CN114880957B - Unconventional reservoir fracturing perforation section cluster parameter combination optimization method - Google Patents
Unconventional reservoir fracturing perforation section cluster parameter combination optimization method Download PDFInfo
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Abstract
The invention discloses a parameter combination optimization method for unconventional reservoir fracturing perforation segment clusters, which comprises the following steps: s1: screening candidate parameters for reservoir classification; s2: screening reservoir classification parameters; s3: clustering and classifying the reservoirs; s4: establishing a single-cluster fracturing production integrated numerical model II aiming at different reservoir types; s5: the target horizontal well is combined in a segmented mode, and the optimized segment length of each fracturing segment is determined; s6: judging the type of the reservoir to which each fracturing section belongs, and determining the optimized cluster spacing and the optimized injection scale; s7: optimizing the position of the perforation cluster by using an interior point method; s8: and establishing a single-section multi-cluster fracturing numerical model, and determining the optimized perforation number of each perforation cluster of each fracturing section. The method can systematically and effectively realize the fracturing perforation section cluster combination scheme design of the horizontal well of the unconventional reservoir, reduce the unbalanced fracture initiation probability in the section, improve the production benefit of the single well, and has important significance for fracturing design and yield increase and efficiency improvement of the unconventional reservoir.
Description
Technical Field
The invention relates to the technical field of hydraulic fracturing of petroleum engineering, in particular to a method for optimizing parameter combination of a fracturing perforation section cluster of an unconventional reservoir.
Background
The horizontal well multistage hydraulic fracturing technology is used as an effective technical method for unconventional reservoir transformation, and is widely applied to the production and development process of oil and gas reservoirs at present. Based on the current situation, the reasonable design of the fracturing perforation section cluster combination becomes the key for directly influencing the reservoir transformation effect and increasing the yield and the efficiency, and is also the key point of current attention.
The existing horizontal well multistage fracturing segmented design is mainly finished by equal-interval division or according to engineering experience, and the clustering design is mainly based on qualitative judgment, so that the problems of low efficiency, poor precision, low automation degree and the like generally exist. From the actual fracturing site, a reasonable and effective section cluster combination scheme is quickly designed by utilizing the existing logging information, the efficient and accurate subsection and clustering design of the horizontal well fracturing is realized, and the method has important significance for the fracturing production guidance and the commercial development of unconventional oil reservoirs.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a parameter combination optimization method for unconventional reservoir fracturing perforation segment clusters.
The technical scheme of the invention is as follows:
a parameter combination optimization method for unconventional reservoir fracturing perforation segment clusters comprises the following steps:
s1: collecting basic parameters in a reservoir logging curve, analyzing the correlation among the basic parameters, and selecting the basic parameters or basic parameter combinations with the correlation smaller than a threshold value as reservoir classification candidate parameters; the basic parameters comprise geological physical parameters and geomechanical parameters;
s2: establishing a single-cluster fracturing production integrated numerical model I on the basis of the average value of each basic parameter, carrying out capacity numerical simulation according to the single-cluster fracturing production integrated numerical model I, analyzing the correlation between the reservoir classification candidate parameters and the capacity according to the capacity numerical simulation result, and selecting the parameter or the parameter combination with the strongest correlation with the capacity from the reservoir classification candidate parameters as the reservoir classification parameters;
s3: clustering the reservoir classification parameters by adopting a clustering algorithm, classifying drilling reservoirs of all wells according to a clustering result, and obtaining an average value of each basic parameter of each type of reservoir;
s4: establishing a single-cluster fracturing production integrated numerical model II on the basis of the average value of each basic parameter corresponding to each reservoir type, and performing numerical simulation according to the single-cluster fracturing production integrated numerical model II to obtain 15-year accumulated oil production of single-cluster cracks of each reservoir type under the conditions of different liquid injection scales and different cluster intervals;
s5: dividing a target horizontal well into a plurality of initial small sections, combining the initial small sections according to the similarity of the reservoir classification candidate parameters of the adjacent initial small sections, realizing the division of fracturing sections, and determining the optimized section length of each fracturing section;
s6: judging the type of the reservoir to which each fracturing section belongs, calculating theoretical benefits of each fracturing section under the conditions of different liquid injection scales and different cluster distances according to the 15-year accumulated oil production of the single cluster cracks of each reservoir type under the conditions of different liquid injection scales and different cluster distances obtained in the step S4, and determining the optimized cluster distance and the optimized liquid injection scale of each fracturing section according to the theoretical benefits;
s7: performing perforation cluster equal division placement on each fracturing section according to the optimized cluster spacing, and optimizing the positions of the perforation clusters by using an interior point method;
s8: and establishing a single-section multi-cluster fracturing numerical model by taking the average value of each basic parameter of each corresponding reservoir type of each fracturing section as a basis and combining the optimized perforation cluster positions, and determining the optimized perforation number of each perforation cluster in each fracturing section.
Preferably, in step S1, the geomechanical parameters include porosity, oil saturation, and permeability, and the geomechanical parameters include minimum level principal stress, brittleness index, young 'S modulus, and poisson' S ratio.
Preferably, in step S1, when analyzing the correlation, a pearson correlation coefficient analysis method is used to analyze the correlation, and the basic parameter or the combination of the basic parameters with the correlation coefficient smaller than 0.5 is selected as the candidate parameter for reservoir classification.
Preferably, in step S1, the reservoir classification candidate parameters are product of porosity and oil saturation, brittleness index, and minimum level principal stress; in step S2, the reservoir classification parameter is a product of porosity and oil saturation.
Preferably, in step S3, when the drilling and encountering reservoirs of each well are classified according to the clustering result, the reservoirs are classified into four types; wherein the product of the porosity and oil saturation of a class i reservoir is greater than 650; the product of the porosity and the oil saturation of the II reservoir is within the range of (560, 650), the product of the porosity and the oil saturation of the III reservoir is within the range of (440, 560), and the product of the porosity and the oil saturation of the IV reservoir is less than or equal to 440.
Preferably, in step S5, the merging according to the similarity of the candidate parameters for reservoir classification of adjacent initial segments specifically includes the following sub-steps:
s51: calculating the average change rate I of the candidate parameters of the reservoir classifications of an initial subsection and an initial subsection adjacent to the left side of the initial subsection, and calculating the average change rate II of the candidate parameters of the reservoir classifications of the initial subsection and an initial subsection adjacent to the right side of the initial subsection;
s52: and judging whether the initial small section is combined with two adjacent initial small sections on the left side and the right side of the initial small section according to the average change rate I and the average change rate II:
when the average change rate I or the average change rate II is less than 20%, the initial small section is merged with the adjacent initial small section with the average change rate of less than 20%;
when the average change rate I and the average change rate II are both smaller than 20%, selecting adjacent initial small sections with smaller average change rates to merge;
s53: taking the merged segments as new segments, taking the average value of the reservoir classification candidate parameters of the merged segments as the value of the reservoir classification candidate parameters of the new segments, and repeating the steps S51-S53 until the algorithm stops or the segment length constraint is reached;
in step S52, if both average rates of change of a certain segment are greater than or equal to 20%, and the segment does not reach the minimum segment length, the segment is merged with an adjacent segment with a smaller average rate of change; and if the two average change rates of a small section are both more than or equal to 20 percent and the section length of the small section is more than or equal to the minimum section length, taking the small section as an independent fracturing section.
Preferably, in step S6, the theoretical benefit is calculated by the following formula:
in the formula: p is the single stage theoretical yield, Yuan; n is the number of single well clusters; r is o Profit for crude oil, yuan/ton; q. q.s o One cluster of oil production, one ton; e.g. of the type c Cost of fracturing fluid, yuan/fang; v c The single-cluster liquid injection amount is used; e.g. of the type p For proppant cost, yuan/square; v p The dosage of the single-cluster propping agent is shown in the specification; e.g. of the type s For fracturing service fee, element/segment; s n Is a single wellThe number of fracturing stages.
Preferably, in step S7, optimizing the perforation cluster position by using the interior point method specifically includes the following sub-steps: allowing the perforation clusters to move in the sections, searching the minimum position of the minimum horizontal main stress difference in the range of left and right searching threshold values of the initial positions of the perforation clusters by using an interior point method, so that the internal stress difference of the sections is smaller than the critical value of the minimum horizontal main stress difference for balanced initiation, and meanwhile, the spacing between the restraint clusters is not smaller than 5m, thereby determining the optimized positions of the perforation clusters; and when the set search threshold value cannot meet the condition that the section internal stress difference is smaller than the minimum level main stress difference critical value of the balanced crack initiation, enlarging the search range.
Preferably, in step S8, when determining the optimized perforation number of each perforation cluster in each fracturing segment, setting an initial perforation number, and simulating a current-limiting fracturing perforation number when the difference of the internal stress of the segment is a minimum level main stress difference critical value of reservoir equilibrium fracture initiation, and the difference of the liquid inlet amount between each cluster in the segment is less than 30%, where the current-limiting fracturing perforation number is the optimized perforation number.
Preferably, when the initial perforation number is set, a conventional fracturing perforation number is adopted, and the conventional fracturing perforation number is an arbitrary integer from 8 to 12.
The invention has the beneficial effects that:
the invention can complete a complete set of complete perforation section cluster parameter combination optimization design of the horizontal well based on logging information, provides a method for dividing a fracturing section based on logging curve similarity, selects optimal fracturing construction parameters by considering fracturing site feasibility from the practical starting point of fracturing conditions, and provides a reference balanced fracture initiation and crack distribution principle, can quickly and effectively realize the fracturing perforation section cluster combination scheme design of the horizontal well of the unconventional reservoir, reduces the unbalanced fracture initiation probability in the section, improves the production benefit of a single well, and has important significance for the fracturing design and yield increase improvement of the unconventional reservoir.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art 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 for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for optimizing the parameter combination of a fractured perforation segment cluster of an unconventional reservoir according to the invention;
FIG. 2 is a schematic diagram of horizontal well fracture staged design results in a particular embodiment; wherein, fig. 2(a) is a schematic diagram of the relationship between the sounding of the fracturing section and the brittleness index, fig. 2(b) is a schematic diagram of the relationship between the sounding of the fracturing section and the minimum horizontal principal stress, and fig. 2(c) is a schematic diagram of the relationship between the sounding of the fracturing section and the product of the porosity and the oil saturation;
FIG. 3 is a chart of type I reservoir fracturing construction parameter selection in one embodiment;
FIG. 4 is a chart of type II reservoir fracturing construction parameter selection in one embodiment;
FIG. 5 is a chart illustrating selection of fracturing parameters for a class III reservoir in an exemplary embodiment;
FIG. 6 illustrates a single segment fracture morphology at different stress differences in an embodiment; wherein the stress difference of fig. 6(a) is 0MPa, the stress difference of fig. 6(b) is 1.0MPa, the stress difference of fig. 6(c) is 2.0MPa, and the stress difference of fig. 6(d) is 2.1 MPa;
FIG. 7 is a comparison graph of a pre-and post-fracturing design scenario for fine tuning of a perforation cluster based on the principle of balanced fracture initiation and placement in an exemplary embodiment; wherein FIG. 7(a) is the optimized solution of the present invention, and FIG. 7(b) is the solution of the present invention before optimization;
FIG. 8 is an enlarged view of a portion of A in FIG. 7; wherein FIG. 8(a) is the optimized version of the present invention, and FIG. 8(b) is the version of the present invention before optimization;
FIG. 9 is an enlarged partial view of B in FIG. 7; wherein, fig. 9(a) is the optimized scheme of the invention, and fig. 9(b) is the scheme of the invention before optimization.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the drawings. It should be noted that, in the present application, the embodiments and the technical features of the embodiments may be combined with each other without conflict. It is noted that, unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The use of the terms "comprising" or "including" and the like in the present disclosure is intended to mean that the elements or items listed before the term cover the elements or items listed after the term and their equivalents, but not to exclude other elements or items.
As shown in fig. 1, the invention provides a method for optimizing the parameter combination of a fracturing perforation segment cluster of an unconventional reservoir, which comprises the following steps:
s1: collecting basic parameters in a reservoir logging curve, analyzing the correlation among the basic parameters, and selecting the basic parameters or basic parameter combinations with the correlation smaller than a threshold value as reservoir classification candidate parameters; the base parameters include geomechanical parameters and geomechanical parameters.
In a specific embodiment, the geomechanical parameters include porosity, oil saturation, permeability, and the geomechanical parameters include minimum level principal stress, brittleness index, young's modulus, poisson's ratio. And when the correlation is analyzed, analyzing by adopting a Pearson correlation coefficient analysis method, and selecting the basic parameter of which the correlation coefficient is less than 0.5 as the reservoir classification candidate parameter.
In a specific embodiment, three parameters of porosity multiplied by oil saturation, brittleness index and minimum level principal stress are selected as the reservoir classification candidate parameters.
S2: establishing a single-cluster fracturing production integrated numerical model I on the basis of the average value of each basic parameter, carrying out capacity numerical simulation according to the single-cluster fracturing production integrated numerical model I, analyzing the correlation between the reservoir classification candidate parameters and the capacity according to the capacity numerical simulation result, and selecting the parameter or the parameter combination with the strongest correlation with the capacity from the reservoir classification candidate parameters as the reservoir classification parameters.
In a specific embodiment, when the single-cluster fracturing production integrated numerical model is established, Petrel software is adopted for establishment, an average value of each basic parameter is used as a basic model parameter to establish a geological and geomechanical three-dimensional model, a fracturing module is used for carrying out fracturing simulation under a typical single-cluster liquid injection scale, a numerical simulation module is used for carrying out oil production prediction after pressing, the value of the selected reservoir classification candidate parameter in S1 is modified, and according to the correlation between each reservoir classification candidate parameter and capacity of the numerical simulation result, the parameter or parameter combination with the strongest correlation is selected as the reservoir classification basis.
In a specific embodiment, a pearson correlation coefficient analysis method is also selected to perform correlation analysis between the candidate parameters for reservoir classification and the productivity, and finally the product of the porosity and the oil saturation is selected as the reservoir classification parameter.
S3: and clustering the reservoir classification parameters by adopting a clustering algorithm, classifying drilling reservoirs of all wells according to a clustering result, and acquiring an average value of each basic parameter of each type of reservoir.
In a specific embodiment, a K-means clustering algorithm is selected for clustering. It should be noted that the purpose of clustering is to classify the reservoir, and other clustering methods in the prior art, such as KNN algorithm, K-medoid algorithm, CLARANS algorithm, etc., can also be applied to the present invention.
In a specific embodiment, the product of the porosity and the oil saturation is used as the reservoir classification parameter, and the reservoirs are classified into four types according to the clustering result; wherein the product of the porosity and oil saturation of a class i reservoir is greater than 650; the product of the porosity and the oil saturation of the II reservoir is within the range of (560, 650), the product of the porosity and the oil saturation of the III reservoir is within the range of (440, 560), and the product of the porosity and the oil saturation of the IV reservoir is less than or equal to 440.
S4: and establishing a single-cluster fracturing production integrated numerical model II on the basis of the average value of each basic parameter corresponding to each reservoir type, and performing numerical simulation according to the single-cluster fracturing production integrated numerical model II to obtain 15-year accumulated oil production of single-cluster cracks of each reservoir type under the conditions of different liquid injection scales and different cluster intervals.
In a specific embodiment, the method for establishing the single-cluster fracturing production integrated numerical model II is the same as the method for establishing the single-cluster fracturing production integrated numerical model I, and the difference between the two methods is only that the basic data of the model is different.
S5: dividing the target horizontal well into a plurality of initial small sections, combining the initial small sections according to the similarity of the reservoir classification candidate parameters of the adjacent initial small sections, realizing the division of the fracturing sections, and determining the optimized section length of each fracturing section.
In a specific embodiment, the merging according to the similarity of the candidate parameters of the reservoir classification of the adjacent initial segments specifically includes the following sub-steps:
s51: calculating the average change rate I of the candidate parameters of the reservoir classifications of an initial subsection and an initial subsection adjacent to the left side of the initial subsection, and calculating the average change rate II of the candidate parameters of the reservoir classifications of the initial subsection and an initial subsection adjacent to the right side of the initial subsection;
s52: and judging whether the initial small section is combined with two adjacent initial small sections on the left side and the right side of the initial small section according to the average change rate I and the average change rate II:
when the average change rate I or the average change rate II is less than 20%, the initial small section is merged with the adjacent initial small section with the average change rate of less than 20%;
when the average change rate I and the average change rate II are both smaller than 20%, selecting adjacent initial small sections with smaller average change rates to merge;
s53: taking the merged segments as new segments, taking the average value of the reservoir classification candidate parameters of the merged segments as the value of the reservoir classification candidate parameters of the new segments, and repeating the steps S51-S53 until the algorithm stops or the segment length constraint is reached;
in step S52, if both average rates of change of a certain segment are greater than or equal to 20%, and the segment does not reach the minimum segment length, the segment is merged with an adjacent segment with a smaller average rate of change; and if the two average change rates of a small section are both more than or equal to 20 percent and the section length of the small section is more than or equal to the minimum section length, taking the small section as an independent fracturing section.
In a specific embodiment, every 1 measuring point is taken as a small segment, the average change rate of the three reservoir classification candidate parameters (the product of porosity and oil saturation, brittleness index and minimum level main stress) between the measuring point and the adjacent small segments (namely the 1 st small segment and the 3 rd small segment) is calculated from the 2 nd small segment, if the change rate of the measuring point and the small segments on the two sides is not more than 20%, the adjacent small segments with smaller change rate are merged with the measuring point, and the average value of the three parameters of the small segments after merging is respectively calculated as a new value of each parameter; if the change rate of the small section adjacent to one side is not more than 20%, merging the small section with the side, and taking the average value of three parameters of the merged small section as a new value of each parameter; if the rate of change is greater than 20% from both sides, then no merging is made with the adjacent segments. Considering the feasibility and the economical efficiency of field construction, setting the segment length constraint conditions as the minimum segment length of 40 m and the maximum segment length of 100 m, combining the fracturing segments smaller than 40 m with the adjacent segments with small change rate, and averagely dividing the fracturing segments larger than 100 m to ensure that the length of each segment is smaller than 100 m. This merging process is repeated until the algorithm stops or the segment length constraint is reached, with the results shown in fig. 2.
S6: judging the type of the reservoir to which each fracturing section belongs, calculating the theoretical benefit of each fracturing section under the conditions of different liquid injection scales and different cluster distances according to the 15-year accumulated oil production of the single cluster cracks of each reservoir type under the conditions of different liquid injection scales and different cluster distances obtained in the step S4, and determining the optimized cluster distance and the optimized liquid injection scale of each fracturing section according to the theoretical benefit.
In a specific embodiment, the theoretical benefit is calculated by:
in the formula: p is the single stage theoretical yield, Yuan; n is the number of single well clusters; r is o Profit for crude oil, yuan/ton; q. q of o One cluster of oil production, one ton; e.g. of the type c Cost of fracturing fluid, yuan/fang; v c The single cluster of injection amount is used; e.g. of the type p For proppant cost, yuan/square; v p Is a sheetThe amount of the cluster proppant is used; e.g. of the type s For fracturing service fee, element/segment; s n The number of single well fracturing stages.
In a specific embodiment, taking the well spacing of 150m as an example, simulating the fracturing and production conditions of single-cluster fractures of reservoirs in types I, II and III under the single-cluster injection scales of 300, 400, 500, 600 and 700 squares and the single-cluster intervals of 10, 20 and 30 m to obtain hundreds of groups of simulation results in total, and converting to obtain the final recovery of a single well. The theoretical yield under each condition is calculated according to the final yield of the single well under each condition, and the results are shown in figures 3-5. And taking the injection scale and the cluster spacing corresponding to the condition with the highest theoretical benefit as the optimized cluster spacing and the optimized injection scale, wherein the single-seam 700 square gauge +20 m cluster spacing is selected for the I-type reservoir, the single-seam 600 square gauge +20 m cluster spacing is selected for the II-type reservoir, the single-seam 600 square gauge +30 m cluster spacing is selected for the III-type reservoir, and the optimal single-seam 600 square gauge +30 m cluster spacing is selected for the III-type reservoir, so that the fracturing section with better physical properties can be subjected to encrypted crack distribution and has higher injection strength. Under the assumed condition of homogeneous formation parameters, the length of a section of a type I reservoir is 70 m, and the length of a section of a type II/type III reservoir is 80 m; and considering construction conditions, the injection amount of the single-segment constraint is less than 2500 square.
It should be noted that the physical properties of the IV reservoir are too poor, and the IV reservoir is not constructed in actual fracturing, so the productivity of the IV reservoir is not calculated here.
It should be noted that, in the above embodiment, when determining the optimized cluster spacing and the optimized injection scale of each fracturing segment in steps S4-S6, the yield of a single seam of each type of reservoir is calculated first, and the yields of several seams of the segment are directly converted according to the type of the segment after the segment is divided, so that the production simulation is not run for each segment, and the calculation amount is greatly reduced. However, in practical application, if the reservoir type is segmented, then each segment is modeled according to the corresponding reservoir type, and then the production simulation calculation theoretical yield is performed to optimize the cluster spacing and the injection scale, the method is the same as the method in essence, and the method also belongs to the protection scope of the invention.
S7: and (4) performing perforation cluster equal distribution on each fracturing section according to the optimized cluster spacing, and optimizing the positions of the perforation clusters by using an interior point method.
In a specific embodiment, optimizing the perforation cluster position using the interior point method specifically comprises the sub-steps of: allowing the perforation clusters to move in the segments, searching the minimum position of the minimum horizontal main stress difference in the range of the left and right search threshold values of the initial position of each perforation cluster by using an inner point method, so that the internal stress difference of the segments is smaller than the critical value of the minimum horizontal main stress difference for balanced initiation, and the spacing between the restraint clusters is not smaller than 5m, thereby determining the optimized positions of the perforation clusters; and when the set search threshold value cannot meet the condition that the stress difference in the section is smaller than the minimum level main stress difference critical value of the balanced crack initiation, the search range is enlarged.
In a specific embodiment, the initial search threshold is 5 m. As shown in fig. 6, the local minimum horizontal principal stress is modified, and a single-stage fracturing mechanism simulation is performed, when the difference of the horizontal principal stresses in the initiation section is greater than 2MPa, the perforation cluster at the position with the larger minimum horizontal principal stress is difficult to initiate and balance to initiate fractures, so that the critical value of the difference of the minimum horizontal principal stresses is determined to be 2 MPa. An inner point method in a nonlinear optimization algorithm is utilized to allow perforation clusters to move in a section, a minimum horizontal main stress difference minimum position is searched in a feasible region (the range of 5m around an initial position), the section inner stress difference is smaller than the equilibrium initiation minimum horizontal main stress difference critical value by 2MPa, meanwhile, the perforation cluster interval is prevented from being smaller than 5m, the seam distribution position is further optimized, the intra-section non-equilibrium initiation probability is reduced, the single well production benefit is improved, and the result is shown in figures 7-9.
It should be noted that the interior point method is the prior art, and the specific method is not described herein again.
S8: and establishing a single-section multi-cluster fracturing numerical model by taking the average value of each basic parameter of each corresponding reservoir type of each fracturing section as a basis and combining the optimized perforation cluster positions, and determining the optimized perforation number of each perforation cluster in each fracturing section.
In a specific embodiment, the method for establishing the single-stage multi-cluster fracturing numerical model is the same as the method for establishing the fracturing numerical model in the single-cluster fracturing production integrated numerical model I and the single-cluster fracturing production integrated numerical model II, and the differences are only that the basic data of the models are different and the cluster numbers are different, and meanwhile, the single-stage multi-cluster fracturing numerical model only carries out fracturing simulation and does not need production simulation.
In a specific embodiment, when the optimized perforation number of each perforation cluster in each fracturing section is determined, the initial perforation number is set to be the conventional fracturing perforation number of 8-12, and when the stress difference in the section is the minimum level main stress difference critical value of reservoir equilibrium fracture initiation, the flow-limiting fracturing perforation number when the liquid inlet quantity difference between clusters in the section is less than 30% is simulated, and the flow-limiting fracturing perforation number is the optimized perforation number. In a specific embodiment, the threshold value of the minimum level principal stress difference of the equilibrium initiation of each type of reservoir is 2MPa, and the optimized perforation numbers corresponding to the reservoirs I, II and III are 12, 8 and 8.
In a specific embodiment, taking an unconventional reservoir single horizontal well of an M oil field as an example, the unconventional reservoir fracturing perforation segment cluster parameter combination optimization method of the present invention is adopted to perform a segmented clustering design, and compared with the current fracturing construction scheme of the well, the results are shown in table 1:
table 1 results of comparing the present invention with the current fracturing construction protocol
As can be seen from Table 1, the theoretical yield and the yield of the unconventional reservoir fracturing perforation segment cluster parameter combination scheme obtained by optimization are obviously increased. Compared with the prior art, the invention has remarkable progress.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A parameter combination optimization method for unconventional reservoir fracturing perforation segment clusters is characterized by comprising the following steps:
s1: collecting basic parameters in a reservoir logging curve, analyzing the correlation among the basic parameters, and selecting the basic parameters or basic parameter combinations with the correlation smaller than a threshold value as reservoir classification candidate parameters; the basic parameters comprise geological physical parameters and geomechanical parameters;
s2: establishing a single-cluster fracturing production integrated numerical model I on the basis of the average value of each basic parameter, carrying out capacity numerical simulation according to the single-cluster fracturing production integrated numerical model I, analyzing the correlation between the reservoir classification candidate parameters and the capacity according to the capacity numerical simulation result, and selecting the parameter or the parameter combination with the strongest correlation with the capacity from the reservoir classification candidate parameters as the reservoir classification parameters;
s3: clustering the reservoir classification parameters by adopting a clustering algorithm, classifying drilling reservoirs of all wells according to a clustering result, and obtaining an average value of each basic parameter of each type of reservoir;
s4: establishing a single-cluster fracturing production integrated numerical model II on the basis of the average value of each basic parameter corresponding to each reservoir type, and performing numerical simulation according to the single-cluster fracturing production integrated numerical model II to obtain 15-year accumulated oil production of single-cluster cracks of each reservoir type under the conditions of different liquid injection scales and different cluster intervals;
s5: dividing a target horizontal well into a plurality of initial small sections, combining the initial small sections according to the similarity of the reservoir classification candidate parameters of the adjacent initial small sections, realizing the division of fracturing sections, and determining the optimized section length of each fracturing section;
s6: judging the type of the reservoir to which each fracturing section belongs, calculating theoretical benefits of each fracturing section under the conditions of different liquid injection scales and different cluster intervals according to the 15-year accumulated oil production of the single-cluster cracks of each reservoir type under the conditions of different liquid injection scales and different cluster intervals obtained in the step S4, and determining the optimized cluster intervals and the optimized liquid injection scales of each fracturing section according to the theoretical benefits;
s7: performing perforation cluster equal division placement on each fracturing section according to the optimized cluster spacing, and optimizing the positions of the perforation clusters by using an interior point method;
the optimization of the perforation cluster position by using the interior point method specifically comprises the following sub-steps: allowing the perforation clusters to move in the sections, searching the minimum position of the minimum horizontal main stress difference in the range of left and right searching threshold values of the initial positions of the perforation clusters by using an interior point method, so that the internal stress difference of the sections is smaller than the critical value of the minimum horizontal main stress difference for balanced initiation, and meanwhile, the spacing between the restraint clusters is not smaller than 5m, thereby determining the optimized positions of the perforation clusters; when the set search threshold value cannot meet the condition that the section internal stress difference is smaller than the minimum level main stress difference critical value of the balanced crack initiation, the search range is enlarged;
s8: and establishing a single-section multi-cluster fracturing numerical model by taking the average value of each basic parameter of each corresponding reservoir type of each fracturing section as a basis and combining the optimized perforation cluster positions, and determining the optimized perforation number of each perforation cluster in each fracturing section.
2. The unconventional reservoir fracturing perforation segment cluster parameter combination optimization method of claim 1, wherein in the step S1, the geomechanical parameters comprise porosity, oil saturation and permeability, and the geomechanical parameters comprise minimum level principal stress, brittleness index, young modulus and poisson ratio.
3. The unconventional reservoir fracturing perforation segment cluster parameter combination optimization method of claim 2, wherein in step S1, when analyzing the correlation, a pearson correlation coefficient analysis method is used for analysis, and a basic parameter or a basic parameter combination with a correlation coefficient smaller than 0.5 is selected as a reservoir classification candidate parameter.
4. The unconventional reservoir fracturing perforation segment cluster parameter combination optimization method of claim 3, wherein in step S1, the reservoir classification candidate parameters are product of porosity and oil saturation, brittleness index, minimum level principal stress; in step S2, the reservoir classification parameter is a product of porosity and oil saturation.
5. The unconventional reservoir fracturing perforation segment cluster parameter combination optimization method according to claim 4, wherein in step S3, when the drilling reservoirs of each well are classified according to the clustering result, the reservoirs are classified into four categories; wherein the product of the porosity and oil saturation of a class i reservoir is greater than 650; the product of the porosity and the oil saturation of the II reservoir is within the range of (560, 650), the product of the porosity and the oil saturation of the III reservoir is within the range of (440, 560), and the product of the porosity and the oil saturation of the IV reservoir is less than or equal to 440.
6. The unconventional reservoir fracturing perforation segment cluster parameter combination optimization method of claim 1, wherein in the step S5, the merging according to the similarity of the reservoir classification candidate parameters of the adjacent initial segments specifically comprises the following sub-steps:
s51: calculating the average change rate I of the candidate parameters of the reservoir classifications of an initial subsection and an initial subsection adjacent to the left side of the initial subsection, and calculating the average change rate II of the candidate parameters of the reservoir classifications of the initial subsection and an initial subsection adjacent to the right side of the initial subsection;
s52: judging whether the initial small section is combined with two adjacent initial small sections on the left side and the right side of the initial small section according to the average change rate I and the average change rate II:
when the average change rate I or the average change rate II is less than 20%, the initial small section is merged with the adjacent initial small section with the average change rate less than 20%;
when the average change rate I and the average change rate II are both smaller than 20%, selecting adjacent initial small sections with smaller average change rates to merge;
s53: taking the merged segments as new segments, taking the average value of the reservoir classification candidate parameters of the merged segments as the value of the reservoir classification candidate parameters of the new segments, and repeating the steps S51-S53 until the algorithm stops or the segment length constraint is reached;
in step S52, if both average rates of change of a certain segment are greater than or equal to 20%, and the segment does not reach the minimum segment length, the segment is merged with an adjacent segment with a smaller average rate of change; and if the two average change rates of a small section are both more than or equal to 20 percent and the section length of the small section is more than or equal to the minimum section length, taking the small section as an independent fracturing section.
7. The unconventional reservoir fracturing perforation segment cluster parameter combination optimization method of claim 1, wherein in step S6, the theoretical benefit is calculated by the following formula:
P＝n×(r o ×q o -e c ×V c -e p ×V p )-e s ×S n (1)
in the formula: p is the single stage theoretical yield, Yuan; n is the number of single well clusters; r is o Profit for crude oil, yuan/ton; q. q.s o One cluster of oil production, one ton; e.g. of the type c Cost of fracturing fluid, yuan/fang; v c The single-cluster liquid injection amount is used; e.g. of the type p For proppant cost, yuan/square; v p The dosage of the single-cluster propping agent is shown in the specification; e.g. of the type s For fracturing service fee, element/segment; s n The number of single well fracturing stages.
8. The unconventional reservoir fracturing perforation segment cluster parameter combination optimization method of claim 1, wherein in step S8, when determining the optimized number of perforations of each perforation cluster in each fracturing segment, setting an initial number of perforations, and simulating the number of flow-limiting fracturing perforations when the difference of liquid inflow amounts between each cluster in the segment is less than 30% when the difference of internal stresses in the segment is the minimum level main stress difference critical value of reservoir equilibrium initiation fracture, wherein the number of flow-limiting fracturing perforations is the optimized number of perforations.
9. The unconventional reservoir fracturing perforation segment cluster parameter combination optimization method of claim 8, wherein a conventional fracturing perforation number is adopted when the initial perforation number is set, and the conventional fracturing perforation number is any integer from 8 to 12.
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