CN116128084A - Prediction method for volume fracture network control reserves of tight oil reservoir horizontal well - Google Patents
Prediction method for volume fracture network control reserves of tight oil reservoir horizontal well Download PDFInfo
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Abstract
The invention provides a prediction method of a tight oil reservoir horizontal well volume fracture network control reserve, which comprises the steps of firstly, establishing a fracture network control volume prediction model coupled with different influencing factors according to prediction horizontal well basic big data, and predicting the horizontal well whole-well section microseism monitoring fracture network control volume; secondly, establishing a geological model according to physical parameters of a reservoir where the horizontal well is located, and importing the control volume of the whole-well section microseism monitoring stitch net into the geological model to obtain a productivity prediction model; and finally, calculating the effective fracture network control volume by utilizing a productivity prediction model according to the production dynamic parameters of the horizontal well, further quantitatively calculating the fracture network control reserve according to the physical parameters of the reservoir, defining a fracture network control reserve index of the horizontal well, quantitatively evaluating the volume fracturing development effect of the horizontal well, solving the problem of low numerical simulation prediction precision of the oil reservoir, having the advantages of simplicity in calculation, strong operability and the like, having wide application prospect and having important guiding function on fracturing optimization design.
Description
Technical Field
The invention relates to the field of petroleum and natural gas development, in particular to a prediction method for controlling reserves of a tight oil reservoir horizontal well volume fracture network in the field of hydraulic fracturing.
Background
With the continuous development of basin tight oil reservoirs, the volume fracturing technology and reservoir matching face a plurality of challenges, for example, large data of the production profile test of each fracturing segment of a horizontal well of a mining site show that the productivity contribution difference of each fracturing segment is large. Whether the volume fracturing parameters of the horizontal well are optimally matched with the geomechanical parameters of the reservoir is difficult to evaluate at present. The control reserve of the slotted net (namely the slotted control reserve) after the horizontal well is fractured is a direct important index for directly evaluating whether the volume fracturing parameters are matched with the reservoir, and the larger the value is, the better the fracturing transformation effect is, so the slotted control reserve is important for improving the volume fracturing technology of the horizontal well. However, because of strong heterogeneity of reservoir physical properties, large differences in fracturing construction parameters and different effects of each parameter on fracturing effects on different layers, the determination of the control reserves of the fracture network is very difficult.
At present, a micro-seismic monitoring technology and an oil reservoir numerical simulation method are mainly adopted to determine the fracture network control volume, wherein the micro-seismic monitoring technology can directly acquire the hydraulic fracture morphology and the fracture network control volume, but a large number of productivity numerical simulations result in that the effective fracture network control volume is far smaller than the micro-seismic monitoring volume, the real hydraulic fracture network control volume cannot be acquired, and further the fracture network control reserve cannot be accurately calculated. Meanwhile, the conventional reservoir is usually fractured in multiple sections and clusters, and each section cannot use the microseism monitoring technology, so that the test period is long, the test cost is high, and the method is difficult to test and apply in all well sections of oil fields. The oil reservoir numerical simulation method can rapidly acquire the control volume of the whole well section of the horizontal well, further calculate the control volume reserves of the fracture network, but the model acquires physical parameters based on a large number of indoor experiments, meanwhile, the model has more assumption conditions, the simulation result and the actual difference are larger, and the hydraulic fracturing transformation effect is difficult to evaluate. Therefore, a reliable and economical method for predicting the control volume reserves of a horizontal well volume fracture network is needed to further promote the efficient development of unconventional oil and gas.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a method for determining the control reserve of a tight oil reservoir horizontal well volume fracture network. And secondly, establishing a heterogeneous geological model according to physical parameters of a reservoir where the horizontal well is located, and implanting a full-well section gap net control volume of the horizontal well into the geological model to obtain a productivity prediction model. And finally, calculating the effective joint net control volume by utilizing a productivity prediction model according to the production dynamic parameters of the horizontal well, and further quantitatively calculating the joint net control reserve according to the physical property parameters of the reservoir, thereby achieving the purpose of determination.
In order to achieve the technical purpose, the invention provides the following technical scheme:
a prediction method for a tight oil reservoir horizontal well volume fracture network control reserve comprises the following steps:
s1, acquiring and building a joint network control volume prediction model for coupling different influence factors according to basic big data of a predicted horizontal well, and further predicting microseism monitoring joint network control volumes of a whole well section of the horizontal well;
s2, establishing a heterogeneous geological model according to physical parameters of a reservoir where the horizontal well is located, and importing the control volume of the micro-seismic monitoring stitch net of the whole well section of the horizontal well into the geological model to obtain a productivity prediction model;
and S3, calculating an effective fracture network control volume by utilizing a productivity prediction model according to the production dynamic parameters of the horizontal well, further quantitatively calculating fracture network control reserves according to the physical property parameters of the reservoir, defining a fracture network control reserve index of the horizontal well, and quantitatively evaluating the volume fracturing development effect of the horizontal well.
Further, step S1 is described, where a stitch network control volume prediction model for coupling different influencing factors is established according to predicted horizontal well basic big data, and the microseism monitoring stitch network control volume of the whole well section of the horizontal well is further predicted, and specifically includes:
step S101, basic big data are acquired: collecting geomechanical parameters and volume fracturing transformation parameters of each fracturing section of a predicted horizontal well, and controlling the volume of a fracture network monitored by microseism of a measured section of the horizontal well;
step S102, analyzing the influence factors of the control reserves of the tight oil reservoir horizontal well volume fracture network according to the basic big data obtained in the step S101, and further establishing a fracture network control volume prediction matrix M and a prediction reference column M 0 The prediction matrix elements are geomechanical parameters and volume fracturing transformation parameters of the measured section of the horizontal well; the predicted reference column elements are geomechanical parameters and volume fracturing transformation parameters of the horizontal well unmeasured sections;
step S103, coupling the prediction matrix M and the prediction reference series M of step S102 0 Establishing a control volume prediction model A of a horizontal well volume fracture network;
step S104, respectively carrying out standardization processing on the stitch net control volume prediction matrix element, the prediction reference series element and the horizontal well volume prediction model element established in the step S102;
step S105, quantitatively calculating a similarity coefficient between a predicted fracturing segment and a measured fracturing segment of the horizontal well according to the stitch-net control volume prediction matrix standardized element obtained in the step S104;
step S106, sorting the similarity coefficients between the horizontal well predicted fracturing segment and the measured fracturing segment calculated in the step S105, and assigning the measured fracturing segment network control volume corresponding to the maximum similarity coefficient to the predicted fracturing segment;
and S107, calculating the micro-seismic monitoring stitch control volume of the whole well section of the horizontal well according to the single-section micro-seismic monitoring stitch control volume predicted in the step S106.
Further, in step S101, the geomechanical parameters include porosity, permeability, oil saturation, clay content, brittleness index, level stress difference, and fracture pressure; the volume fracturing transformation parameters comprise fracture density, construction displacement, fracturing fluid volume and propping agent.
Further, in step S102, the stitch control volume prediction matrix M and the prediction reference column M 0 And (3) respectively adopting a formula (I) and a formula (II) to calculate:
M 0 ＝[M 0 (1)，M 0 (2)，…，M 0 (n)] (Ⅱ)
wherein: m is a stitch net control volume prediction matrix;
M i (j) Is a prediction matrix element;
m is the number of measured sections of the horizontal well volume fracturing;
n is the number of factors affecting the control volume of the measured section of the horizontal well;
M 0 and the elements of the predicted reference column are geomechanical parameters and volumetric fracturing construction parameters of the horizontal well unmeasured section.
Further, in step S103, the horizontal well volume fracture network control volume prediction model a is calculated by using formula (iii):
wherein: and A is a control volume prediction model of the horizontal well volume fracture network.
In step S104, the standardized element of the stitch control volume prediction matrix is standardized by adopting a formula (IV), the standardized element of the stitch control volume prediction model is standardized by adopting a formula (V),
wherein:
the elements are normalized for the stitch net control volume prediction matrix,
A * the volume prediction matrix is normalized for the stitch net control.
Further, in step S105, the similarity coefficient between the predicted fracture and the measured fracture of the horizontal well is calculated by using formula (vi):
wherein: SI (service information indicator) i Predicting similarity coefficients between the fracturing segment and the measured fracturing segment for the horizontal well, and dimensionless;
Further, in step S107, the microseism monitoring fracture network control volume of the horizontal well whole well section is calculated by using formula (vii):
wherein: SRV is the control volume of the horizontal well full-well section microseism monitoring joint network, 10 4 m 3 ；
SRV i Control volume for single-section microseism monitoring joint net of horizontal well, 10 4 m 3 ；
k is the number of fracturing sections of the horizontal well, and the sections are formed.
Further, the step S2 establishes a heterogeneous geological model according to physical parameters of a reservoir where the horizontal well is located, and introduces the control volume of the micro-seismic monitoring stitch net of the whole well section of the horizontal well into the geological model to obtain a productivity prediction model, specifically comprising,
step S201, basic parameter database establishment: the method comprises the steps of obtaining basic parameters of a horizontal well and basic parameters of a tight oil reservoir where the horizontal well is located, wherein the basic parameters of the horizontal well comprise horizontal section length, fracturing section number and 1 st year cumulative oil production, and the basic parameters of the tight oil reservoir where the horizontal well is located comprise reservoir burial depth, well completion depth, well spacing, reservoir pressure, reservoir temperature, reservoir porosity of each fracturing section of the horizontal well, permeability, oil saturation, reservoir thickness and reservoir fluid parameters;
step S202, establishing a horizontal well geological model by using reservoir numerical simulation software Eclipse according to the basic parameter database established in the step S201, wherein the horizontal well geological model comprises a reservoir porosity distribution field, a permeability distribution field, an oil saturation distribution field and a stratum pressure distribution field;
and step S203, implanting the microseism monitoring joint network control volume of the whole well section of the horizontal well obtained in the step S1 into a heterogeneous geological model to obtain a productivity prediction model.
Further, the step S3 calculates an effective fracture network control volume according to the dynamic parameters of the horizontal well production by using a productivity prediction model, further calculates the fracture network control reserves quantitatively according to the physical parameters of the reservoir, defines a fracture network control reserve index of the horizontal well, and quantitatively evaluates the volume fracturing development effect of the horizontal well, and specifically comprises:
step S301, predicting the first annual cumulative oil production of the horizontal well by utilizing the productivity prediction model established in the step S2, and quantitatively calculating the effective slotted network control volume of the horizontal well by combining the first annual cumulative oil production of the horizontal well history production and the microseism monitoring slotted network control volume of the whole well section of the horizontal well predicted in the step S1;
step S302, according to the porosity and the oil saturation in the basic parameters of the tight oil reservoir where the horizontal well is located and the effective fracture network control volume of the horizontal well calculated in step S301, the fracture network control reserves are calculated quantitatively; and a horizontal well fracture network control reserve index is defined, and the volumetric fracturing development effect of the horizontal well is quantitatively evaluated, wherein the larger the value is, the better the development effect is.
Further, in step S301, the effective control volume of the horizontal well is calculated using formula (viii):
wherein: ESRV is effective net-sewing control volume of horizontal well, 10 4 m 3 ；
Q H Cumulative oil production in 1 st year, t, for historical production of horizontal wells;
Q P and predicting the cumulative oil yield of the 1 st year for the horizontal well, and t.
Further, in step S302, the fracture control reserves are calculated using the formula (ix), and the horizontal well fracture control reserves index is calculated using the formula (x):
wherein: ESRV is effective seam net control of horizontal wellVolume of 10 4 m 3 ；
φ i Porosity of the ith fracturing section of the horizontal well,%;
S oi oil saturation of the ith fracturing section of the horizontal well,%;
k is the number of fracturing sections of the horizontal well, and the sections;
Q R controlling reserves for a horizontal well gap net, t;
EQ is a horizontal well gap net control reserve index, t;
l is the length of the horizontal segment, m;
d is the well spacing of the temporary well and m;
h is the reservoir thickness, m.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, reservoir physical property parameters and volume fracturing transformation parameters which influence the control reserves of the horizontal well volume fracturing fracture network can be comprehensively considered, a multi-level evaluation system is established, the evaluation accuracy is improved, the scientificity of decision making through only a single evaluation index is avoided, and meanwhile, the method is applicable to evaluation and prediction of influence factors of the vertical well fracture control reserves.
2. Compared with the prior art, the prediction method calculates the fracture network control reserve index through actual test big data of a horizontal well mine field, can quantitatively evaluate the volume fracturing effect of the horizontal well, solves the problem of low numerical simulation prediction precision of the oil reservoir, has the advantages of simplicity in calculation, strong operability and the like, is applicable to fracture network control reserve prediction of other similar unconventional tight oil reservoir horizontal wells, has wide application prospect, and has important guiding effect on fracture optimization design.
The foregoing description is only an overview of the technical solution of the present invention, and in order to make the technical means of the present invention more clearly understood, it can be implemented according to the content of the specification, and the following detailed description of the preferred embodiments of the present invention will be given with reference to the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other designs and drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram of a multi-level evaluation system for control reserve influence factors of a horizontal well volume fracture network;
FIG. 2 is a graph showing the result of calculating the similarity coefficient of the prediction segment Fra20 and the ranking thereof according to the present invention;
fig. 3 is a graph showing the calculation result and the ranking of the similarity coefficient of the prediction segment Fra21 according to the present invention.
In order to more clearly illustrate the present invention, the present invention will be further described with reference to preferred embodiments. It is to be understood by persons skilled in the art that the following detailed description is illustrative and not restrictive, and that this invention is not limited to the details given herein.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
A prediction method for a tight oil reservoir horizontal well volume fracture network control reserve comprises the following steps:
s1, acquiring and building a joint network control volume prediction model for coupling different influence factors according to basic big data of a predicted horizontal well, and further predicting microseism monitoring joint network control volumes of a whole well section of the horizontal well;
s2, establishing a heterogeneous geological model according to physical parameters of a reservoir where the horizontal well is located, and importing the control volume of the micro-seismic monitoring stitch net of the whole well section of the horizontal well into the geological model to obtain a productivity prediction model;
and S3, calculating an effective fracture network control volume by utilizing a productivity prediction model according to the production dynamic parameters of the horizontal well, further quantitatively calculating fracture network control reserves according to the physical property parameters of the reservoir, defining a fracture network control reserve index of the horizontal well, and quantitatively evaluating the volume fracturing development effect of the horizontal well, wherein the larger the value is, the better the development effect is.
Further, step S1 is described, where a stitch network control volume prediction model for coupling different influencing factors is established according to predicted horizontal well basic big data, and the microseism monitoring stitch network control volume of the whole well section of the horizontal well is further predicted, and specifically includes:
step S101, basic big data are acquired: collecting geomechanical parameters and volume fracturing transformation parameters of each fracturing section of a predicted horizontal well, and controlling the volume of a fracture network monitored by microseism of a measured section of the horizontal well; wherein the geomechanical parameters include porosity, permeability, oil saturation, clay content, brittleness index, level stress difference, and fracture pressure; the volume fracturing transformation parameters comprise fracture density, construction displacement, fracturing fluid quantity and propping agent;
step S102, analyzing the influence factors of the control reserves of the tight oil reservoir horizontal well volume fracture network according to the basic big data obtained in the step S101, and further establishing a fracture network control volume prediction matrix M and a prediction reference column M 0 Specifically, as shown in a formula (I) and a formula (II), the prediction matrix elements are geomechanical parameters and volume fracturing transformation parameters of the measured section of the horizontal well; the predicted reference column elements are geomechanical parameters and volume fracturing transformation parameters of the horizontal well unmeasured sections;
M 0 ＝[M 0 (1)，M 0 (2)，…，M 0 (n)] (Ⅱ)
wherein: m is a stitch net control volume prediction matrix;
M i (j) Is a prediction matrix element;
m is the number of measured sections of the horizontal well volume fracturing;
n is the number of factors affecting the control volume of the measured section of the horizontal well;
M 0 the elements of the prediction reference column are geomechanical parameters and volumetric fracturing construction parameters of the horizontal well unmeasured section;
step S103, coupling the prediction matrix M and the prediction reference series M of step S102 0 Establishing a control volume prediction model A of a horizontal well volume fracture network, wherein the control volume prediction model A is represented by the following formula (III):
wherein: a is a control volume prediction model of a horizontal well volume fracture network;
step S104, the stitch net control volume prediction matrix elements and the prediction reference series elements established in the step S102 are respectively shown in the following formula (IV); and (3) carrying out standardization treatment on the horizontal well volume prediction model elements established in the step S103, wherein the following formula (V) is adopted:
wherein:
the elements are normalized for the stitch net control volume prediction matrix,
A * controlling a volume prediction matrix standardization matrix for the stitch net;
step S105, quantitatively calculating a similarity coefficient between a predicted fracturing segment and a measured fracturing segment of the horizontal well by adopting a formula (VI) according to the stitch network control volume prediction matrix standardized element obtained in the step S104;
wherein: SI (service information indicator) i Predicting similarity coefficients between the fracturing segment and the measured fracturing segment for the horizontal well, and dimensionless;
Step S106, sorting the similarity coefficients between the horizontal well predicted fracturing segment and the measured fracturing segment calculated in the step S105, and assigning the measured fracturing segment network control volume corresponding to the maximum similarity coefficient to the predicted fracturing segment;
step S107, calculating the micro-seismic monitoring stitch control volume of the whole well section of the horizontal well by adopting a formula (VII) according to the single-section micro-seismic monitoring stitch control volume predicted in the step S106:
wherein: SRV is the control volume of the horizontal well full-well section microseism monitoring joint network, 10 4 m 3 ；
SRV i Control volume for single-section microseism monitoring joint net of horizontal well, 10 4 m 3 ；
k is the number of fracturing sections of the horizontal well, and the sections are formed.
Further, the step S2 establishes a heterogeneous geological model according to physical parameters of a reservoir where the horizontal well is located, and introduces the control volume of the micro-seismic monitoring stitch net of the whole well section of the horizontal well into the geological model to obtain a productivity prediction model, specifically comprising,
step S201, basic parameter database establishment: the method comprises the steps of obtaining basic parameters of a horizontal well and basic parameters of a tight oil reservoir where the horizontal well is located, wherein the basic parameters of the horizontal well comprise horizontal section length, fracturing section number and 1 st year cumulative oil production, and the basic parameters of the tight oil reservoir where the horizontal well is located comprise reservoir burial depth, well completion depth, well spacing, reservoir pressure, reservoir temperature, reservoir porosity of each fracturing section of the horizontal well, permeability, oil saturation, reservoir thickness and reservoir fluid parameters;
step S202, establishing a horizontal well geological model by using reservoir numerical simulation software Eclipse according to the basic parameter database established in the step S201, wherein the horizontal well geological model comprises a reservoir porosity distribution field, a permeability distribution field, an oil saturation distribution field and a stratum pressure distribution field;
and step S203, implanting the microseism monitoring joint network control volume of the whole well section of the horizontal well obtained in the step S1 into a heterogeneous geological model to obtain a productivity prediction model.
Further, the step S3 calculates an effective fracture network control volume according to the dynamic parameters of the horizontal well production by using a productivity prediction model, further calculates the fracture network control reserves quantitatively according to the physical parameters of the reservoir, defines a fracture network control reserve index of the horizontal well, and quantitatively evaluates the volume fracturing development effect of the horizontal well, and specifically comprises:
step S301, predicting the first annual cumulative oil production of the horizontal well by utilizing the productivity prediction model established in the step S2, and quantitatively calculating the effective stitch control volume of the horizontal well by adopting a formula (VIII) by combining the first annual cumulative oil production of the horizontal well history production and the microseism monitoring stitch control volume of the whole well section of the horizontal well predicted in the step S1:
wherein: ESRV is effective net-sewing control volume of horizontal well, 10 4 m 3 ；
Q H Cumulative oil production in 1 st year, t, for historical production of horizontal wells;
Q P predicting annual cumulative oil production for horizontal wells，t
Step S302, according to the porosity and the oil saturation in the basic parameters of the tight oil reservoir where the horizontal well is located and the effective fracture network control volume of the horizontal well calculated in step S301, quantitatively calculating the fracture network control reserve by adopting a formula (IX); calculating a horizontal well fracture network control reserve index by adopting a formula (X), and quantitatively evaluating the volume fracturing development effect of the horizontal well, wherein the formula (IX) and the formula (X) are specifically as follows:
wherein: ESRV is effective net-sewing control volume of horizontal well, 10 4 m 3 ；
φ i Porosity of the ith fracturing section of the horizontal well,%;
S oi oil saturation of the ith fracturing section of the horizontal well,%;
k is the number of fracturing sections of the horizontal well, and the sections;
Q R controlling reserves for a horizontal well gap net, t;
EQ is a horizontal well gap net control reserve index, t;
l is the length of the horizontal segment, m;
d is the well spacing of the temporary well and m;
h is the reservoir thickness, m.
Example 1
The following detailed description of the embodiments of the invention, with reference to the figures and the Erdos basin-tight reservoir as examples, illustrates the applicability of the method.
The Erdos basin tight oil reservoir is rich in resources, is mainly distributed in an extension group, has huge development potential, forms a 'big well cluster, a long horizontal well, subdivision cutting, a soluble ball seat and industrialization' technical mode of the tight oil reservoir through years of practice, and has the advantages that the coverage volume of a seam network is greatly improved through underground microseism monitoring, the volume transformation is realized, and the yield is greatly improved. But the basin shale oil has the characteristics of low pressure coefficient, low brittleness index, longitudinal multi-interlayer and the like, and has great difference with the North American shale oil. As shale oil continues to develop, volumetric fracturing technology and reservoir matching also face challenges such as how to evaluate volumetric fracturing effects, whether existing process parameters achieve an optimal match with the reservoir is currently difficult to answer, with single well fracture network control reserves being the most important indicator to evaluate volumetric fracturing technology suitability. The example predicts that the horizontal well WY1 is positioned in a basin tight oil reservoir main development test area, the reservoir burial depth is 2135m, the horizontal section length is 1980m, the reservoir thickness is 14.2m, the adjacent well pitch is 500m, and the method has strong heterogeneity, so that the fracture network control reserve prediction after the volume fracturing of the horizontal well has great challenges.
The embodiment provides a complete prediction method for the control reserve of a tight oil reservoir horizontal well volume fracture network, which comprises the following steps:
1. based on geological and volume fracturing transformation parameter big data of each fracturing section of the horizontal well, a joint network control volume evaluation matrix for coupling different influence factors is established, the calculated similarity coefficient between the predicted fracturing section and the measured fracturing section is calculated quantitatively, and the joint network control volume of the whole well section of the horizontal well is monitored by microseism, wherein the concrete contents are as follows:
(1) And collecting the microseism monitoring joint network control volume of the predicted horizontal well or the pressure-measured fracture section of the same fracturing process which is used for the same layer of the same platform. As shown in table 2.
(2) And establishing a multi-level evaluation system for controlling the volume factors of the fracture network by using an analytic hierarchy process, wherein the multi-level evaluation system affects the volume fracture network of the horizontal well of the tight oil reservoir, and is shown in figure 1. Including geomechanical parameters and volumetric fracture modification parameters. The geomechanical parameters comprise the porosity, permeability, oil saturation, clay content, brittleness index, horizontal stress difference and fracture pressure of each fracturing section of the horizontal well; the volumetric fracture modification parameters include fracture density, construction displacement, fracturing fluid volume, propping agent dosage, as shown in tables 1 and 2.
Table 1 geomechanical parameters table for each fracture zone of horizontal well WY1
Table 2 volume fracturing modification parameters and microseism monitoring fracture network control volume table for each fracturing stage of horizontal well WY1 well
(3) According to the multi-level evaluation system factor data in the steps (1) and (2), a stitch network control volume prediction matrix M and a prediction reference column M are established 0 Wherein the expression of the prediction matrix M is shown as (XI), and the reference column M is predicted 0 The elements are geomechanical parameters and volume fracturing modification parameters of the unmeasured segments in tables 1 and 2, and a prediction reference column M 0 The expression of (XII) is shown in the specification;
(4) Coupling prediction matrix M and prediction reference series M 0 A horizontal well volume fracture network control volume prediction model A is established, a prediction reference column is taken as an example of Fra20, and the calculation is shown as an expression (XIII).
(5) And (3) standardizing the control volume prediction model element of the horizontal well volume fracture network established in the step (4) by using a formula (IV).
(6) According to the standardized element of the fracture network control volume prediction matrix, the similarity coefficient between the horizontal well prediction fracturing segment Fra20 and the measured fracturing segments Fra1 to Fra19 is quantitatively calculated by using a formula (VI), and the calculation results are shown in table 3.
TABLE 3 ranking of similarity coefficients between predicted segments Fra 20-Fra 21 and measured segments Fra 1-Fra 19
(7) Sequencing the similarity coefficients between the horizontal well predicted fracture section and the measured fracture section calculated in the step (6), wherein the similarity coefficients are shown in fig. 2 and fig. 3. Wherein the similarity coefficient between the predicted frac section Fra20 and the already-pressure-measured Fra11 is the largest, and therefore the net-stitch control volume of the predicted Fra20 is 253×10 as the net-stitch control volume of Fra11 4 m 3 . Repeating steps (4) - (6) by the same method to obtain the maximum similarity coefficient between the predicted frac section Fra21 and the measured frac section Fra14, so that the seam control volume of the predicted Fra21 is 240×10 identical to the seam control volume of Fra14 4 m 3 。
(8) Calculating the control volume of the single-section microseism monitoring stitch net according to the control volume of the single-section microseism monitoring stitch net predicted in the step (7), wherein the control volume of the microseism monitoring stitch net of the whole well section of the horizontal well is 4537 multiplied by 10 4 m 3 。
2. According to physical parameters of a reservoir where the horizontal well is located, establishing a heterogeneous geological model by using reservoir numerical simulation software Eclipse, and importing microseism monitoring fracture network control volumes of the whole well section of the horizontal well into the geological model to obtain a productivity prediction model, wherein the specific contents are as follows:
(1) And (3) establishing a basic parameter database: the parameters of the horizontal well and the parameters of the tight oil reservoir where the horizontal well is located, wherein the length of the horizontal well basic horizontal section, the number of fracturing sections and the cumulative oil production in 1 st year are included in the parameters of the tight oil reservoir where the horizontal well is located, such as the burial depth of the reservoir, the depth of the well to be drilled, the well spacing between the adjacent wells, the thickness of the reservoir, the formation pressure, the formation temperature, the average porosity of the reservoir, the average permeability, the average oil saturation, the reservoir fluid and the like, and are shown in table 4.
Table 4 table of geological base parameters of the reservoir where the predicted horizontal well WY1 is located
| Parameters (parameters) | Numerical value | Parameters (parameters) | Numerical value |
| Reservoir burial depth (m) | 2135 | Saturation with oil (%) | 55.2 |
| Drilling well depth (m) | 4034 | Porosity (%) | 12.1 |
| Linjing distance (m) | 500 | Formation crude oil volume coefficient (/) | 1.30 |
| Reservoir thickness (m) | 14.2 | Crude oil viscosity (mPa.s) | 1.50 |
| Formation pressure (MPa) | 19.7 | Horizontal segment length (m) | 1980 |
| Formation temperature (. Degree. C.) | 68.2 | Number of fracturing segments (segment) | 21 |
| Permeability (mD) | 0.13 | Cumulative oil production (t) of 1 st year | 3560 |
(2) And establishing a heterogeneous geological model of the horizontal well by using reservoir numerical simulation software Eclipse according to the basic parameter database, wherein the heterogeneous geological model comprises a reservoir porosity distribution field, a permeability distribution field, an oil saturation distribution field and a stratum pressure distribution field.
(3) Implanting the microseism monitoring joint network control volume of the horizontal well whole well section obtained in the step 2) into a geological model to obtain a productivity prediction model.
3. According to the production dynamic parameters of the horizontal well, calculating the effective joint net control volume by utilizing the productivity prediction model, and further quantitatively calculating the joint net control reserve according to the physical property parameters of the reservoir. The specific contents are as follows:
(1) Predicting the first annual cumulative oil yield of the horizontal well to be 13692t by utilizing the productivity prediction model established in the step 2, and quantitatively calculating the effective slotted network control volume of the horizontal well to be 1179.6 multiplied by 10 by utilizing a formula (VIII) by combining the first annual cumulative oil yield (table 4) of the historical production of the horizontal well and the microseism monitoring slotted network control volume of the whole well section of the horizontal well predicted in the step 1 4 m 3 。
(2) According to the physical property parameter porosity and the oil saturation of each fracturing section of the horizontal well (see table 1) and the effective joint net control volume of the horizontal well, the quantitative calculation joint net control reserve of 6.2328 multiplied by 10 is utilized by a formula (IX) 5 m 3 . At the same time, the formula (X) is utilized to calculate the horizontal well seamThe net control reserve index was 0.891. The fracture network control reserve index can be used for quantitatively evaluating the volume fracturing development effect of the horizontal well, and the larger the fracture network control reserve index is, the better the development effect is.
The fracture network control reserve prediction method provided by the invention calculates the fracture network control reserve index through the actual test big data of the horizontal well mine field, can quantitatively evaluate the volume fracturing effect of the horizontal well, solves the problem of low oil reservoir numerical simulation prediction precision, has the advantages of simple calculation, strong operability and the like, is also applicable to fracture network control reserve prediction of other similar unconventional tight oil reservoirs, has wide application prospect, and has important guiding function on fracturing optimization design.
The foregoing is a specific description of the present invention by way of example, and it is to be understood that this example is merely a preferred embodiment of the invention and is not intended to limit the invention to the form disclosed herein, but is not to be construed as excluding other embodiments. And the modifications and simple changes carried out by the person skilled in the art do not deviate from the technical idea and scope of the invention, and all belong to the protection scope of the technical scheme of the invention.
Claims (11)
1. The method for predicting the control reserve of the volume fracture network of the tight oil reservoir horizontal well is characterized by comprising the following steps of:
s1, acquiring and building a joint network control volume prediction model for coupling different influence factors according to basic big data of a predicted horizontal well, and further predicting microseism monitoring joint network control volumes of a whole well section of the horizontal well;
s2, establishing a heterogeneous geological model according to physical parameters of a reservoir where the horizontal well is located, and importing the control volume of the micro-seismic monitoring stitch net of the whole well section of the horizontal well into the geological model to obtain a productivity prediction model;
and S3, calculating an effective fracture network control volume by utilizing a productivity prediction model according to the production dynamic parameters of the horizontal well, further quantitatively calculating fracture network control reserves according to the physical property parameters of the reservoir, defining a fracture network control reserve index of the horizontal well, and quantitatively evaluating the volume fracturing development effect of the horizontal well.
2. The method for predicting the volumetric fracture network control reserves of the tight oil reservoir horizontal well according to claim 1, wherein the step S1 is to obtain and build a fracture network control volume prediction model coupled with different influencing factors according to the predicted horizontal well basic big data, and further predict the microseism monitoring fracture network control volume of the horizontal well whole well section, and specifically comprises the following steps:
step S101, basic big data are acquired: collecting geomechanical parameters and volume fracturing transformation parameters of each fracturing section of a predicted horizontal well, and controlling the volume of a fracture network monitored by microseism of a measured section of the horizontal well;
step S102, analyzing the influence factors of the control reserves of the tight oil reservoir horizontal well volume fracture network according to the basic big data obtained in the step S101, and further establishing a fracture network control volume prediction matrix M and a prediction reference column M 0 The prediction matrix elements are geomechanical parameters and volume fracturing transformation parameters of the measured section of the horizontal well; the predicted reference column elements are geomechanical parameters and volume fracturing transformation parameters of the horizontal well unmeasured sections;
step S103, coupling the prediction matrix M and the prediction reference series M of step S102 0 Establishing a control volume prediction model A of a horizontal well volume fracture network;
step S104, respectively carrying out standardization processing on the stitch net control volume prediction matrix element, the prediction reference series element and the horizontal well volume prediction model element established in the step S102;
step S105, quantitatively calculating a similarity coefficient between a predicted fracturing segment and a measured fracturing segment of the horizontal well according to the stitch-net control volume prediction matrix standardized element obtained in the step S104;
step S106, sorting the similarity coefficients between the horizontal well predicted fracturing segment and the measured fracturing segment calculated in the step S105, and assigning the measured fracturing segment network control volume corresponding to the maximum similarity coefficient to the predicted fracturing segment;
and S107, calculating the micro-seismic monitoring stitch control volume of the whole well section of the horizontal well according to the single-section micro-seismic monitoring stitch control volume predicted in the step S106.
3. The method for predicting volumetric fracture network controlled reserves of a tight reservoir horizontal well of claim 2, wherein in step S101, the geomechanical parameters include porosity, permeability, oil saturation, clay content, brittleness index, horizontal stress difference, fracture pressure; the volume fracturing transformation parameters comprise fracture density, construction displacement, fracturing fluid volume and propping agent.
4. The method for predicting volumetric fracture network control reserves of a tight reservoir horizontal well according to claim 2, wherein in step S102, the fracture network control volume prediction matrix M and the prediction reference column M 0 And (3) respectively adopting a formula (I) and a formula (II) to calculate:
M 0 ＝[M 0 (1)，M 0 (2)，…，M 0 (n)] (Ⅱ)
wherein: m is a stitch net control volume prediction matrix;
M i (j) Is a prediction matrix element;
m is the number of measured sections of the horizontal well volume fracturing;
n is the number of factors affecting the control volume of the measured section of the horizontal well;
M 0 and the elements of the predicted reference column are geomechanical parameters and volumetric fracturing construction parameters of the horizontal well unmeasured section.
5. The method for predicting the volumetric fracture network control reserves of the tight reservoir horizontal well according to claim 2, wherein in step S103, the volumetric fracture network control volume prediction model A of the tight reservoir horizontal well is calculated by adopting a formula (III), in step S104, the fracture network control volume prediction matrix standardization element is standardized by adopting a formula (IV), the fracture network control volume prediction model element is standardized by adopting a formula (V),
wherein: a is a control volume prediction model of a horizontal well volume fracture network;
A * the volume prediction matrix is normalized for the stitch net control.
6. The method for predicting volumetric fracture network controlled reserves of a tight reservoir horizontal well of claim 2, wherein in step S105, the similarity coefficient between the predicted and measured fracture segments of the horizontal well is calculated using formula (vi):
wherein: SI (service information indicator) i Predicting similarity coefficients between the fracturing segment and the measured fracturing segment for the horizontal well, and dimensionless;
7. The method for predicting volumetric fracture network control reserves of a tight reservoir horizontal well of claim 2, wherein in step S107, the microseismic monitoring fracture network control volume of the horizontal well full well segment is calculated using formula (vii):
wherein: SRV is the control volume of the horizontal well full-well section microseism monitoring joint network, 10 4 m 3 ；
SRV i Control volume for single-section microseism monitoring joint net of horizontal well, 10 4 m 3 ；
k is the number of fracturing sections of the horizontal well, and the sections are formed.
8. The method for predicting the volumetric fracture network control reserves of the tight oil reservoir horizontal well according to claim 1, wherein the step S2 establishes a heterogeneous geological model according to physical parameters of the reservoir where the horizontal well is located, and introduces the horizontal well full-well section microseism monitoring fracture network control volumes into the geological model to obtain a productivity prediction model, and the method specifically comprises the following steps:
step S201, basic parameter database establishment: the method comprises the steps of obtaining basic parameters of a horizontal well and basic parameters of a tight oil reservoir where the horizontal well is located, wherein the basic parameters of the horizontal well comprise horizontal section length, fracturing section number and 1 st year cumulative oil production, and the basic parameters of the tight oil reservoir where the horizontal well is located comprise reservoir burial depth, well completion depth, well spacing, reservoir pressure, reservoir temperature, reservoir porosity of each fracturing section of the horizontal well, permeability, oil saturation, reservoir thickness and reservoir fluid parameters;
step S202, establishing a horizontal well geological model by using reservoir numerical simulation software Eclipse according to the basic parameter database established in the step S201, wherein the horizontal well geological model comprises a reservoir porosity distribution field, a permeability distribution field, an oil saturation distribution field and a stratum pressure distribution field;
and step S203, implanting the microseism monitoring joint network control volume of the whole well section of the horizontal well obtained in the step S1 into a heterogeneous geological model to obtain a productivity prediction model.
9. The method for predicting the volumetric fracture network control reserves of the tight oil reservoir horizontal well according to claim 1, wherein the step S3 calculates the effective fracture network control volume according to the dynamic parameters of the horizontal well production by using a productivity prediction model, further quantitatively calculates the fracture network control reserves according to the physical parameters of the reservoir, defines the fracture network control reserves index of the horizontal well, and quantitatively evaluates the volumetric fracture development effect of the horizontal well, and specifically comprises:
step S301, predicting the first annual cumulative oil production of the horizontal well by utilizing the productivity prediction model established in the step S2, and quantitatively calculating the effective slotted network control volume of the horizontal well by combining the first annual cumulative oil production of the horizontal well history production and the microseism monitoring slotted network control volume of the whole well section of the horizontal well predicted in the step S1;
step S302, according to the porosity and the oil saturation in the basic parameters of the tight oil reservoir where the horizontal well is located and the effective fracture network control volume of the horizontal well calculated in step S301, the fracture network control reserves are calculated quantitatively; and defining a horizontal well fracture network control reserve index, and quantitatively evaluating the volume fracturing development effect of the horizontal well.
10. The method for predicting volumetric fracture network control reserves of a tight reservoir horizontal well of claim 9, wherein in step S301, the effective fracture network control volume of the horizontal well is calculated using formula (viii):
wherein: ESRV is effective net-sewing control volume of horizontal well, 10 4 m 3 ；
Q H Cumulative oil production in 1 st year, t, for historical production of horizontal wells;
Q P and predicting the cumulative oil yield of the 1 st year for the horizontal well, and t.
11. The method for predicting tight reservoir horizontal well volume fracture network control reserves of claim 9, wherein in step S302, the fracture network control reserves are calculated using formula (ix), and the horizontal well fracture network control reserves index is calculated using formula (x):
wherein: ESRV is effective net-sewing control volume of horizontal well, 10 4 m 3 ；
φ i Porosity of the ith fracturing section of the horizontal well,%;
S oi oil saturation of the ith fracturing section of the horizontal well,%;
k is the number of fracturing sections of the horizontal well, and the sections;
Q R controlling reserves for a horizontal well gap net, t;
EQ is a horizontal well gap net control reserve index, t;
l is the length of the horizontal segment, m;
d is the well spacing of the temporary well and m;
h is the reservoir thickness, m.
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| CN117077573A (en) * | 2023-10-16 | 2023-11-17 | 西安石油大学 | Quantitative characterization method and system for shale oil reservoir laminated fracture network morphology |
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| CN117077572A (en) * | 2023-10-16 | 2023-11-17 | 西安石油大学 | Quantitative characterization method for multi-cluster crack expansion uniformity degree of shale oil reservoir |
| CN117077573A (en) * | 2023-10-16 | 2023-11-17 | 西安石油大学 | Quantitative characterization method and system for shale oil reservoir laminated fracture network morphology |
| CN117077572B (en) * | 2023-10-16 | 2024-01-26 | 西安石油大学 | Quantitative characterization method for multi-cluster crack expansion uniformity degree of shale oil reservoir |
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