CN113434812A - Hydrate trial production target optimization method based on fuzzy comprehensive evaluation - Google Patents
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Abstract
The invention discloses a hydrate exploitation target optimization method based on fuzzy comprehensive evaluation, which is characterized in that a secondary index system of exploitation target influence factors is established in consideration of engineering factors and geological factors; establishing a numerical model for hydrate exploitation, selecting a plurality of continuous numerical values in the value range of each index in a secondary index system to carry out calculation, and obtaining the calculation results of all secondary indexes under different values; then determining the influence trend of the change of each index on the calculation result, and calculating a single-factor evaluation matrix according to the index values of the station to be selected; sequencing the influence degree of each secondary index on the result according to the numerical simulation calculation result, determining the importance degree of the indexes, and calculating the weight of each factor; and the final weight of the secondary indexes is determined by integrating the primary index weight and the secondary index weight, and then the comprehensive evaluation factor of the final station to be selected is obtained by combining the single-factor evaluation matrix, so that the optimal target selection is realized.
Description
Technical Field
The invention relates to the field of hydrate exploitation, in particular to a hydrate trial-production target optimization method based on fuzzy comprehensive evaluation.
Background
In recent years, the international hydrate development situation has gradually entered the pilot mining phase from the exploration phase, and pilot mining has been successfully carried out in several countries so far. The difference of the storage occurrence characteristics of the hydrate in the sea area is obvious, the hydrate in the Daini Atsumi Knoll sea area on the north slope of the south sea gully of Japan tends to be enriched in coarse-grained turbid accumulated sand, and the hydrate-containing sediment in the south sea of China is mostly fine-grained clay-siltstone. The hydrate saturation in the blake sea chest is lower by about 14% and in the gulf of mexico is higher by more than 40% of the sediment pore volume, with an average value of about 67%. The hydrate-containing deposit layer thickness varies from site to site even in the confined drilling zone of south sea, for example 10m for the neurophu SH3 site and 25m for the SH7 site. The above "uneven" geological features constitute typical features of hydrate reserves in specific regions of sea areas, and researches find that the difference of occurrence characteristics of different hydrate reserves has obvious influence on the gas production potential of the hydrate, thereby increasing the difficulty in selecting a hydrate exploitation target.
From the perspective of the hydrate resource pyramid, hydrate extraction is easiest in the medium-coarse sand hydrate reservoir at the top of the pyramid, while the clay silt reservoir at the bottom of the pyramid is most difficult, but the largest amount of hydrate resource is exactly in the clay silt. Although successful exploitation is realized in the clayey silt hydrate, the characteristic difference of the clayey silt hydrate reservoir is large, different water depths, different sediment mechanical properties, different pore seepage characteristics and the like exist, and how to comprehensively consider the factors to select the exploitation target is an important premise for realizing the hydrate exploitation.
The selection of the hydrate exploitation target needs to consider more factors, which mainly include geological factors and engineering factors. From the geological factor perspective, the factors such as the permeability, the porosity, the saturation, the temperature, the pressure and the like of a hydrate reservoir stratum are mainly considered, so that the purpose of maximum exploitation productivity is achieved; the engineering factors are factors such as water depth, drilling safety, construction safety and the like, and the aim of mining construction safety is fulfilled. At the present stage, the influence of some factors on the exploitation is usually concerned with selecting the hydrate exploitation target, for example, a station with high hydrate saturation is usually selected in geological factors, and the seabed gradient is usually concerned in engineering so as to ensure the safety of a wellhead during exploitation. However, there are many factors affecting hydrate exploitation, and considering only a few of them is likely to cause deviation due to subjective factors, and under the combined action of multiple factors, determining the final target with only a few of the factors is also likely to cause misjudgment.
Disclosure of Invention
The invention provides a fuzzy comprehensive evaluation-based method aiming at the problem of hydrate exploitation target selection, which comprehensively considers engineering factors and geological factors by combining numerical simulation results, realizes the evaluation of the exploitation target in a quantitative manner, and avoids the one-sidedness of hydrate exploitation target selection.
The invention is realized by adopting the following technical scheme: a hydrate exploitation target optimization method based on fuzzy comprehensive evaluation comprises the following steps:
s1, establishing a two-stage index system of hydrate mining target influence factors from two aspects of engineering factors and geological factors, determining the value range of each index, and determining the value of each two-stage index of a station to be selected;
s2, establishing a numerical model for hydrate exploitation by adopting a numerical simulation method, selecting a plurality of continuous numerical values in the value range of each index in the secondary index system established in the S1 to carry out calculation, and obtaining the calculation results of all secondary indexes under different values;
s3, according to the numerical simulation calculation result of S2, determining the influence trend of the change of each index on the calculation result, and calculating a single-factor evaluation matrix according to the index values of the station to be selected;
s4, sequencing the influence degree of each secondary index on the result according to the numerical simulation calculation result of S2, determining the importance degree of the indexes, and calculating the weight of each factor;
s5, determining the weight of the primary index according to the importance degree of the primary index, and determining the final weight of the secondary index by integrating the weight of the primary index and the weight of the secondary index calculated in the S4;
and S6, calculating a comprehensive evaluation factor of the final station to be selected by using the final weight of the secondary indexes calculated in the S5 and the single-factor evaluation matrix calculated in the S4, and selecting an optimal target according to the size of the comprehensive evaluation factor.
Further, the step S1 is specifically implemented by the following method:
s11, establishing two primary indexes of geological factors and engineering factors under a pilot mining target;
s12, establishing six secondary indexes of permeability, porosity, hydrate saturation, hydrate layer thickness, reservoir temperature and reservoir pressure under the geological factors; establishing four secondary indexes of water depth, seabed gradient, sediment strength and overburden thickness under the engineering factors;
s13, determining the value range of each index value according to the survey results in the world
S14, determining index values of the geological factors and the engineering factors of a plurality of stations to be selected according to actual survey data
Further, the step S2 includes:
s21, calculating the influence of geological factors on mining production capacity by using a numerical simulation method with capacity as a target;
further, the step S21 includes:
s211, dividing six indexes in the geological factors into a plurality of values from small to large according to the range of each index;
s212, calculating the exploitation capacity of the hydrate by a numerical simulation method under each index value;
s213, drawing a relation curve between the productivity and the index value according to the productivity of a certain index under the different values;
s214, sequencing the influence degrees of all the geological factor indexes from large to small according to the influence degrees of all the indexes on productivity from large to small;
s22, calculating the influence of engineering factors on the mining safety by adopting a numerical simulation method with the mining mechanical stability as a target;
s221, dividing four indexes in the engineering factors into a plurality of values from small to large according to the range of each index;
s222, calculating the stratum deformation degree in the mining process through a numerical simulation method under each index value;
s223, drawing a relation curve of the stratum deformation and an index according to the stratum deformation of the index under a plurality of different values;
s224, sorting the influence degrees of all engineering factor indexes from large to small according to the influence degrees of all the indexes on formation deformation;
further, the step S3 includes:
s31, determining single-factor evaluation matrix of all indexes in geological factors
Further, the step S31 includes:
s311, determining the position of the parameter on the relation curve of the capacity and the index value obtained in S213 according to the definite parameter values of the geological factors of all the stations to be selected in S1;
s312, dividing the yield value corresponding to the parameter on the relation curve of the yield and the index value minus the minimum yield value of the curve by the maximum yield value on the curve minus the minimum yield value on the curve to obtain the single-factor membership degree of the parameter;
s313, the single-factor membership degrees of all the geological factor indexes of all the stations form a geological factor single-factor evaluation matrix
S32, determining single-factor evaluation matrix of all indexes in engineering factors
Further, the S32 includes:
s322, determining the position of the parameter on the relation curve of the stratum deformation and the index value obtained in the S223 according to the parameter values of the engineering factors of all the stations to be selected which are clear in the S1;
s322, dividing the stratum deformation amount corresponding to the parameter on the relation curve of the stratum deformation and the index value minus the minimum stratum deformation amount of the curve by the maximum stratum deformation amount on the curve minus the minimum stratum deformation amount on the curve to obtain the single-factor membership degree of the parameter;
and S323, forming an engineering factor single-factor evaluation matrix by the single-factor membership degrees of all the engineering factor indexes of all the stations.
Further, the step S4 includes:
s41, determining the secondary index weight of the geological factors,
further, the step S41 includes:
s411, sorting according to the importance degree of each geological factor determined in S214, and listing the judgment value between the two factors by adopting a 1-9 scale method
S412. the judgment values of all the factors form a geological factor judgment matrix
S413, calculating the feature vector of the judgment matrix, wherein the feature vector is the weight vector of each geological factor
S414, carrying out consistency check on the geological factor judgment matrix, if the check is passed, carrying out the next step, and if the check is not passed, repeating S411-S413;
s42, determining the secondary index weight of the engineering factor
Further, the step S42 includes:
s421, sorting according to the importance degree of each engineering factor determined in S224, and listing the judgment value between the two factors by adopting a 1-9 scale method
S422, the judgment values of all factors form an engineering factor judgment matrix
S423, calculating the characteristic vector of the engineering factor judgment matrix, wherein the characteristic vector is the weight vector of each engineering factor
S424, carrying out consistency check on the engineering factor judgment matrix, carrying out the next step if the check is passed, and repeating S421-S423 if the check is not passed;
further, the step S5 includes:
s51, determining the weight of two primary indexes, namely engineering factors and geological factors according to a 1-9 scaling method
S52, multiplying the weight of the first-level index of the geological factor by the weight vector of the second-level index under the geological factor to obtain a final weight vector of the second-level index of the geological factor;
s53, multiplying the weight of the engineering factor primary index by the weight vector of the secondary index under the engineering factor to obtain a final weight vector of the engineering factor secondary index;
further, the step S6
S61, multiplying the secondary weight vector of the geological factor calculated in the step S51 by the geological factor single-factor evaluation matrix obtained in the step S313 to obtain an evaluation factor of the geological factor;
s62, multiplying the secondary weight vector of the engineering factor calculated in the S52 by the engineering factor single-factor evaluation matrix obtained in the S323 to obtain an evaluation factor of the engineering factor;
s63, adding the evaluation factors of the geological factors and the engineering factors to obtain a final comprehensive evaluation factor
And S64, the station with the maximum comprehensive evaluation factor is the finally selected target.
Compared with the prior art, the invention has the advantages and positive effects that:
the method based on fuzzy comprehensive evaluation is provided, the factors influencing the selection of the hydrate mining target are divided into two aspects of geological factors and engineering factors, the targets of the two aspects of mining productivity and mining safety are respectively taken as constraints, the calculation result of numerical simulation is combined to be taken as the influence degree and judgment basis of all the factors, the fuzzy comprehensive evaluation method is adopted, the influence of all the influencing factors on the final target selection is quantified, and the one-sidedness in the subjective selection process is avoided.
Drawings
FIG. 1 is a schematic diagram of a two-level evaluation index system constructed according to an embodiment of the present invention;
FIG. 2 is a relationship curve of the influence of geological factors on productivity, which is calculated according to an embodiment of the present invention.
Detailed Description
In order to make the above objects, features and advantages of the present invention more clearly understood, the present invention will be further described with reference to the accompanying drawings and examples. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and thus, the present invention is not limited to the specific embodiments disclosed below.
The embodiment discloses a hydrate exploitation target optimization method based on fuzzy comprehensive evaluation, which comprises the following steps of:
s1, considering two primary indexes of engineering factors and geological factors, establishing a secondary index system of hydrate exploitation target influence factors, determining the value range of each secondary index, and determining the value of each secondary index of a station to be selected;
s2, establishing a numerical model for hydrate exploitation by adopting a numerical simulation method, selecting a plurality of continuous numerical values in the value range of each index in the secondary index system established in the S1 to carry out calculation, and obtaining the calculation results of all secondary indexes under different continuous numerical values;
s3, according to the numerical simulation calculation result of S2, determining the influence trend of the change of each index on the calculation result, and calculating a single-factor evaluation matrix according to the index values of the station to be selected;
s4, sequencing the influence degree of each secondary index on the result according to the numerical simulation calculation result of S2, determining the importance degree of the indexes, and calculating the weight of each factor in the secondary index;
s5, determining the weight of the primary index according to the importance degrees of the two primary indexes of geological factors and engineering factors, and determining the final weight of the secondary index by integrating the weight of the primary index and the weight of the secondary index calculated in the step S4;
and S6, calculating a comprehensive evaluation factor of the final station to be selected by using the final weight of the secondary indexes calculated in the S5 and the single-factor evaluation matrix calculated in the S3, and selecting an optimal target according to the size of the comprehensive evaluation factor.
Specifically, the method comprises the following steps:
the step S1 specifically includes the following steps:
s11, establishing two primary indexes of geological factors and engineering factors under a pilot mining target;
s12, establishing six secondary indexes of permeability, porosity, hydrate saturation, hydrate layer thickness, reservoir temperature and reservoir pressure under the geological factors; four secondary indexes of water depth, seabed gradient, sediment strength and overburden thickness are established under the engineering factors, and are specifically shown in figure 1.
S13, determining the value range of each index value according to the survey result in the world, specifically:
geological factors:
permeability (mD): 0 to 1000;
porosity (decimal fraction): 0 to 1;
hydrate saturation (decimal): 0 to 1;
hydrate layer thickness (m):0 to 100 parts;
reservoir pressure (MPa): 0 to 30 parts by weight;
reservoir temperature (K): 13273.15-290
Engineering factors:
depth of water (m) of 0 to 2000
Sea floor slope (°):0 to 10
Reservoir strength (kPa) of 0 to 500
The thickness (m) of the upper coating layer is 0-500
S14, determining index values of the geological factors and the engineering factors of the 4 stations to be selected according to actual survey data
For example, according to the survey on the voyage of natural gas hydrate drilling in the sea area of the Shenhu on the land slope in the north of the south sea, the reservoir geological factor data of four typical stations (XX01 station, XX02 station, XX03 station and XX04 station) are as follows:
XX01 station: the average permeability was 0.22mD, the average effective porosity was 34.5%, the average hydrate saturation was 22.9%, the hydrate reservoir thickness was 78.36m, the average pressure was 15.45MPa, and the average temperature was 14.73 ℃. Engineering factors are as follows: the water depth is 1309.95m, the seabed gradient is 4 degrees, the soil strength is 180kPa, and the thickness of the overburden is 113 m.
XX02 station: the average permeability was 0.315mD, the average effective porosity was 33.2%, the average hydrate saturation was 19.4%, the hydrate reservoir thickness was 43.13m, the average pressure was 15.38MPa, and the average temperature was 14.4 ℃. Engineering factors are as follows: the water depth is 1249.30m, the seabed gradient is 3.2 degrees, the soil strength is 170kPa, and the thickness of the overburden is 210 m.
XX03 station: the average permeability was 100mD, the average effective porosity was 56.7%, the average hydrate saturation was 30.5%, the hydrate reservoir thickness was 11.56m, the average pressure was 14.5MPa, and the average temperature was 11.22 ℃. Engineering factors are as follows: the water depth is 1285.41m, the seabed gradient is 3.8 degrees, the soil strength is 160kPa, and the thickness of the overburden is 144 m.
XX04 station: the average permeability was 5.5mD, the average effective porosity was 30%, the average hydrate saturation was 46.2%, the hydrate reservoir thickness was 17.59m, the average pressure was 14.6MPa, and the average temperature was 9.7 ℃. Engineering factors are as follows: the water depth is 1273.80m, the bottom gradient is 1.6 degrees, the soil strength is 200kPa, and the thickness of the upper coating is 135 m.
The step S2 is specifically implemented by the following steps:
s21, calculating the influence of geological factors on mining productivity by adopting a numerical simulation method with productivity as a target, wherein the method comprises the following steps:
s211, dividing six indexes in geological factors into five index values (5 are taken as an example in the embodiment) from small to large according to the range of each index, namely, dividing the permeability into 0.1, 10, 100, 500 and 1000; the hydrate saturation degree is divided into: 0.1, 0.2, 0.4, 0.6, 0.8; the porosity is 0.3, 0.4, 0.5, 0.6 and 0.7; the hydrate layer has the following thickness: 10. 20, 30, 40, 50; reservoir pressure is divided into: 9. 10, 15, 20, 30; the reservoir temperature is divided into: 282.5, 285, 288, 291, 294;
s212, calculating the exploitation capacity of the hydrate by a numerical simulation method under each index value;
s213, determining the influence rule of a single secondary index on the productivity according to the influence rule of the single secondary index on the productivity from large to small under different index value values, and drawing a relation curve of the productivity and the index values, as shown in FIG. 2;
s214, sequencing the influence degrees of all the geological factor indexes from large to small according to the influence degrees of all the indexes on productivity from large to small; according to the calculation result, the sequence of all factors is permeability, porosity, hydrate saturation, hydrate occurrence temperature, hydrate occurrence pressure and hydrate layer thickness. (ii) a
S22, calculating the influence of the engineering factors on the mining safety by adopting a numerical simulation method with the mining mechanical stability as a target, wherein the method comprises the following steps:
s221, dividing four indexes in the engineering factors into five values (5 are taken as an example in the embodiment) from small to large according to the range of each secondary index under each engineering factor;
s222, calculating the stratum deformation degree in the mining process through a numerical simulation method under each index value;
s223, determining the influence rule of a single index on the mining safety according to the stratum deformation of the index under different values from large to small, and drawing a relation curve of the stratum deformation and the index value;
s224, according to the influence degrees of all the indexes on stratum deformation, the influence degrees of all the engineering factor indexes are ranked from large to small, and according to the calculation result, the influence degrees of all the engineering factors on the mining safety are sequentially from large to small: the seabed gradient is larger than the overlying layer thickness, the reservoir strength is larger than the water depth.
The step S3 includes:
s31, determining a single-factor evaluation matrix of all secondary indexes in the geological factors, wherein the single-factor evaluation matrix comprises the following steps:
s311, determining the position of the parameter on the relation curve of the capacity and the index value obtained in S213 according to the definite parameter values of the geological factors of all the stations to be selected in S1;
s312, dividing the yield value corresponding to the parameter on the relation curve of the yield and the index value minus the minimum yield value of the curve by the maximum yield value on the curve minus the minimum yield value on the curve to obtain the single-factor membership degree of the parameter; taking the permeability in fig. 1 as an example, the relationship between the permeability and the productivity is monotonically increasing, i.e., the greater the permeability, the higher the productivity. The permeability index has a value range of 0-1000 mD, and in this example, permeability parameters of four stations are 0.22mD, 0.315mD, 100mD and 5.5mD, respectively, and the membership degree of the normalized permeability obtained by dividing each permeability value by 1000 is as follows: 0.000220.0003150.10.0055, and the calculation of other factors is analogized.
And S313, forming a geological factor single-factor evaluation matrix by the single-factor membership degrees of all geological factor indexes of all stations. The single-factor evaluation matrix of the porosity, the permeability, the hydrate saturation, the reservoir thickness, the reservoir temperature and the reservoir pressure of the four stations is obtained by calculation and is as follows:
s32, determining a single-factor evaluation matrix of all indexes in the engineering factors, wherein the single-factor evaluation matrix comprises the following steps:
s321, determining the position of the parameter on the relation curve of the stratum deformation and the index value obtained in the S223 according to the parameter values of the engineering factors of all the stations to be selected, which are determined in the S1;
s322, dividing the stratum deformation amount corresponding to the parameter on the relation curve of the stratum deformation and the index value minus the minimum stratum deformation amount of the curve by the maximum stratum deformation amount on the curve minus the minimum stratum deformation amount on the curve to obtain the single-factor membership degree of the parameter;
s323, forming an engineering factor single-factor evaluation matrix by the single-factor membership degrees of all the engineering factor indexes of all the stations;
according to the correlation between the engineering parameters and the mining difficulty, the membership degree is calculated in a simple monotonous linear mode, and the single-factor evaluation matrix of the four station engineering factors is obtained as follows:
the step S4 specifically includes the following steps:
s41, determining the secondary index weight of the geological factor, which comprises the following steps:
s411, sorting according to the importance degree of each geological factor determined in S214, listing a judgment value between two factors by adopting a 1-9 scale method, wherein the sorting of the influence degree of each factor determined in S214 is as follows: permeability > porosity > hydrate saturation > hydrate occurrence temperature > hydrate occurrence pressure > hydrate layer thickness, values of each texture factor are taken according to values of a scaling method shown in table 1, and taking permeability and hydrate layer thickness as an example, since the influence degree of permeability is the largest and the influence degree of hydrate layer thickness is the smallest, permeability is more important than hydrate layer thickness, so that the scaling value is 9, and the scaling among other factors is analogized.
TABLE 11-9 Scale Table
S412. the judgment values of all the factors form a geological factor judgment matrix as shown in Table 2
| Influencing factor | Permeability rate of penetration | Porosity of | Degree of saturation | Reservoir thickness | Pressure of | Temperature of | Weight of |
| Permeability rate of penetration | 1 | 4 | 4 | 7 | 6 | 5 | 0.4431 |
| Porosity of | 1/4 | 1 | 3 | 6 | 5 | 4 | 0.2593 |
| Degree of saturation | 1/4 | 1/3 | 1 | 4 | 3 | 2 | 0.1285 |
| Reservoir thickness | 1/7 | 1/6 | 1/4 | 1 | 1/2 | 1/3 | 0.0362 |
| Initial pressure | 1/6 | 1/5 | 1/3 | 2 | 1 | 1/2 | 0.0525 |
| Initial temperature | 1/5 | 1/4 | 1/2 | 3 | 2 | 1 | 0.0804 |
S413, calculating a feature vector of the judgment matrix, wherein the feature vector is a weight vector of each geological factor;
let the decision matrix p be (p)ij)n×nDetermining the product of the elements of each row of the matrix, wherein
② calculating MiRoot of cubic (n times)
③ opposite vector w ═ w1,w2,…wn]TNormalization, i.e.
Then w is ═ w1,w2,…wn]TIs the feature vector found.
Fourthly, calculating the maximum characteristic root lambda of the judgment matrixmax，
In the formula (pw)iThe ith element of pw.
The calculation results are shown in the last column of table 2.
S414, carrying out consistency check on the geological factor judgment matrix, if the check is passed, carrying out the next step, and if the check is not passed, repeating S411-S413; the inspection steps are as follows:
defining CI as the consistency index of the judgment matrix P,
and introducing a random consistency index RI.
Let CR be the consistency ratio of the judgment matrix P,
and when CR is less than 0.1, the judgment matrix is considered to have satisfactory consistency, otherwise, the judgment matrix is adjusted to have satisfactory consistency.
And when n is equal to 1 and 2, the positive and negative matrixes of order 1 and 2 with RI equal to 0 always have a consistent matrix without judgment.
S42, determining the secondary index weight of the engineering factor, comprising the following steps:
and S421, sorting according to the importance degree of each engineering factor determined in S224, and listing a judgment value between the two factors by adopting a 1-9 scale method. The importance ranking of each engineering factor determined in S224 is: the sea bottom gradient is more than the overlying layer thickness and more than the reservoir strength and more than the water depth, so that the sea bottom gradient is more important than the water depth, the scale value is 9, and the scale values of other factors are analogized.
S422, the judgment values of all the factors form an engineering factor judgment matrix as shown in Table 3.
TABLE 3 engineering factor weight calculation judgment matrix Table
| Influencing factor | Depth of water | Grade of sea floor | Reservoir strength | Thickness of overlying layer | Weight of |
| Depth of water | 1 | 1/7 | 1/3 | 1/3 | 0.0641 |
| Grade of sea floor | 7 | 1 | 5 | 3 | 0.577 |
| Reservoir strength | 3 | 1/5 | 1 | 1/3 | 0.1207 |
| Thickness of overlying layer | 3 | 1/3 | 3 | 1 | 0.2375 |
S423, calculating the characteristic vector of the engineering factor judgment matrix, wherein the characteristic vector is the weight vector of each engineering factor, the calculation steps are the same as S413, and the calculation results are shown in the last column of the table 3
S424, carrying out consistency check on the engineering factor judgment matrix, carrying out the next step if the check is passed, and repeating S421-S423 if the check is not passed; the consistency checking step is the same as S414.
The step S5 includes:
s51, determining the weight of two primary indexes, namely engineering factors and geological factors, according to a 1-9 scaling method. The engineering and geological factors are of equal importance, so the scale value is taken to be 1, as shown in table 4.
TABLE 4 calculation and judgment matrix table for two main weights of engineering factors and geological factors
| Influencing factor | Engineering of | Geological factors | Weight of |
| Engineering factor | 1 | 1 | 0.5 |
| Geological factors | 1 | 1 | 0.5 |
S52, multiplying the weight of the first-level index of the geological factor by the weight vector of the second-level index under the geological factor to obtain a final weight vector of the second-level index of the geological factor, as shown in Table 5:
TABLE 5 Final weight vector of geological factor Secondary indices
And S53, multiplying the weight of the primary index of the engineering factor by the weight vector of the secondary index under the engineering factor to obtain a final weight vector of the secondary index of the engineering factor, as shown in Table 5.
The step S6 specifically includes the following steps:
s61, multiplying the secondary weight vector of the geological factor calculated in S51 by the geological factor single-factor evaluation matrix obtained in S313 to obtain an evaluation factor of the geological factor,
s62, multiplying the secondary weight vector of the engineering factor calculated in the S52 by the engineering factor single-factor evaluation matrix obtained in the S323 to obtain an evaluation factor of the engineering factor;
s63, adding the evaluation factors of the geological factors and the evaluation factors of the engineering factors to obtain a final comprehensive evaluation factor, wherein the evaluation factors of the four stations are B [0.232,0.18,0.217 and 0.212]
S64, the station with the maximum comprehensive evaluation factor is the finally selected target, and the XX01 is the preferred target due to the fact that the comprehensive evaluation factor of the XX01 station is the maximum.
The above description is only a preferred embodiment of the present invention, and not intended to limit the present invention in other forms, and any person skilled in the art may apply the above modifications or changes to the equivalent embodiments with equivalent changes, without departing from the technical spirit of the present invention, and any simple modification, equivalent change and change made to the above embodiments according to the technical spirit of the present invention still belong to the protection scope of the technical spirit of the present invention.
Claims (7)
1. The hydrate pilot mining target optimization method based on fuzzy comprehensive evaluation is characterized by comprising the following steps of:
s1, considering two primary indexes of engineering factors and geological factors, establishing a secondary index system of hydrate exploitation target influence factors, determining the value range of each secondary index, and determining the value of each secondary index of a station to be selected;
s2, establishing a numerical model for hydrate exploitation, selecting a plurality of continuous numerical values in the value range of each index in the secondary index system established in S1 to carry out calculation, and obtaining calculation results of all secondary indexes under different values;
s3, according to the numerical simulation calculation result of S2, determining the influence trend of the change of each secondary index on the calculation result, and calculating a single-factor evaluation matrix according to the index values of the station to be selected;
s4, according to the numerical simulation calculation result of S2, the influence degree of each secondary index on the result is sequenced, the importance degree of the secondary indexes is determined, and the weight of the secondary indexes is obtained through calculation;
s5, determining the weight of the first-level index according to the importance degree of the two first-level indexes, and determining the final weight of the second-level index by integrating the weight of the first-level index and the weight of the second-level index calculated in the S4;
and S6, obtaining a comprehensive evaluation factor of the final station to be selected by using the final weight of the secondary indexes calculated in the S5 and the single-factor evaluation matrix calculated in the S3, and selecting the optimal station according to the size of the comprehensive evaluation factor.
2. The hydrate pilot mining target optimization method based on fuzzy comprehensive evaluation according to claim 1, characterized in that: the step S1 is specifically implemented in the following manner:
s11, establishing two primary indexes of geological factors and engineering factors under a pilot mining target;
s12, establishing six secondary indexes of permeability, porosity, hydrate saturation, hydrate layer thickness, reservoir temperature and reservoir pressure under the geological factors; establishing four secondary indexes of water depth, seabed gradient, sediment strength and overburden thickness under the engineering factors;
s13, determining the value range of each corresponding index value according to the existing investigation result:
permeability: 0-1000 mD; porosity: 0 to 1; saturation degree of hydrate: 0 to 1; hydrate layer thickness (m): 0-100 m; reservoir pressure: 0 to 30 MPa; reservoir temperature: 13273.15K to 290K;
the water depth is 0-2000 m; sea floor slope: 0 to 10 degrees; the strength of the reservoir is 0-500 kPa; the thickness of the upper coating is 0-500 m;
s14, determining index values of geological factors and engineering factors of the plurality of stations to be selected according to actual survey data.
3. The hydrate pilot mining target optimization method based on fuzzy comprehensive evaluation according to claim 2, characterized in that: the step S2 includes:
s21, calculating the influence of geological factors on mining productivity by adopting a numerical simulation method with productivity as a target, wherein the method comprises the following steps:
s211, dividing six secondary indexes in the geological factors into a plurality of corresponding index values from small to large according to the range of each secondary index under the geological factors;
s212, calculating the exploitation capacity of the hydrate by a numerical simulation method under each index value;
s213, determining the influence rule of a single secondary index on the productivity according to the influence rule of the capacity of the secondary index under different index values from large to small, and drawing a relation curve of the capacity and the corresponding index value;
s214, sequencing the influence degrees of the secondary indexes of all geological factors from large to small according to the influence degrees of all the secondary indexes on productivity from large to small;
s22, calculating the influence of the engineering factors on the mining safety by adopting a numerical simulation method with the mining mechanical stability as a target, wherein the method comprises the following steps:
s221, dividing four secondary indexes in the engineering factors into a plurality of corresponding index values from small to large according to the range of each secondary index under each engineering factor;
s222, calculating the stratum deformation degree in the mining process through a numerical simulation method under each secondary index value;
s223, determining the influence rule of a single secondary index on the mining safety according to the stratum deformation of the secondary index under different index values from large to small, and drawing a relation curve of the stratum deformation and the corresponding index value;
s224, according to the influence degree of all the secondary indexes on the stratum deformation, the influence degrees of all the engineering factor indexes are ranked from large to small.
4. The hydrate pilot mining target optimization method based on fuzzy comprehensive evaluation according to claim 3, characterized in that: the step S3 includes:
s31, determining a single-factor evaluation matrix of all secondary indexes in the geological factors, wherein the single-factor evaluation matrix comprises the following steps:
s311, according to the parameter values of the secondary indexes of all the stations to be selected, which are determined in S1, determining the position of the parameter on the relation curve of the capacity and the index value, which is obtained in S213;
s312, dividing the productivity value corresponding to the parameter on the relation curve of the productivity and the index value minus the minimum yield value of the curve by the maximum yield value on the curve minus the minimum yield value on the curve to obtain the single-factor membership degree of the parameter;
s313, forming a geological factor single-factor evaluation matrix by the single-factor membership degrees of all geological factor indexes of all stations;
s32, determining a single-factor evaluation matrix of all indexes in the engineering factors, wherein the single-factor evaluation matrix comprises the following steps:
s321, determining the position of the parameter on the relation curve of the stratum deformation and the index value obtained in the S223 according to the parameter values of the engineering factors of all the stations to be selected, which are determined in the S1;
s322, dividing (the minimum formation deformation of the curve is subtracted from the formation deformation corresponding to the parameter on the relation curve of the formation deformation and the index value) by (the minimum formation deformation on the curve is subtracted from the maximum formation deformation on the curve), and obtaining the single-factor membership degree of the parameter;
and S323, forming an engineering factor single-factor evaluation matrix by the single-factor membership degrees of all the engineering factor indexes of all the stations.
5. The hydrate pilot mining target optimization method based on fuzzy comprehensive evaluation according to claim 4, characterized in that: the step 4 comprises the following steps:
s41, determining the secondary index weight of the geological factor, which comprises the following steps:
s411, sorting according to the importance degree of each geological factor determined in S214, and listing a judgment value between the two factors by adopting a 1-9 scale method;
s412, forming a geological factor judgment matrix by the judgment values of all factors;
s413, calculating a feature vector of the judgment matrix, wherein the feature vector is a weight vector of each geological factor;
s414, carrying out consistency check on the geological factor judgment matrix, if the check is passed, carrying out the next step, and if the check is not passed, repeating S411-S413;
s42, determining the secondary index weight of the engineering factor, comprising the following steps:
s421, sorting according to the importance degree of each engineering factor determined in S224, and listing a judgment value between the two factors by adopting a 1-9 scale method;
s422, the judgment values of all the factors form an engineering factor judgment matrix;
s423, calculating a characteristic vector of the engineering factor judgment matrix, wherein the characteristic vector is a weight vector of each engineering factor;
and S424, carrying out consistency check on the engineering factor judgment matrix, carrying out the next step if the check is passed, and repeating S421-S423 if the check is not passed.
6. The hydrate pilot mining target optimization method based on fuzzy comprehensive evaluation according to claim 5, characterized in that: the step 5 comprises the following steps:
s51, determining the weights of two primary indexes, namely engineering factors and geological factors according to a 1-9 scaling method, wherein the scaling values of the primary indexes are 1;
s52, multiplying the weight of the first-level index of the geological factor by the weight vector of the second-level index under the geological factor to obtain a final weight vector of the second-level index of the geological factor;
and S53, multiplying the weight of the engineering factor primary index by the weight vector of the secondary index under the engineering factor to obtain the final weight vector of the engineering factor secondary index.
7. The hydrate pilot mining target optimization method based on fuzzy comprehensive evaluation according to claim 6, characterized in that: the step 6 comprises the following steps:
s61, multiplying the secondary weight vector of the geological factor calculated in the step S51 by the geological factor single-factor evaluation matrix obtained in the step S313 to obtain an evaluation factor of the geological factor;
s62, multiplying the secondary weight vector of the engineering factor calculated in the S52 by the engineering factor single-factor evaluation matrix obtained in the S323 to obtain an evaluation factor of the engineering factor;
s63, adding the evaluation factors of the geological factors and the engineering factors to obtain a final comprehensive evaluation factor;
and S64, the station with the maximum comprehensive evaluation factor is the finally selected target.
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