curve to achieve optimal matching so as to obtain fitting points;
and obtaining a yield pressure coupling inversion result by utilizing the fitting point interrelation.
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| CN109658016B (en) * | 2019-02-12 | 2022-11-22 | 中国石油大学(北京) | Identification method and system for tight gas reservoir supply boundary |
| CN112780253B (en) * | 2020-01-20 | 2022-05-10 | 中国石油天然气集团有限公司 | Method for predicting and evaluating fractured reservoir |
| CN116629165B (en) * | 2023-07-24 | 2023-09-22 | 中国石油大学(华东) | Reservoir fracturing reconstruction area and non-reconstruction area parameter inversion method, system and equipment |
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