CN111199107A - Novel evaluation method of deltaic acid sandstone traps - Google Patents
Novel evaluation method of deltaic acid sandstone traps Download PDFInfo
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Abstract
The invention provides a new evaluation method of delta-facies sandstone traps, which comprises the following steps: step 1, preprocessing data, and sorting single evaluation parameters of each trap; step 2, sorting all the data obtained in the step 1 and establishing a matrix A; step 3, carrying out the normalization of the trap parameters; step 4, calculating a correlation coefficient; step 5, calculating entropy values of all trap parameters
(ii) a Step 6, calculating the weight W of each parameter; and 7, sequencing the trap scores in a descending order to finish the quantitative evaluation of the traps. The novel delta facies sandstone trapping evaluation method solves the practical problem that the oil content of oil gas is difficult to quantitatively compare, provides a theoretical basis for scientifically evaluating the oil gas trapping, can quantitatively evaluate the trapping, and can more intuitively and scientifically reflect the quality of the trapping。
Description
Technical Field
The invention relates to the field of petroleum and natural gas exploration, in particular to a novel delta facies sandstone entrapment evaluation method.
Background
In geology, rocks capable of producing oil, gas or water are called reservoirs, and clastic rocks or carbonates formed in sedimentary action or formed in diagenesis and metagenesis processes and having good porosity and permeability inside are limited by impermeable rock layers in the upward-inclined direction or around to form geological aggregates called traps. Traps are important sites for oil and gas gathering, and oil and gas-containing reservoirs inside the traps are also direct targets of oil and gas exploration and development. The starting point of the trap research is to evaluate traps according with actual geological facts, and the quality of trap evaluation results directly influences economic benefits of geological exploration and development.
At present, a plurality of methods for evaluating trap at home and abroad exist, but because the uncertainty factors of the oil-gas exploration process on geology are numerous, most of the methods are qualitative or semi-quantitative evaluation. The single geological condition evaluation of geological evaluation comprises five categories of trap, reservoir, oil source, preservation and matching history, and relates to more than 30 geological parameters. At present, the research focus is mainly to finally obtain the comprehensive evaluation parameters of each trap by various mathematical methods, and the applied mathematical methods are various, such as a multilayer comprehensive evaluation method, a fuzzy mathematical method, a weighted average method, an expert scoring method, an artificial neural network method and a gray correlation method.
In the application No.: 201811123403.X, which relates to a trap evaluation method of a heterogeneous thin sandstone interbed reservoir, comprises the following steps: (a) combining logging data, well drilling and logging data, coring data and seismic inversion data to perform stratum contrast division and sediment environment research; meanwhile, acquiring hydrocarbon source rock evolution data through geochemical data; (b) combining stratum contrast division, deposition environment research and hydrocarbon source rock evolution data to obtain reservoir characteristics; (c) according to the seismic data, sequentially carrying out seismic geological comprehensive calibration, fracture interpretation and development period time analysis, structure horizon tracking, structure evolution analysis and structure trap analysis; (d) performing oil and gas reservoir formation research by combining the reservoir characteristics according to the result of the structural evolution analysis; (e) from the results of the hydrocarbon reservoir studies, final favorable trap evaluations were performed in conjunction with the structural trap analysis. The application provides a calculation process of trap parameters, but the parameters are not quantized, so that the advantages and the disadvantages of each trap cannot be compared in a numerical expansion mode, and the advantages and the disadvantages cannot be expressed visually.
In the application No.: 201910527350.6, relates to a method for evaluating encirclement, which comprises the following steps: (1) calculating a weight coefficient of the corresponding sub-factor according to the initial value of the sub-factor; (2) calculating the weight coefficient of the corresponding parent factor according to the weight coefficients of all the child factors belonging to the same parent factor; (3) calculating a correction value corresponding to the trap in the geological risk evaluation model according to the weight coefficients of all the father factors; (4) for any trap, if the correction value of the trap in the geological risk evaluation model is larger than a set threshold value, determining to mine the trap; otherwise, it is determined not to exploit the trap. The determination of the weight value in the application is completed based on a single parameter and a single type of parameter, the parameters need a large number of traps to determine the range of the pollution zone in the implementation process, in the example, 225 traps exist, but if the drilling rate is low and the traps are low in the initial exploration period, the problem that the range of the pollution zone is difficult to determine and the weight value of the parameter is inaccurate occurs. In addition, the parameter of the sedimentary phase is not used in the method, and the parameter of the sedimentary phase is very important for evaluating the Delta trap.
Therefore, a novel method for evaluating the sandstone traps in the delta facies is invented, and the technical problems are solved.
Disclosure of Invention
The invention aims to provide a novel method for evaluating deltaic sandstone traps in an exploration phase, wherein the new method is used for evaluating the deltaic sandstone traps, and the exploration direction is optimized so as to obtain greater economic benefit.
The object of the invention can be achieved by the following technical measures: the novel method for evaluating the deltaic sandstone traps comprises the following steps: step 1, preprocessing data, and sorting single evaluation parameters of each trap; step 2, sorting all the data obtained in the step 1 and establishing a matrix A; step 3, carrying out the normalization of the trap parameters; step 4, calculating a correlation coefficient; step 5, calculating the entropy value E of each trap parameter; step 6, calculating the weight W of each parameter; and 7, sequencing the trap scores in a descending order to finish the quantitative evaluation of the traps.
The object of the invention can also be achieved by the following technical measures:
in step 1, geometric scale parameters, reservoir condition parameters and transportation storage condition parameters are obtained.
In step 1, acquiring geometric scale parameters including trap area, high point burial depth and trap amplitude; respectively measuring the actual area of each trap, wherein the unit is square kilometer; measuring the embedded depth of the trap high point in unit meter; the trap amplitude is measured in meters.
In the step 1, acquiring reservoir condition parameters including reservoir sedimentary microfacies, reservoir lithology, average porosity, average permeability and reservoir thickness; identifying the deposition micro type of a trap inner reservoir, introducing a Delta system deposition micro-phase concept, assigning values to each trap deposition micro-phase concept to form five micro-phases of the reservoir, wherein the micro-phase assignment of the Delta plain river sand dam is 3, the micro-phase assignment of the Delta plain abandoned river channel is 2, the micro-phase assignment of the leading edge underwater diversion river channel is 5, the micro-phase assignment of the leading edge estuary dam is 4, and the micro-phase assignment of the leading edge mat-shaped sand is 1; reservoir lithology is divided into fine sandstone value 1, medium sandstone value 2 and coarse sandstone value 3 according to well-drilling logging information; the average porosity of the reservoir is calculated by actually measuring the porosity or calculating the porosity through lithology, and the unit is dimensionless; the average permeability of the reservoir is also obtained by actually measuring the permeability of the core or calculating the permeability, and the unit is square micron.
In the step 1, obtaining transportation storage condition parameters including circle source distance and cover layer thickness; the circle source distance is the closest linear distance between the measured oil source fault and the trap, and the unit is meter; the thickness of the cover layer is calculated by using the drilling data and the seismic data, and the unit of the thickness of the cover layer is meter.
In step 2, the matrix a is established as:
wherein a isijEnclosing the jth evaluation parameter for the ith; i represents the serial number of the ith trap to be researched, and m is the total number of the traps to be researched; j represents the sequence number of the jth evaluation parameter, and n is the total number of the parameters participating in trap evaluation;
when the parameter is a benefit type parameter, i.e. parameter aijThe larger the numerical value of (2), the better the trap is, the maximum value is selected as an optimal value, and the trap area, the burial depth high point, the trap amplitude, the reservoir sedimentary microfacies, the reservoir lithology, the average porosity, the average permeability and the cover thickness parameters belong to the condition; when the parameter is a cost-type parameter, i.e. parameter aijThe larger the numerical value is, the worse the trap is, the minimum value is selected as the optimal value, and the distance of the trap source belongs to the condition; setting an optimal parameter array A0：
A0＝(a01,a02,...,a0j,...a0n) (formula 2)
In the formula, a0jRepresents the jth parameterN is the total number of parameters participating in the evaluation of traps.
In step 3, the benefit type parameters are normalized using equation 3:
the cost-type parameters are normalized by equation 4:
in the formula, aijEnclosing the jth evaluation parameter for the ith; a isjminRepresents the minimum value of the jth parameter in all traps; a isjmaxRepresents the maximum value of the jth parameter in all traps; a'ijRepresenting the normalized parameter value of the jth evaluation parameter value of the ith trap;
the normalized value is recorded as a'ijThese normalized parameters are then considered to be a single score for each trap; in the evaluation parameters of the trap, sorting the trap evaluation single parameter set to establish a parameter matrix A ', a'ijFor the normalized ith circled jth evaluation parameter:
in formula (II), a'ijRepresenting the normalized parameter value of the jth evaluation parameter value of the ith trap; i represents the serial number of the ith trap to be researched, and m is the total number of the traps to be researched; j represents the j-th evaluation parameter number, and n is the total number of parameters participating in trap evaluation.
In step 4, using the idea of grey correlation degree, the normalized data and the optimal parameter value are used to calculate the correlation coefficient by using equation 6, and finally ξijAnd (3) the correlation coefficient of the jth parameter representing the ith trap and the optimal value of the parameter forms a matrix B:
a 'in the formula'ijThe normalized parameter value of the jth evaluation parameter value representing the ith trap, a0jRepresents the optimal value of the jth evaluation parameter; rho is a resolution coefficient, and rho belongs to [0,1 ]]The smaller the value is, the larger the difference between the representative correlation coefficients is, and the stronger the resolution capability is; i represents the serial number of the ith trap to be researched, and m is the total number of the traps to be researched; j represents the sequence number of the jth evaluation parameter, and n is the total number of the parameters participating in trap evaluation;
in the formula, i represents the serial number of the ith trap to be researched, and m is the total number of the traps to be researched; j represents the j-th evaluation parameter number, and n is the total number of parameters participating in trap evaluation.
In step 5, the specific gravity of the single parameter in the trap evaluation, P, is calculated using equation 8ijCalculating the entropy value E of each trap parameter for the proportion of the jth parameter in the ith trap by using a formula 9:
in formula (II), a'ijRepresenting the normalized parameter value of the jth evaluation parameter value of the ith trap; k is the adjustment coefficient, and k is the adjustment coefficient,
where lnm is the natural logarithm of m. m is the total number of traps to be studied; ejRepresenting the entropy value of the jth parameter; n is the total number of evaluation parameters participating in the evaluation of traps.
In step 6, comprehensive parameter evaluation is performed, an entropy weight method is used to determine a weight value according to an entropy value E provided by each parameter for the trap evaluation, and an entropy weight W of each parameter is calculated by using formula 10:
in the formula, WjAnd (3) representing the entropy weight of the jth parameter, wherein m is the total number of traps to be researched, and n is the total number of evaluation parameters participating in trap evaluation.
In step 7, the formula 11 is used to calculate the association relationship reflecting each evaluation trap and the optimal value of the parameter, called the association degree R, which is the comprehensive score of each trap, and the trap scores are arranged in descending order to complete the quantitative evaluation of the traps:
wherein R isjRepresents WjWeight value of the jth parameter to be analyzed, ξ, determined by entropy weight methodijAnd representing a correlation coefficient between the jth parameter and the optimal parameter in the ith trap, wherein n is the total number of the evaluation parameters participating in the evaluation of the traps.
According to the novel delta sandstone trap evaluation method, qualitative reservoir description or quantitative description of a single parameter is abandoned and a comprehensive judgment parameter is determined aiming at the delta sandstone trap, so that the evaluation of the delta sandstone trap is more scientific. The method solves the practical problem that the oiliness of the oil gas is difficult to quantitatively compare, and provides a theoretical basis for scientifically evaluating the oil gas trapping. And taking the optimal value of each parameter in the trap as a reference value, calculating by using an objective and high-precision algorithm such as an entropy weight method to obtain a weight value of the evaluation parameter of the trap, calculating the association degree of the evaluation parameter of each trap and the reference value by using a gray association entropy weight method, taking the association degree as a comprehensive evaluation parameter, and quantitatively evaluating the quality of the trap.
Drawings
FIG. 1 is a flow chart of one embodiment of the novel method of evaluating cold sandstone traps of the present invention;
FIG. 2 is a diagram illustrating a descending order of final trap scores according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
The delta sandstone trap belongs to a lithologic trap, and the lithologic trap refers to a geological composition which is formed in a deposition process due to the change of the lithologic property of the delta, has a sandstone reservoir with good porosity and permeability inside and is surrounded by mudstone with poor permeability on the periphery. And finally, obtaining comprehensive parameters capable of evaluating the deltaic acid sandstone reservoir through calculation, and quantifying evaluation of the trap by the method, wherein the quantitative evaluation can reflect the quality of the trap more intuitively and scientifically. The three aspects of the geometric scale of the trap, the reservoir conditions and the transportation and storage are evaluated. The technical scheme is as follows:
as shown in FIG. 1, FIG. 1 is a flow chart of the novel method for evaluating delta phase sandstone traps of the present invention.
And 101, preprocessing data and sorting single evaluation parameters of each trap.
Step 1.1: and obtaining geometric scale parameters including trap area, high point burial depth and trap amplitude. Respectively measuring the actual area of each trap, wherein the unit is square kilometer; measuring the embedded depth of the trap high point in unit meter; the trap amplitude is measured in meters.
Step 1.2: and acquiring reservoir condition parameters, wherein the reservoir condition parameters comprise reservoir sedimentary microfacies, reservoir lithology, average porosity, average permeability and reservoir thickness. The method is characterized in that a deposition micro-type of a reservoir layer in a trap is identified, a Delta system deposition micro-phase concept is introduced, a concept of each trap deposition micro-phase is assigned, in the method, under the Delta deposition background, five micro-phases capable of forming the reservoir layer are considered, namely a Delta plain river sand dam micro-phase is assigned to be 3, a Delta plain waste river channel micro-phase is assigned to be 2, a leading edge underwater diversion river channel micro-phase is assigned to be 5, a leading edge estuary dam micro-phase is assigned to be 4, and a leading edge mat-shaped sand micro-phase is assigned to be 1. Reservoir lithology is divided into fine sandstone value 1, medium sandstone value 2 and coarse sandstone value 3 according to well-drilling logging information. The average porosity of the reservoir is calculated by actually measuring the porosity or calculating the porosity through lithology to trap the average porosity, and the unit is dimensionless. The average permeability of the reservoir is also obtained by actually measuring the permeability of the core or calculating the permeability, and the unit is square micron.
Step 1.3: and acquiring transportation storage condition parameters including circle source distance and cover layer thickness. The circle source distance is the linear distance between the measured oil source fault and the trap, and the unit is meter. The thickness of the cover layer is calculated by using the drilling data and the seismic data, and the unit of the thickness of the cover layer is meter.
Step 102: all the data obtained in step 101 are collated to create a matrix A (equation 1), where aijThe ith evaluation parameter is enclosed. When the parameter is a benefit type parameter, i.e. parameter aijThe larger the numerical value of (A), the better the trap is, the maximum value is selected as the optimal value, and the trap area, the buried depth high point, the trap amplitude, the reservoir sedimentary microfacies, the reservoir lithology, the average porosity, the average permeability and the cover thickness parameters belong to the condition; when the parameter is a cost-type parameter, i.e. parameter aijThe larger the value is, the worse the trap is, the minimum value is selected as the optimal value, and the distance of the ring source in the invention belongs to the condition. Setting an optimal parameter array A0(equation 2).
Wherein a isijEnclosing the jth evaluation parameter for the ith; i represents the serial number of the ith trap to be researched, and m is the total number of the traps to be researched; j represents the j-th evaluation parameter number, and n is the total number of parameters participating in trap evaluation.
A0＝(a01,a02,...,a0j,...a0n) (formula 2)
In the formula, a0jRepresents the optimal value of the jth parameter, and n is the total number of parameters participating in the trap evaluation.
Step 103: and (4) normalizing the trap parameters, wherein the benefit type parameters are normalized by adopting a formula 3, and the cost type parameters are normalized by adopting a formula 4. The normalized value is recorded as a'ijThese normalized parameters can then simply be considered as a single score for each trap. In the evaluation parameters of the trap, sorting the trap evaluation single parameter set to establish a parameter matrix A ', a'ijAnd enclosing the j evaluation parameter for the normalized i.
In the formula, aijEnclosing the jth evaluation parameter for the ith; a isjminRepresents the minimum value of the jth parameter in all traps; a isjmaxRepresents the maximum value of the jth parameter in all traps; a'ijThe normalized parameter value of the jth evaluation parameter value representing the ith trap.
In formula (II), a'ijRepresenting the normalized parameter value of the jth evaluation parameter value of the ith trap; i represents the serial number of the ith trap to be researched, and m is the total number of the traps to be researched; j represents the j-th evaluation parameter number, and n is the total number of parameters participating in trap evaluation.
104, calculating a correlation coefficient of the normalized data and the optimal parameter value by using a formula 6 by using the idea of grey correlation degree, and finally ξijAnd (4) forming a matrix B by the correlation coefficient of the jth parameter representing the ith trap and the optimal value of the parameter.
A 'in the formula'ijJ represents the ith trapValue of each evaluation parameter normalized by a0jRepresents the optimal value of the jth evaluation parameter; rho is a resolution coefficient, and rho belongs to [0,1 ]]The smaller the value is, the larger the difference between the representative correlation coefficients is, and the stronger the resolution capability is, and the value rho is 0.75 in the invention; i represents the serial number of the ith trap to be researched, and m is the total number of the traps to be researched; j represents the j-th evaluation parameter number, and n is the total number of parameters participating in trap evaluation.
In the formula, i represents the serial number of the ith trap to be researched, and m is the total number of the traps to be researched; j represents the j-th evaluation parameter number, and n is the total number of parameters participating in trap evaluation.
Step 105: calculating entropy of the parameter by calculating the specific gravity of the single parameter in the trap evaluation by using formula 8ijCalculating the entropy value E of each trap parameter by using a formula 9 for the parameter proportion of the jth parameter in the ith trap.
In formula (II), a'ijRepresenting the normalized parameter value of the jth evaluation parameter value of the ith trap; k is the adjustment coefficient, and k is the adjustment coefficient,
wherein lnm is the natural logarithm of m, wherein m is the total number of traps to be studied; ejRepresenting the entropy value of the jth parameter; n is the total number of evaluation parameters participating in the evaluation of traps.
Step 106: and (3) comprehensive parameter evaluation, namely determining a weight value according to an entropy value provided by each parameter for the trap evaluation by using an entropy weight method, and calculating a weight W of each parameter by using a formula 10.
In the formula, WjAnd (3) representing the weight of the jth parameter, wherein m is the total number of traps to be researched, and n is the total number of evaluation parameters participating in trap evaluation.
Step 107: the formula 11 is used to calculate the correlation reflecting the evaluation traps and the optimal parameter value, which is called the correlation degree R, and the correlation degree is the comprehensive score of each trap, and the quantitative evaluation of traps can be completed by arranging the trap scores in descending order.
Wherein R isjRepresents WjWeight value of the jth parameter to be analyzed, ξ, determined by entropy weight methodijAnd representing a correlation coefficient between the jth parameter and the optimal parameter in the ith trap, wherein n is the total number of the evaluation parameters participating in the evaluation of the traps.
By utilizing the comprehensive parameter sequencing, the trap evaluation can be not only established on the qualitative good and bad, but also evaluated by utilizing comprehensive quantitative parameters.
In a specific embodiment applying the method, the method and the device are applied to evaluate the traps by taking 6 traps of a certain dwarfism system in the pseudo-sonchol basin as an example.
A first part: the data selection and pretreatment, namely Q1, Q2 to Q6, are carried out, and the operation process is divided into 3 steps.
Step 1.1: and acquiring geometric parameters, and counting the trap area, the high point burial depth and the trap amplitude of each trap, which are shown in table 1.
TABLE 1 statistical table of encirclement geometry parameters
Step 1.2: and (3) acquiring reservoir conditions, and counting the sedimentary microfacies, the lithology, the average porosity, the average permeability and the thickness of the reservoir of each trap, wherein the table 2 shows the results. Among important reservoir sedimentary microfacies parameters, a reservoir with a trap Q1 is a leading edge underwater diversion river channel microfacies value of 5; the trap Q2 reservoir is assigned a leading edge estuary dam micro-facies of 4; the reservoir of trap Q3 is a delta plain river sand dam micro-facies assigned a value of 3; the trap Q4 is the micro-phase assignment of the delta plain abandoned river channel of 2, and the trap Q5 reservoir is the micro-phase assignment of leading edge mat-shaped sand of 1; the reservoir of trap Q6 is an delta plain river dam micro phase assigned a value of 3.
TABLE 2 trap reservoir Condition statistics Table
Step 1.3: and (5) acquiring transportation storage condition parameters, and counting the ring source distance and the cover layer thickness of each trap, which are shown in a table 3.
TABLE 3 transportation preservation condition parameter statistics table
A second part: and synthesizing and evaluating comprehensive parameters, namely selecting an optimal value aiming at each parameter obtained by the first part, calculating the correlation degree of each parameter of the trap and the optimal parameter by using a gray correlation method and an entropy weight method, and performing quantitative evaluation on each trap by using the correlation degree as a comprehensive score.
Step 2.1: and forming a matrix A by the data obtained by sorting. Selecting maximum values of the trap area, the buried depth high point, the trap amplitude, the reservoir sedimentary microfacies, the reservoir lithology, the average porosity, the average permeability and the cover layer thickness as optimal parameters, selecting the minimum value in the trap source distance as the most optimal parameter, and forming an optimal parameter sequence A0。
A0＝(3.8,-3892,50,5,3，12.4，16.4,12.0，0.8，30)
Step 2.2: and (3) normalizing parameters, namely normalizing parameters of the trap area, the buried depth high point, the trap amplitude, the reservoir sedimentary microfacies, the reservoir lithology, the average porosity, the average permeability and the cover layer thickness by using a formula 3, and normalizing parameters of the distance between the trap source and the cover layer by using a formula 4 to obtain A'.
Step 2.3: the normalized data and the optimal parameter A are obtained by using the grey correlation thought and the formula 60The correlation coefficients of (a) constitute a matrix B as follows:
step 2.4: and calculating the weight value of each trap parameter in the comprehensive evaluation by using an entropy weight method, and calculating the proportion of a single parameter of the trap in each trap by using a formula 8 to form a matrix P. Entropy values of the various parameters of the trap are calculated using equation 9, see table 4.
Table 4 entropy values of the various trap parameters
Step 2.5: the weight values of the trap parameters in the final evaluation are calculated using equation 10, see table 5.
TABLE 5 weight values of various trap parameters
Step 2.6: the degree of association reflecting each evaluation trap and the optimal value of the parameter is calculated by using formula 11 (see table 6), the obtained degree of association is used as a trap score, and quantitative evaluation of traps is completed by descending order, as shown in fig. 2.
TABLE 6 Final Scoring
| Trap Q1 | Trap Q2 | Trap Q3 | Trap Q4 | Trap Q5 | Trap Q6 | |
| Composite score | 0.0605 | 0.0504 | 0.0807 | 0.0346 | 0.0484 | 0.0403 |
Claims (11)
1. The new evaluation method of the delta-facies sandstone trap is characterized by comprising the following steps:
step 1, preprocessing data, and sorting single evaluation parameters of each trap;
step 2, sorting all the data obtained in the step 1 and establishing a matrix A;
step 3, carrying out the normalization of the trap parameters;
step 4, calculating a correlation coefficient;
step 5, calculating the entropy value E of each trap parameter;
step 6, calculating the weight W of each parameter;
and 7, sequencing the trap scores in a descending order to finish the quantitative evaluation of the traps.
2. The novel delta facies sandstone entrapment evaluation method of claim 1 wherein, in step 1, geometric scale parameters, reservoir condition parameters, and transport storage condition parameters are obtained.
3. The novel delta facies sandstone trap evaluation method of claim 2, wherein in step 1, geometric scale parameters are obtained, including trap area, high point burial depth, and trap amplitude; respectively measuring the actual area of each trap, wherein the unit is square kilometer; measuring the embedded depth of the trap high point in unit meter; the trap amplitude is measured in meters.
4. The novel delta facies sandstone entrapment evaluation method of claim 2 wherein, in step 1, reservoir condition parameters are obtained including reservoir sedimentary microfacies, reservoir lithology, average porosity, average permeability, reservoir thickness; identifying the deposition micro type of a trap inner reservoir, introducing a Delta system deposition micro-phase concept, assigning values to each trap deposition micro-phase concept to form five micro-phases of the reservoir, wherein the micro-phase assignment of the Delta plain river sand dam is 3, the micro-phase assignment of the Delta plain abandoned river channel is 2, the micro-phase assignment of the leading edge underwater diversion river channel is 5, the micro-phase assignment of the leading edge estuary dam is 4, and the micro-phase assignment of the leading edge mat-shaped sand is 1; reservoir lithology is divided into fine sandstone value 1, medium sandstone value 2 and coarse sandstone value 3 according to well-drilling logging information; the average porosity of the reservoir is calculated by actually measuring the porosity or calculating the porosity through lithology, and the unit is dimensionless; the average permeability of the reservoir is also obtained by actually measuring the permeability of the core or calculating the permeability, and the unit is square micron.
5. The novel delta phase sandstone entrapment evaluation method of claim 2, wherein, in step 1, transportation storage condition parameters are obtained, including the circle source distance and the cap thickness; the circle source distance is the closest linear distance between the measured oil source fault and the trap, and the unit is meter; the thickness of the cover layer is calculated by using the drilling data and the seismic data, and the unit of the thickness of the cover layer is meter.
6. The novel delta phase sandstone entrapment evaluation method of claim 1, wherein in step 2, a matrix a is established as:
wherein a isijEnclosing the jth evaluation parameter for the ith; i represents the serial number of the ith trap to be researched, and m is the total number of the traps to be researched; j represents the sequence number of the jth evaluation parameter, and n is the total number of the parameters participating in trap evaluation;
when the parameter is a benefit type parameter, i.e. parameter aijThe larger the numerical value of (2), the better the trap is, the maximum value is selected as an optimal value, and the trap area, the burial depth high point, the trap amplitude, the reservoir sedimentary microfacies, the reservoir lithology, the average porosity, the average permeability and the cover thickness parameters belong to the condition; when the parameter is a cost-type parameter, i.e. parameter aijThe larger the numerical value is, the worse the trap is, the minimum value is selected as the optimal value, and the distance of the trap source belongs to the condition; setting the optimal index of each parameter as an array A0：
A0＝(a01,a02,...,a0j,...a0n) (formula 2)
In the formula, a0jRepresents the optimal value of the jth parameter, and n is the total number of parameters participating in the trap evaluation.
7. The novel delta phase sandstone entrapment evaluation method of claim 1, wherein in step 3, the benefit-type parameters are normalized using equation 3:
the cost-type parameters are normalized by equation 4:
in the formula, aijEnclosing the jth evaluation parameter for the ith; a isjminRepresents the minimum value of the jth parameter in the trap to be studied; a isjmaxRepresents the maximum value of the jth parameter in the trap to be studied; a'ijRepresenting the normalized parameter value of the jth evaluation parameter value of the ith trap;
normalized parameter a'ijCan be considered as a single score for each trap; in the evaluation parameters of the trap, sorting the evaluation single parameter set of the trap to establish a parameter matrix A':
in formula (II), a'ijRepresenting the normalized parameter value of the jth evaluation parameter value of the ith trap; i represents the serial number of the ith trap to be researched, and m is the total number of the traps to be researched; j represents the j-th evaluation parameter number, and n is the total number of parameters participating in trap evaluation.
8. The novel delta phase sandstone entrapment evaluation method of claim 1, wherein in step 4, a correlation coefficient is calculated by using a grey correlation idea and using formula 6 to calculate the normalized data and the optimal parameter value, and finally ξijAnd (3) the correlation coefficient of the jth parameter representing the ith trap and the optimal value of the parameter and forming a matrix B:
a 'in the formula'ijThe j-th evaluation parameter value representing the i-th trap is classifiedNormalized parameter value, a0jRepresents the optimal value of the jth evaluation parameter; rho is a resolution coefficient, and rho belongs to [0,1 ]]The smaller the value is, the larger the difference between the representative correlation coefficients is, and the stronger the resolution capability is; i represents the serial number of the ith trap to be researched, and m is the total number of the traps to be researched; j represents the sequence number of the jth evaluation parameter, and n is the total number of the parameters participating in trap evaluation;
in the formula, i represents the serial number of the ith trap to be researched, and m is the total number of the traps to be researched; j represents the j-th evaluation parameter number, and n is the total number of parameters participating in trap evaluation.
9. The novel delta phase sandstone trap evaluation method of claim 1, wherein in step 5, the specific gravity of the single parameter in the trap evaluation, P, is calculated by using the formula 8ijCalculating the entropy value E of each trap parameter for the proportion of the jth parameter in the ith trap by using a formula 9:
in formula (II), a'ijRepresenting the normalized parameter value of the jth evaluation parameter value of the ith trap; k is the adjustment coefficient, and k is the adjustment coefficient,
wherein lnm is the natural logarithm of m, wherein m is the total number of traps to be studied; ejRepresenting the entropy value of the jth parameter; n is the total number of evaluation parameters participating in the evaluation of traps.
10. The novel delta facies sandstone trap evaluation method of claim 1, wherein in step 6, comprehensive parameter evaluation is performed, an entropy weight method is used to determine a weight value according to an entropy value E provided by each parameter for trap evaluation, and a weight value W of each parameter is calculated by using formula 10:
in the formula, WjAnd (3) representing the weight of the jth parameter, wherein m is the total number of traps to be researched, and n is the total number of evaluation parameters participating in trap evaluation.
11. The novel evaluation method of deltaic sandstone traps of claim 1, wherein in step 7, the relationship reflecting the evaluation traps and the optimal parameter value is calculated by formula 11, which is called the relationship degree R, and the relationship degree is the comprehensive score of each trap, and the trap scores are arranged in descending order to complete the quantitative evaluation of traps:
wherein R isjRepresents WjWeight value of the jth parameter to be analyzed, ξ, determined by entropy weight methodijAnd representing a correlation coefficient between the jth parameter and the optimal parameter in the ith trap, wherein n is the total number of the evaluation parameters participating in the evaluation of the traps.
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