CN117077572B - Quantitative characterization method for multi-cluster crack expansion uniformity degree of shale oil reservoir - Google Patents
Quantitative characterization method for multi-cluster crack expansion uniformity degree of shale oil reservoir Download PDFInfo
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Abstract
The application discloses a quantitative characterization method of shale oil reservoir multi-cluster crack extension uniformity, relates to the field of petroleum and natural gas development, and can improve the accuracy of the shale oil reservoir full well Duan Duo crack extension uniformity to a certain extent. The method comprises the following steps: acquiring influence parameters of the micro-seismic monitoring seam network wave and volume of a plurality of fracturing sections of the horizontal well, and establishing an influence parameter set; acquiring key control factors influencing the wave and volume of the microseism monitoring seam network of each fracturing section of the horizontal well through a gray correlation analysis method; acquiring microseism monitoring seam network wave and volume of each fracturing section of a plurality of horizontal wells of an area to be evaluated and influence parameters corresponding to key control factors, establishing a seam network wave and volume prediction model, acquiring seam network wave and volume prediction values, acquiring a multi-crack expansion uniformity index through the seam network wave and volume prediction values and the microseism monitoring seam network wave and volume, and outputting and taking the multi-crack expansion uniformity degree of the area to be evaluated.
Description
Technical Field
The application relates to the field of petroleum and natural gas development, in particular to a quantitative characterization method for the uniformity of multi-cluster crack expansion of a shale oil reservoir.
Background
Shale oil and gas resources occupy very abundant storage space in China, oil and gas exploitation is a common shale oil exploitation and utilization means by means of unconventional volume fracturing of shale oil reservoirs, and the method is specifically implemented by establishing a complex fracture network in the reservoirs by means of horizontal well staged multi-cluster volume fracturing; wherein the balanced expansion of multiple clusters of cracks is a precondition for ensuring the formation of a complex stitch net. The land shale oil reservoir in China has strong heterogeneity, longitudinal thin interbedded composite lithology superposition development and obvious rock mineral composition difference, meanwhile, the shale oil reservoir usually develops natural cracks, the occurrence and distribution of the natural cracks lack of effective means for characterization, so that the multi-crack competition and expansion rule is extremely complex, and the evaluation of the post-compaction transformation effect brings great challenges.
The methods of evaluating the uniformity of multi-crack propagation disclosed in the prior art include the following three. Firstly, respectively establishing a fluid flow equation and a crack constitutive equation by collecting geological and engineering parameters, researching a multi-crack expansion rule, calculating a multi-cluster crack flow distribution rule, and further calculating a uniformity index to quantitatively represent the multi-crack expansion uniformity degree; secondly, a three-dimensional ground stress model is established by acquiring reservoir parameters of tight sandstone, regional earthquake and logging data, sand fracturing construction data and field detection data, artificial crack expansion is simulated in the fracturing process, an artificial crack form is embedded into the three-dimensional geological model, and the efficiency evaluation of reservoir utilization of the artificial crack after fracturing is carried out by comparing and fitting the productivity prediction result after fracturing with tracer interpretation and post-fracturing test results, so that the multi-crack expansion equilibrium degree is evaluated; thirdly, monitoring the abrasion degree of the holes after the volume fracturing of the horizontal well by means of perforation imaging, and further analyzing the crack initiation uniformity degree of each cluster of cracks and the entering condition of propping agents.
The first method and the second method are used for researching a multi-crack expansion rule by adopting numerical simulation so as to analyze the crack expansion equilibrium degree; the third method uses perforation imaging to monitor the degree of perforation abrasion after horizontal well volume fracturing. The first and second methods need to assume various ideal conditions in the implementation process, and the conditions of the first and second methods are greatly different from those of shale oil reservoirs with strong non-uniformity in the actual production process; the third method depends on the monitoring precision of the camera and the cleaning degree of the shaft, and a plurality of interpretations exist in the obtained result; in summary, the evaluation method disclosed in the prior art is difficult to reflect the situation of uniform multi-crack expansion of a real mine.
Disclosure of Invention
According to the quantitative characterization method for the multi-cluster crack extension uniformity degree of the shale oil reservoir, the accuracy of the crack extension uniformity degree of the Duan Duo shale oil reservoir can be improved to a certain extent.
The embodiment of the application provides a quantitative characterization method for the multi-cluster fracture expansion uniformity degree of a shale oil reservoir, which comprises the following steps:
acquiring influence parameters of the micro-seismic monitoring seam network wave and volume of a plurality of fracturing sections of the horizontal well, and establishing an influence parameter set; the influence parameters comprise geomechanical parameters and fracturing transformation parameters corresponding to a plurality of fracturing stages of the horizontal well; the geomechanical parameters comprise the porosity, permeability, oil saturation, oil layer thickness, brittleness index and horizontal stress difference of each fracturing section of the horizontal well; the fracturing modification parameters include: the number of crack clusters, construction displacement, ground entering liquid and sand adding amount of each fracturing section of the horizontal well;
acquiring key control factors influencing the wave and volume of the microseism monitoring seam network of each fracturing section of the horizontal well through a gray correlation analysis method;
acquiring microseism monitoring seam network wave and volume of each fracturing section of a plurality of horizontal wells of an area to be evaluated and influence parameters corresponding to key control factors, establishing a seam network wave and volume prediction model, acquiring seam network wave and volume prediction values, acquiring a multi-crack expansion uniformity index through the seam network wave and volume prediction values and the microseism monitoring seam network wave and volume, and outputting and taking the multi-crack expansion uniformity degree of the area to be evaluated.
In one possible implementation manner, the acquiring key control factors affecting the coverage volume of the microseism monitoring seam network of each fracturing segment of the horizontal well through the gray correlation analysis method includes:
acquiring the micro-seismic monitoring joint network sweep volumes of each fracturing section of a horizontal well single well, establishing a multi-factor comprehensive evaluation reference column, and establishing a multi-factor comprehensive evaluation matrix according to an influence parameter set;
respectively carrying out standardized treatment on a multi-factor comprehensive evaluation reference column and a multi-factor comprehensive evaluation matrix;
calculating correlation factors between various influence parameters and the horizontal well volume fracturing microseism monitoring seam network wave and volume, screening the correlation factors larger than preset conditions, and outputting the influence parameters corresponding to the correlation factors as key control factors.
In one possible implementation manner, the establishing the multi-factor comprehensive evaluation reference column includes:
establishing a multi-factor comprehensive evaluation reference column according to the wave and volume of each fracturing segment microseism monitoring seam net of the horizontal well, wherein the multi-factor comprehensive evaluation reference column is expressed as the following formula:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein,for the multi-factor comprehensive evaluation reference column,multi-element comprehensive evaluation reference column elements;the number of the volume fracturing stages of the horizontal well;is the volume pressure of a horizontal wellNumber of split segments.
In one possible implementation manner, the establishing the multi-factor comprehensive evaluation matrix according to the influence parameter set includes the following expression:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein,is a multi-element comprehensive evaluation matrix;for the multi-element comprehensive evaluation matrix element,；the number of the volume fracturing stages of the horizontal well;the number of the factors influencing the net wave and the volume is sewn.
In one possible implementation manner, the separately normalized multi-factor comprehensive evaluation reference column and the multi-factor comprehensive evaluation matrix are expressed as the following formulas:
；
；
wherein,standardized elements for the multi-element comprehensive evaluation matrix;is an element of a multi-element comprehensive evaluation matrix;the maximum value of the matrix elements is comprehensively evaluated for multiple elements;standardized elements are listed for multi-element comprehensive evaluation reference;comprehensively evaluating reference column elements for multiple elements;the maximum value of the reference column element is evaluated for multiple elements.
In one possible implementation, the calculating the correlation factor between the plurality of influencing parameters and the horizontal well volume fracturing microseismic monitoring network sweep volume includes:
obtaining standard deviation by calculating the difference of the standardized elements of the multi-element comprehensive evaluation matrix standardized elements and the standardized elements of the multi-element comprehensive evaluation reference column;
and calculating correlation factors between various influencing parameters and the horizontal well volume fracturing microseism monitoring fracture network sweep volumes through the acquired standard deviation.
In one possible implementation manner, the standard deviation is obtained by taking a difference between the multi-element comprehensive evaluation matrix standardized element and the multi-element comprehensive evaluation reference column standardized element, and is expressed as the following formula:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein,to evaluate a standard deviation between the matrix normalized data and the reference column normalized data;standardized elements for the multi-element comprehensive evaluation matrix;the reference column normalizes the elements for multi-element comprehensive evaluation.
In one possible implementation manner, the correlation factor between the plurality of influencing parameters and the horizontal well volume fracturing microseism monitoring seam network sweep volume is calculated through the acquired standard deviation, and is expressed as the following formula:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein,is a correlation factor;to evaluate a standard deviation between the matrix normalized data and the reference column normalized data;for the resolution factor, it is configured to reduce the probability that the maximum absolute error value is too large to be distorted.
In one possible implementation, the acquiring the seam network sweep volume prediction value is expressed as the following formula:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein,is the predicted value of the seam net wave and the volume, and the unit is；Numbering fracturing sections of the shale oil horizontal well;the unit is that of the liquid entering the ground；The number of crack clusters is the number of the crack clusters, and the unit is a cluster;for construction displacement, the unit is；The sand adding amount is t;is a brittleness index in units of;the unit is MPa, which is the two-way stress difference of the horizontal well;the unit is the clay content;is permeability in mD;porosity in units of;oil saturation in units of; a. b, c, d, e, f, g, h, o, p, r are constant terms.
In one possible implementation manner, the multi-crack propagation uniformity index is obtained by monitoring the predicted value of the crack propagation volume and the microseismic monitoring of the crack propagation volume, and is expressed as the following formula:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein EF is a multi-crack expansion uniformity index and is dimensionless;numbering fracturing sections of the shale oil horizontal well;the number of fracturing sections of the shale oil horizontal well;the unit is that the micro-seismic monitoring seam net wave and volume；Is the predicted value of the seam net wave and the volume, and the unit is。
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
according to the embodiment of the application, the key control factors influencing the volume of the horizontal well, the fracturing microseism monitoring slotted network wave and the volume are obtained, the multi-crack expansion uniformity index is calculated through the influence parameter calculation corresponding to the key control factors, and the multi-crack expansion uniformity index is combined with the key geomechanical parameter and the fracturing transformation parameter, so that the defect that the evaluation method in the prior art is difficult to reflect the multi-crack expansion uniformity condition of a real mine is overcome; compared with the method for evaluating the crack expansion uniformity degree through means such as numerical simulation and underground television monitoring disclosed in the prior art, the method provided by the application greatly simplifies the calculation process and steps, can save the test cost, is also suitable for evaluating the expansion uniformity degree of the crack of the similar unconventional reservoir volume fracturing parameters, and can improve the accuracy for representing the crack expansion uniformity degree of the shale oil reservoir full well Duan Duo.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings that are needed in the description of the embodiments of the present application will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a flow chart of a quantitative characterization method for multi-cluster fracture propagation uniformity of a shale oil reservoir provided by an embodiment of the application;
FIG. 2 is a flowchart of a quantitative characterization method provided in an embodiment of the present application for obtaining key control factors affecting the network sweep volume of microseism monitoring at each fracturing segment of a horizontal well by a gray correlation analysis method;
FIG. 3 is a correlation factor ranking chart of shale oil horizontal well fracture network sweep volume influence parameters provided in an embodiment of the present application;
fig. 4 is a graph comparing predicted values of fracture network sweep and volume of each fracturing segment of a shale oil horizontal well according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
In the description of the embodiments of the present application, it should be noted that, directions or positional relationships indicated by terms such as "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., are based on directions or positional relationships shown in the drawings, are merely for convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific direction, be configured and operated in the specific direction, and thus should not be construed as limiting the present application. The terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, the terms "mounted," "connected," "coupled," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the embodiments of the present application will be understood by those of ordinary skill in the art according to the specific circumstances.
Referring to fig. 1, fig. 1 is a flowchart of a quantitative characterization method for multi-cluster fracture expansion uniformity of a shale oil reservoir according to an embodiment of the present application.
The embodiment of the application provides a quantitative characterization method for the multi-cluster fracture expansion uniformity degree of a shale oil reservoir, which comprises the following steps: step 101, step 102 and step 103.
Step 101, acquiring influence parameters of the micro-seismic monitoring seam network wave and volume of a plurality of fracturing sections of a horizontal well, and establishing an influence parameter set; the influence parameters comprise geomechanical parameters and fracturing transformation parameters corresponding to a plurality of fracturing stages of the horizontal well; the geomechanical parameters comprise the porosity, permeability, oil saturation, oil layer thickness, brittleness index and horizontal stress difference of each fracturing section of the horizontal well; the fracturing modification parameters include: the number of crack clusters, construction displacement, the ground entering liquid amount and the sand adding amount of each fracturing section of the horizontal well.
Step 101 is specifically implemented as follows: taking the horizontal well H1 as a test object, collecting geomechanical parameters, fracturing transformation parameters and microseism monitoring fracture network sweep volumes of a plurality of fracturing stages of a single well of the horizontal well H1, and forming data tables shown in tables 1 and 2.
Table 1 geomechanical parameter table for each fracture segment of horizontal well H1
Table 2 table of fracture volume for each fracture zone of horizontal well H1 fracture modification parameters and microseism monitoring fracture volume table
And 102, acquiring key control factors influencing the wave and volume of the microseism monitoring seam network of each fracturing section of the horizontal well by a gray correlation analysis method.
Step 103, acquiring the micro-seismic monitoring seam network wave and volume of each fracturing section of the plurality of horizontal wells of the region to be evaluated and the influence parameters of corresponding key control factors, establishing a seam network wave and volume prediction model, acquiring seam network wave and volume prediction values, acquiring a multi-crack expansion uniformity index through the seam network wave and volume prediction values and the micro-seismic monitoring seam network wave and volume, and outputting and taking the multi-crack expansion uniformity degree of the region to be evaluated.
The multi-crack expansion uniformity degree of the output and taking region to be evaluated is specifically implemented as follows: the full well Duan Duo fracture propagation uniformity index is defined and if its value is greater, it indicates a higher degree of uniformity. When the uniformity index is 0-0.25, the multi-crack expansion uniformity degree is poor; when the uniformity index is between 0.25 and 0.50, the multi-crack expansion uniformity degree is moderate; when the uniformity index is between 0.50 and 0.75, the multi-crack expansion uniformity degree is good; when the uniformity index is 0.75-1.0, the multi-crack expansion uniformity degree is very good.
The method comprises the steps of obtaining key control factors influencing the volume of a horizontal well fracturing microseism monitoring fracture network and the swept volume, calculating influence and calculation multi-crack expansion uniformity indexes through influence parameters corresponding to the key control factors, and combining key geomechanical parameters and fracturing transformation parameters through the multi-crack expansion uniformity indexes, so that the defect that the evaluation method in the prior art is difficult to reflect the multi-crack expansion uniformity condition of a real mine is overcome; compared with the method for evaluating the crack expansion uniformity degree through means such as numerical simulation and underground television monitoring disclosed in the prior art, the method provided by the application greatly simplifies the calculation process and steps, can save the test cost, is also suitable for evaluating the expansion uniformity degree of the crack of the similar unconventional reservoir volume fracturing parameters, and can improve the accuracy for representing the crack expansion uniformity degree of the shale oil reservoir full well Duan Duo.
Referring to fig. 2, fig. 2 is a flowchart of a quantitative characterization method provided in an embodiment of the present application, where key control factors affecting the network sweep volume of microseism monitoring at each fracturing segment of a horizontal well are obtained by a gray correlation analysis method.
In some embodiments, when key control factors affecting the microseism monitoring network sweep volume of each fracturing segment of a horizontal well are obtained through a gray correlation analysis method, the method provided by the embodiment of the application comprises the following steps: step 201, step 202 and step 203.
Step 201, acquiring the micro-seismic monitoring seam network wave and volume of each fracturing section of a horizontal well single well, establishing a multi-factor comprehensive evaluation reference column, and establishing a multi-factor comprehensive evaluation matrix according to an influence parameter set.
Step 202, respectively carrying out standardization processing on the multi-factor comprehensive evaluation reference columns and the multi-factor comprehensive evaluation matrix.
And 203, calculating correlation factors between various influence parameters and the horizontal well volume fracturing microseism monitoring seam network wave and volume, screening the correlation factors larger than a preset condition, and outputting the influence parameters corresponding to the correlation factors as key control factors.
The correlation factors are conveniently obtained through the multi-factor comprehensive evaluation reference column and the multi-factor comprehensive evaluation matrix, the obtained reference column and the obtained evaluation matrix are standardized, the probability that the numerical value difference of extremum in the process of calculating the correlation factors is greatly reduced to reduce the precision of a subsequent calculation result is reduced, and the correlation factors which are larger than a preset condition are screened, so that the influence parameters corresponding to the screened correlation factors are obtained as the key control factors for output, the aim of simplifying the data quantity of analysis of the micro-seismic monitoring crack volume corresponding to the key control factors in the later period can be achieved, and the running speed of the whole calculation process is improved.
Referring to fig. 3, fig. 3 is a correlation factor ranking chart of shale oil horizontal well fracture network sweep volume influence parameters according to an embodiment of the present application.
In some embodiments, in establishing a multi-factor comprehensive evaluation reference column, the quantitative characterization method provided herein includes: establishing a multi-factor comprehensive evaluation reference column according to the wave and volume of each fracturing segment microseism monitoring seam network of the horizontal well, wherein the multi-factor comprehensive evaluation reference column is expressed as the following formula:
(1) Wherein the method comprises the steps of，For the multi-factor comprehensive evaluation reference column,multi-element comprehensive evaluation reference column elements;the number of the volume fracturing stages of the horizontal well;the number of stages is the volume fracturing of the horizontal well.
In some embodiments, a multi-factor comprehensive evaluation matrix is established according to the set of influencing parameters, expressed as the following formula:
(2) Wherein,is a multi-element comprehensive evaluation matrix;for the multi-element comprehensive evaluation matrix element,；the number of the volume fracturing stages of the horizontal well;the number of the factors influencing the net wave and the volume is sewn.
In some embodiments, the multi-factor comprehensive evaluation reference column and the multi-factor comprehensive evaluation matrix are normalized separately, expressed as the following formulas:
（3）
（4）
wherein,standardized elements for the multi-element comprehensive evaluation matrix;is an element of a multi-element comprehensive evaluation matrix;the maximum value of the matrix elements is comprehensively evaluated for multiple elements;standardized elements are listed for multi-element comprehensive evaluation reference;comprehensively evaluating reference column elements for multiple elements;the maximum value of the reference column element is evaluated for multiple elements.
In some embodiments, in calculating correlation factors between various influencing parameters and horizontal well volume fracturing microseism monitoring fracture network sweep volumes, the quantitative characterization method provided by the application comprises the following steps: obtaining standard deviation by calculating the difference of the standardized elements of the multi-element comprehensive evaluation matrix standardized elements and the standardized elements of the multi-element comprehensive evaluation reference column; and calculating correlation factors between various influencing parameters and the horizontal well volume fracturing microseism monitoring fracture network sweep volumes through the acquired standard deviation.
In some embodiments, the standard deviation is obtained by subtracting the multi-element comprehensive evaluation matrix normalization element from the multi-element comprehensive evaluation reference column normalization element expressed as the following formula:
(5) Wherein,to evaluate a standard deviation between the matrix normalized data and the reference column normalized data;standardized elements for the multi-element comprehensive evaluation matrix;the reference column normalizes the elements for multi-element comprehensive evaluation.
In one possible implementation manner, the correlation factor between the multiple influencing parameters calculated by the acquired standard deviation and the horizontal well volume fracturing microseismic monitoring seam network sweep volume is expressed as the following formula:(6) Wherein,is a correlation factor;to evaluate a standard deviation between the matrix normalized data and the reference column normalized data;for the resolution factor, it is configured to reduce the probability that the maximum absolute error value is too large to be distorted.
The specific implementation is as follows: establishing a multi-factor evaluation reference column through a formula (1), establishing a multi-factor comprehensive evaluation matrix through a formula (2), normalizing the multi-factor comprehensive evaluation matrix and the multi-factor evaluation reference column through a formula (3) and a formula (4), calculating correlation factors between different influence parameters and the multi-factor evaluation reference column by using a formula (5) and a formula (6), and obtaining a calculation result shown in a table 3.
TABLE 3 correlation factor calculation Table between different influencing factors and microseism monitoring seam network sweep volumes
The correlation factors obtained in table 3 are screened and sequenced, and the key control factors for influencing the horizontal well volume fracture network wave and the volume are sequentially ground entering liquid amount, the number of fracture clusters, the brittleness index, the horizontal stress difference, the construction displacement, the sand adding amount and the clay content by setting the preset condition to be more than 0.5, namely setting the influence parameters corresponding to the correlation factors with definition values more than 0.5.
The correlation factors are calculated, and key control factors influencing the volume fracture network wave and the volume of the horizontal well are obtained, so that calculation according to the key control factors in the subsequent calculation process is facilitated, the complexity of the whole calculation process is reduced, and the subsequent calculation efficiency can be improved to a certain extent.
In some embodiments, a multiple regression method is used to obtain a seam network wave and volume prediction model coupled with key control factors of geological engineering, and a seam network wave and volume prediction value is obtained, expressed as the following formula:
(7) Wherein,is the predicted value of the seam net wave and the volume, and the unit is；Numbering fracturing sections of the shale oil horizontal well;the unit is that of the liquid entering the ground；The number of crack clusters is the number of the crack clusters, and the unit is a cluster;for construction displacement, the unit is；The sand adding amount is t;is a brittleness index in units of;the unit is MPa, which is the two-way stress difference of the horizontal well;the unit is the clay content;is permeability in mD;porosity in units of;oil saturation in units of; a. b, c, d, e, f, g, h, o, p, r are constant terms.
Referring to fig. 4, fig. 4 is a graph comparing predicted values of fracture network sweep volumes of each fracturing section of a shale oil horizontal well according to an embodiment of the present application.
In some embodiments, a multi-fracture propagation uniformity index is obtained by monitoring the fracture network sweep volume and microseismic monitoring the fracture network sweep volume, expressed as the following formula:
(8) Wherein EF is a multi-crack expansion uniformity index and is dimensionless;numbering fracturing sections of the shale oil horizontal well;the number of fracturing sections of the shale oil horizontal well;the unit is that the micro-seismic monitoring seam net wave and volume；Is the predicted value of the seam net wave and the volume, and the unit is。
The specific implementation is as follows: collecting the monitoring seam network sweep volume of a target block horizontal well 31-mouth 365 section microseism, and the monitoring big data of the seam network sweep volume geomechanics and fracturing transformation main control factor parameter mining fields; on the basis of microseism monitoring seam net wave and volume mine monitoring big data collection, a multiple regression method is adopted to obtain a seam net wave and volume prediction model of coupling geological engineering main control factors, and the expression is as follows:
；
microseism monitoring joint network sweep volume SRVi, key geomechanical parameters and fracturing transformation parameters of each fracturing segment of horizontal well H1, and calculating predicted joint network sweep volumes of each fracturing segment by using a prediction modelAnd the multi-crack expansion uniformity index is 0.80 obtained by using the formula (8), and the multi-crack expansion uniformity degree of the reservoir where the horizontal well H1 is positioned is very good according to the grading output result.
According to the method, key control factors influencing the volume of the horizontal well, the fracturing microseism monitoring network wave and the volume of the horizontal well are obtained, the multi-crack expansion uniformity index is calculated through influence parameter calculation corresponding to the key control factors, and the multi-crack expansion uniformity index is combined with key geomechanical parameters and fracturing transformation parameters, so that the defect that the evaluation method in the prior art is difficult to reflect the multi-crack expansion uniformity condition of a real mine is overcome; compared with the method for evaluating the crack expansion uniformity degree through means such as numerical simulation and underground television monitoring disclosed in the prior art, the method provided by the application greatly simplifies the calculation process and steps, can save the test cost, is also suitable for evaluating the expansion uniformity degree of the crack of the similar unconventional reservoir volume fracturing parameters, and can improve the accuracy for representing the crack expansion uniformity degree of the shale oil reservoir full well Duan Duo.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment is mainly described as a difference from other embodiments.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the present application; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions.
Claims (8)
1. A method for quantitatively characterizing the uniformity of multi-cluster fracture propagation of a shale oil reservoir, the method comprising:
acquiring influence parameters of the micro-seismic monitoring seam network wave and volume of a plurality of fracturing sections of the horizontal well, and establishing an influence parameter set; the influence parameters comprise geomechanical parameters and fracturing transformation parameters corresponding to a plurality of fracturing stages of the horizontal well; the geomechanical parameters comprise the porosity, permeability, oil saturation, oil layer thickness, brittleness index and horizontal stress difference of each fracturing section of the horizontal well; the fracturing modification parameters include: the number of crack clusters, construction displacement, ground entering liquid and sand adding amount of each fracturing section of the horizontal well;
acquiring key control factors influencing the wave and volume of the microseism monitoring seam network of each fracturing section of the horizontal well through a gray correlation analysis method;
acquiring microseism monitoring seam network wave and volume of each fracturing section of a plurality of horizontal wells of an area to be evaluated and influence parameters corresponding to key control factors, establishing a seam network wave and volume prediction model, acquiring seam network wave and volume prediction values, acquiring a multi-crack expansion uniformity index through the seam network wave and volume prediction values and the microseism monitoring seam network wave and volume, and outputting and taking the multi-crack expansion uniformity degree of the area to be evaluated;
the obtained seam net wave and volume prediction value is expressed as the following formula:
wherein ESRV l For the seam net wave and volume prediction value, the unit is 10 4 m 3 The method comprises the steps of carrying out a first treatment on the surface of the l is the number of the fracturing section of the shale oil horizontal well; v (V) L For the amount of liquid introduced into the ground, the unit is m 3 The method comprises the steps of carrying out a first treatment on the surface of the n is the number of crack clusters, and the unit is a cluster; q is construction displacement, the unit is m 3 /min；V P The sand adding amount is t; BI is the brittleness index in units of; Δσ is the two-way stress difference of the horizontal well, and the unit is MPa; s is S H The unit is the clay content; k is permeability, and the unit is mD;porosity in units of; s is S o Oil saturation in units of; a. b, c, d, e, f, g, h, o, p, r are constant terms;
the multi-crack expansion uniformity index is obtained by monitoring the fracture network sweep volume predicted value and the microseism, and is expressed as the following formula:
wherein EF is a plurality ofCrack propagation uniformity index, dimensionless; l is the number of the fracturing section of the shale oil horizontal well; k is the number of fracturing sections of the shale oil horizontal well; SRV (SRV) l For monitoring the wave and volume of the seam net for microseism, the unit is 10 4 m 3 ；ESRV l For the seam net wave and volume prediction value, the unit is 10 4 m 3 。
2. The quantitative characterization method for the multi-cluster fracture propagation uniformity of the shale oil reservoir according to claim 1, wherein the obtaining key control factors affecting the network sweep volume of the microseism monitoring fracture of each fracturing segment of the horizontal well by a gray correlation analysis method comprises the following steps:
acquiring the micro-seismic monitoring joint network sweep volumes of each fracturing section of a horizontal well single well, establishing a multi-factor comprehensive evaluation reference column, and establishing a multi-factor comprehensive evaluation matrix according to an influence parameter set;
respectively carrying out standardized treatment on a multi-factor comprehensive evaluation reference column and a multi-factor comprehensive evaluation matrix;
calculating correlation factors between various influence parameters and the horizontal well volume fracturing microseism monitoring seam network wave and volume, screening the correlation factors larger than preset conditions, and outputting the influence parameters corresponding to the correlation factors as key control factors.
3. The method for quantitatively characterizing the extent of multi-cluster fracture propagation uniformity of a shale oil reservoir according to claim 2, wherein the establishing a multi-factor comprehensive evaluation reference column comprises:
establishing a multi-factor comprehensive evaluation reference column according to the wave and volume of each fracturing segment microseism monitoring seam net of the horizontal well, wherein the multi-factor comprehensive evaluation reference column is expressed as the following formula:
X 0 ＝(X 1 (0)，…，X i (0)，…，X m (0)) T i＝1，2，…，m；
wherein X is 0 For multi-factor comprehensive evaluation reference column, X i (0) Multi-element comprehensive evaluation reference column elements; t is the number of horizontal well volume fracturing stages; and m is the number of horizontal well volume fracturing stages.
4. The quantitative characterization method of the multi-cluster fracture propagation uniformity degree of the shale oil reservoir according to claim 3, wherein the multi-factor comprehensive evaluation matrix is established according to an influence parameter set and expressed as the following formula:
wherein X is a multi-element comprehensive evaluation matrix; x is X i (j) I=1, 2, …, m for multi-element comprehensive evaluation matrix elements; j=1, 2, …, n; m is the number of horizontal well volume fracturing stages; n is the number of the seam net wave and volume influencing factors.
5. The quantitative characterization method for the multi-cluster crack propagation uniformity degree of the shale oil reservoir according to claim 4, wherein the multi-factor comprehensive evaluation reference column and the multi-factor comprehensive evaluation matrix are respectively standardized and expressed as the following formulas:
wherein,standardized elements for the multi-element comprehensive evaluation matrix; x is X i (j) Is an element of a multi-element comprehensive evaluation matrix; (X) i (j)) max The maximum value of the matrix elements is comprehensively evaluated for multiple elements; />Standardized elements are listed for multi-element comprehensive evaluation reference; x is X i (0) Comprehensively evaluating reference column elements for multiple elements;(X i (0)) max the maximum value of the reference column element is evaluated for multiple elements.
6. The method for quantitatively characterizing the extent of multi-cluster fracture propagation uniformity of a shale oil reservoir according to claim 5, wherein calculating the correlation factor between the plurality of influencing parameters and the horizontal well volume fracturing microseism monitoring fracture network sweep volume comprises:
obtaining standard deviation by calculating the difference of the standardized elements of the multi-element comprehensive evaluation matrix standardized elements and the standardized elements of the multi-element comprehensive evaluation reference column;
and calculating correlation factors between various influencing parameters and the horizontal well volume fracturing microseism monitoring fracture network sweep volumes through the acquired standard deviation.
7. The quantitative characterization method of the multi-cluster crack propagation uniformity degree of the shale oil reservoir according to claim 6, wherein the standard deviation is obtained by taking a difference between a multi-element comprehensive evaluation matrix standardized element and a multi-element comprehensive evaluation reference column standardized element, and is expressed as the following formula:
wherein delta is i (j) To evaluate a standard deviation between the matrix normalized data and the reference column normalized data;standardized elements for the multi-element comprehensive evaluation matrix; />The reference column normalizes the elements for multi-element comprehensive evaluation.
8. The quantitative characterization method of the multi-cluster fracture propagation uniformity degree of the shale oil reservoir according to claim 7, wherein the correlation factor between a plurality of influencing parameters and the horizontal well volume fracturing microseism monitoring fracture network sweep volume is calculated through the acquired standard deviation and expressed as the following formula:
wherein, xi j Is a correlation factor; delta i (j) To evaluate a standard deviation between the matrix normalized data and the reference column normalized data; ρ is a resolution factor configured to reduce the probability of distortion if the maximum absolute error value is too large.
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