CN111101924A - Lithologic reservoir dominant facies band prediction method and device - Google Patents
Lithologic reservoir dominant facies band prediction method and device Download PDFInfo
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- CN111101924A CN111101924A CN201911118469.4A CN201911118469A CN111101924A CN 111101924 A CN111101924 A CN 111101924A CN 201911118469 A CN201911118469 A CN 201911118469A CN 111101924 A CN111101924 A CN 111101924A
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Abstract
The embodiment of the invention provides a lithologic reservoir dominant facies band prediction method and lithologic reservoir dominant facies band prediction equipment. The method comprises the following steps: obtaining a plurality of reservoir development evaluation parameters according to the reservoir evaluation result data of each well unit well, constructing an evaluation parameter matrix of different depth points of each well unit well according to the reservoir development evaluation parameters, dividing the reservoir development evaluation parameters into a reservoir development evaluation main parameter and a reservoir development evaluation secondary parameter, and obtaining weight coefficients of the normalized reservoir development evaluation parameters; acquiring a reservoir quality average factor of a layer of sand body of each well unit well according to the normalized plurality of reservoir development evaluation parameters and weight coefficients, and acquiring a reservoir quality comprehensive evaluation factor of a layer of sand body by combining the development state of the layer of sand body; and constructing a lithologic reservoir dominant facies zone division standard according to the reservoir quality comprehensive evaluation factor of one layer, and predicting the lithologic reservoir dominant facies zone. The invention effectively improves the reserve exploration reliability of the lithologic target stratum.
Description
Technical Field
The embodiment of the invention relates to the technical field of geological exploration prediction, in particular to a lithologic reservoir dominant facies band prediction method and device.
Background
With the continuous development of petroleum geological exploration, lithologic oil and gas reservoirs in the slope region of continental facies lakes have become the key point of exploration. Research shows that whether a reservoir of a lithologic oil and gas reservoir in a slope region develops or not can be influenced by multiple key elements, however, in the past, single-factor conventional qualitative evaluation is mainly adopted for the lithologic oil and gas reservoir of the stratum, and the reliability of an exploration target is relatively low, so that the method for quantitatively predicting the lithologic reservoir advantage facies is developed, quantitative prediction can be carried out on the lithologic reservoir advantage facies, the reserve exploration reliability of the lithologic target stratum is effectively improved, and the method becomes a technical problem generally concerned in the industry.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a lithologic reservoir dominant facies band prediction method and lithologic reservoir dominant facies band prediction equipment.
In a first aspect, an embodiment of the present invention provides a lithologic reservoir dominant facies prediction method, including: obtaining a plurality of reservoir development evaluation parameters according to the reservoir evaluation result data of each well unit well, constructing an evaluation parameter matrix of different depth points of each well unit well according to the plurality of reservoir development evaluation parameters, dividing the plurality of reservoir development evaluation parameters into reservoir development evaluation main parameters and reservoir development evaluation secondary parameters, and obtaining weight coefficients of the plurality of reservoir development evaluation parameters after normalization; acquiring a reservoir quality average factor of a layer of sand body of each well unit well according to the normalized plurality of reservoir development evaluation parameters and the weight coefficient, and acquiring a reservoir quality comprehensive evaluation factor of the layer by combining the development state of the layer of sand body; and constructing a lithologic reservoir dominant facies zone division standard according to the reservoir quality comprehensive evaluation factor of the layer, and predicting the lithologic reservoir dominant facies zone according to the layer reservoir quality comprehensive evaluation factor contour map.
Further, on the basis of the contents of the above method embodiments, in the method for predicting lithologic reservoir dominant facies bands provided in the embodiments of the present invention, the several reservoir development evaluation parameters include: depth of burial, sand thickness, porosity, permeability and argillaceous content; wherein, the porosity is a main parameter for evaluating the reservoir development; the buried depth, the sand thickness, the permeability and the argillaceous content are secondary parameters for evaluating the reservoir development.
Further, on the basis of the content of the above method embodiment, the method for predicting lithologic reservoir dominant facies bands provided in the embodiment of the present invention includes:
wherein D is the buried depth; m is the thickness of the sand body; phi is porosity; k is the permeability; p is the mud content; each row of elements of the matrix is five reservoir development evaluation parameters of the same single well and the same depth.
Further, on the basis of the content of the above method embodiment, in the method for predicting lithologic reservoir dominant facies bands provided in the embodiment of the present invention, the normalized weighting coefficients of the plurality of reservoir development evaluation parameters include:
ΔYi(X,Φ)＝|Yi(X)-Yi(Φ)|
Δmax＝max(ΔYi(X,Φ))
Δmin＝min(ΔYi(X,Φ))
wherein A (Φ) is a porosity weight coefficient; x is buried depth, sand thickness, permeability or argillaceous content; a (X) is a burial depth weight coefficient, a sand thickness weight coefficient, a permeability weight coefficient or a argillaceous content weight coefficient corresponding to X;
is the average value of Ri; ri is the correlation degree of each secondary reservoir development evaluation parameter and the primary reservoir development evaluation parameter; rho is a resolution coefficient; i is 1,2, …, n, which is the ith depth of the same single well.
Further, on the basis of the content of the embodiment of the method, the method for predicting lithology reservoir dominant facies bands provided in the embodiment of the present invention includes the following steps:
Qi＝Yi(Φ)A(Φ)+Yi(K)A(K)+Yi(D)A(D)+Yi(M)A(M)+Yi(P)A(P)
wherein the content of the first and second substances,
the reservoir quality average factor of a layer of sand body of each well unit well; qi is a reservoir quality evaluation factor of a layer of sand body of a layer of each well unit well.
Further, on the basis of the content of the embodiment of the method, the method for predicting the lithologic reservoir dominant facies band provided in the embodiment of the present invention, in which the development state of the sand body of the horizon is combined to obtain the reservoir quality comprehensive evaluation factor of the horizon, includes:
wherein, SPW is the weight coefficient of the development state of the sand body at the layer; q is a reservoir quality comprehensive evaluation factor of the layer.
Further, on the basis of the content of the embodiment of the method, the method for predicting the lithologic reservoir dominant facies bands provided in the embodiment of the present invention, wherein the constructing of the lithologic reservoir dominant facies band division standard according to the reservoir quality comprehensive evaluation factor of the one horizon includes: if Q is greater than 0.5, the lithologic reservoir is a type; if Q is more than or equal to 0.4 and less than or equal to 0.5, the lithologic reservoir is of two types; if Q is more than or equal to 0.3 and less than 0.4, the lithologic reservoir is divided into three types; if Q is more than or equal to 0.2 and less than 0.3, the lithologic reservoir is of four types; if Q is less than 0.2, the lithologic reservoir is divided into five types; wherein, one to three types are determined as lithologic reservoir dominant facies.
In a second aspect, an embodiment of the present invention provides a lithologic reservoir dominant facies prediction apparatus, including:
the evaluation parameter and weight coefficient acquisition module is used for obtaining a plurality of reservoir development evaluation parameters according to the reservoir evaluation result data of each well unit well, constructing an evaluation parameter matrix of different depth points of each well unit well according to the plurality of reservoir development evaluation parameters, dividing the plurality of reservoir development evaluation parameters into reservoir development evaluation main parameters and reservoir development evaluation secondary parameters, and acquiring the weight coefficients of the plurality of reservoir development evaluation parameters after normalization;
the comprehensive evaluation factor module is used for obtaining a reservoir quality average factor of a layer of sand body of each well unit well according to the normalized plurality of reservoir development evaluation parameters and the weight coefficient, and obtaining the reservoir quality comprehensive evaluation factor of the layer by combining the development state of the layer of sand body;
and the dominant facies zone prediction module is used for constructing a lithologic reservoir dominant facies zone division standard according to the reservoir quality comprehensive evaluation factor of the layer, constructing a layer level reservoir quality comprehensive evaluation factor contour map and predicting the dominant facies zone of the lithologic reservoir.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor calling the program instructions being capable of performing the method of lithology reservoir dominant facies prediction provided by any one of the various possible implementations of the first aspect.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform a method for lithology reservoir dominant facies prediction provided by any one of the various possible implementations of the first aspect.
According to the lithologic reservoir dominant facies zone prediction method and device provided by the embodiment of the invention, by constructing a plurality of normalized reservoir development evaluation parameters and weight coefficients, a reservoir quality average factor and a reservoir quality comprehensive evaluation factor are obtained on the basis, and further, a lithologic reservoir dominant facies zone division standard and a lithologic reservoir quality comprehensive evaluation factor contour map are constructed, so that quantitative prediction can be carried out on the lithologic reservoir dominant facies zone of the stratum, and the reserve exploration reliability of the lithologic target stratum is effectively improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief description will be given below to the drawings required for the description of the embodiments or the prior art, and it is obvious that the drawings in the following description are 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 flow chart of a lithologic reservoir dominance phase zone prediction method provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of actual prediction effects of lithologic reservoir dominant facies bands provided by an embodiment of the invention;
FIG. 3 is a schematic structural diagram of a lithology reservoir dominant facies band prediction apparatus according to an embodiment of the present invention;
fig. 4 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. In addition, technical features of various embodiments or individual embodiments provided by the invention can be arbitrarily combined with each other to form a feasible technical solution, but must be realized by a person skilled in the art, and when the technical solution combination is contradictory or cannot be realized, the technical solution combination is not considered to exist and is not within the protection scope of the present invention.
The embodiment of the invention provides a lithologic reservoir dominant facies band prediction method, and referring to fig. 1, the method comprises the following steps:
101. obtaining a plurality of reservoir development evaluation parameters according to the reservoir evaluation result data of each well unit well, constructing an evaluation parameter matrix of different depth points of each well unit well according to the plurality of reservoir development evaluation parameters, dividing the plurality of reservoir development evaluation parameters into reservoir development evaluation main parameters and reservoir development evaluation secondary parameters, and obtaining weight coefficients of the plurality of reservoir development evaluation parameters after normalization; specifically, the normalization of the plurality of reservoir development evaluation parameters is to perform normalization processing (i.e., normalization) on the original data, i.e., each evaluation index parameter, by using a maximum value normalization method because the physical meanings of the parameters are different and the dimensions of the data are different in the lithologic reservoir quantitative evaluation. The method adopts a maximum value standardization method to carry out standardization processing on a plurality of reservoir development evaluation parameters (the parameters are five types of parameters in the subsequent embodiment), so that the influence on the evaluation caused by different parameters with different physical meanings and dimensions can be eliminated. For parameters with larger values, which reflect better reservoir quality, such as porosity (phi), permeability (k) and sand thickness (M), a single parameter is divided by the maximum value of the same kind of parameter to perform standardization treatment (namely normalization):
Ystandard of merit＝Y/Ymax
For parameters with larger values, which reflect worse reservoir quality, such as burial depth (D) and shale content (P), the difference of a single parameter subtracted from the maximum value of the parameters is divided by the maximum value to carry out standardization treatment (namely normalization):
Ystandard of merit＝(Ymax-Y)/Ymax
102. Acquiring a reservoir quality average factor of a layer of sand body of each well unit well according to the normalized plurality of reservoir development evaluation parameters and the weight coefficient, and acquiring a reservoir quality comprehensive evaluation factor of the layer by combining the development state of the layer of sand body;
103. and constructing a lithologic reservoir dominant facies zone division standard according to the reservoir quality comprehensive evaluation factor of the layer, and predicting the lithologic reservoir dominant facies zone according to the layer reservoir quality comprehensive evaluation factor contour map.
Based on the content of the above method embodiment, as an optional embodiment, the method for predicting lithologic reservoir dominant facies bands provided in the embodiment of the present invention includes: depth of burial, sand thickness, porosity, permeability and argillaceous content; wherein, the porosity is a main parameter for evaluating the reservoir development; the buried depth, the sand thickness, the permeability and the argillaceous content are secondary parameters for evaluating the reservoir development.
Based on the content of the above method embodiment, as an optional embodiment, the method for predicting lithologic reservoir dominant facies bands provided in the embodiment of the present invention includes:
wherein D is the buried depth; m is the thickness of the sand body; phi is porosity; k is the permeability; p is the mud content; each row of elements of the matrix is five reservoir development evaluation parameters of the same single well and the same depth.
Based on the content of the foregoing method embodiment, as an optional embodiment, in the method for predicting lithologic reservoir dominant facies bands provided in the embodiment of the present invention, the normalized weight coefficients of the plurality of reservoir development evaluation parameters include:
ΔYi(X,Φ)＝|Yi(X)-Yi(Φ)|
Δmax＝max(ΔYi(X,Φ))
Δmin＝min(ΔYi(X,Φ))
wherein A (Φ) is a porosity weight coefficient; x is buried depth, sand thickness, permeability or argillaceous content; a (X) is a burial depth weight coefficient, a sand thickness weight coefficient, a permeability weight coefficient or a argillaceous content weight coefficient corresponding to X;
is the average value of Ri; ri is the correlation degree of each secondary reservoir development evaluation parameter and the primary reservoir development evaluation parameter; rho is a resolution coefficient, aims to weaken distortion caused by too large maximum absolute value difference, and generally takes a value of 0.5; i is 1,2, …, n, which is the ith depth of the same single well. Specifically, Δ Yi (X, Φ) ═ Yi (X) -Yi (Φ) | includes the following cases, respectively:
ΔYi(K,Φ)＝|Yi(K)-Yi(Φ)|
ΔYi(D,Φ)＝|Yi(D)-Yi(Φ)|
ΔYi(M,Φ)＝|Yi(M)-Yi(Φ)|
ΔYi(P,Φ)＝|Yi(P)-Yi(Φ)|
based on the content of the foregoing method embodiment, as an optional embodiment, the method for predicting lithologic reservoir dominant facies bands provided in the embodiment of the present invention, where the obtaining of the reservoir quality average factor of a layer sand body of each unit well in each well includes:
Qi＝Yi(Φ)A(Φ)+Yi(K)A(K)+Yi(D)A(D)+Yi(M)A(M)+Yi(P)A(P)
wherein the content of the first and second substances,
the reservoir quality average factor of a layer of sand body of each well unit well; qi is a reservoir quality evaluation factor of a layer of sand body of a layer of each well unit well.
Based on the content of the method embodiment, as an optional embodiment, the method for predicting lithologic reservoir dominant facies bands provided in the embodiment of the present invention, in which the comprehensive evaluation factor of reservoir quality of the horizon is obtained by combining the development state of the horizon sand, includes:
wherein, SPW is the weight coefficient of the development state of the sand body at the layer; q is a reservoir quality comprehensive evaluation factor of the layer. Specifically, the development state weight coefficient SPW of the sand body at one layer can be referred to table 1, the sandstone content percentage SP of the same layer of the single well is calculated, and the value is assigned according to the SP value, so that the development state weight coefficient SPW of the sand body at one layer is obtained.
TABLE 1
| Sandstone percentage SP | 0＜SP≦10 | 10＜SP≦20 | 20＜SP≦30 | 30＜SP≦40 | 40＜SP≦50 | 50＜SP |
| Weight coefficient SPW | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 |
Based on the content of the method embodiment, as an optional embodiment, the method for predicting lithologic reservoir dominant facies bands provided in the embodiment of the present invention, wherein the constructing of the lithologic reservoir dominant facies band division standard according to the reservoir quality comprehensive evaluation factor of the one horizon includes: if Q is greater than 0.5, the lithologic reservoir is a type; if Q is more than or equal to 0.4 and less than or equal to 0.5, the lithologic reservoir is of two types; if Q is more than or equal to 0.3 and less than 0.4, the lithologic reservoir is divided into three types; if Q is more than or equal to 0.2 and less than 0.3, the lithologic reservoir is of four types; if Q is less than 0.2, the lithologic reservoir is divided into five types; wherein, one to three types are determined as lithologic reservoir dominant facies. See table 2 for details.
TABLE 2
| Lithologic reservoir classification grade | Ⅰ | Ⅱ | Ⅲ | Ⅳ | Ⅴ |
| Reservoir quality comprehensive evaluation factor Q of one layer | ＞0.5 | 0.4～0.5 | 0.3～0.4 | 0.2～0.3 | ＜0.2 |
In table 2, lithological reservoirs of types i, ii, and iii are dominant phase zones, that is, lithological reservoirs having a Q value of 0.3 or more are dominant phase zones.
According to the lithologic reservoir dominant facies zone prediction method provided by the embodiment of the invention, by constructing a plurality of normalized reservoir development evaluation parameters and weight coefficients, a reservoir quality average factor and a reservoir quality comprehensive evaluation factor are obtained on the basis, and further a lithologic reservoir dominant facies zone division standard and a lithologic reservoir quality comprehensive evaluation factor contour map are constructed, so that quantitative prediction can be carried out on the lithologic reservoir dominant facies zone of the stratum, and the reserve exploration reliability of the lithologic target stratum is effectively improved.
In order to more clearly illustrate the essence of the technical solution of the present invention, a specific embodiment is proposed on the basis of the above-mentioned embodiment, and the essence of the technical solution of the present invention is further shown from the details. It should be noted that the specific embodiment is only for further embodying the technical essence of the present invention, and is not intended to limit the scope of the present invention, and those skilled in the art can obtain any combination type technical solution meeting the essence of the technical solution of the present invention by combining technical features based on the various embodiments of the present invention, and as long as the combined technical solution can be practically implemented, the combined technical solution is within the scope of the present patent.
The specific embodiment comprises the following steps: s1: collecting a reservoir evaluation interpretation result table of each well unit well; s2: selecting 5 parameters capable of reflecting the development of the reservoir from the data obtained in the step S1 as lithologic reservoir advantage facies quantitative evaluation parameters, and establishing a multi-well single well evaluation parameter data matrix at different depth points; s3: selecting 1 type of parameters from the 5 types of parameters selected in the step S2 as main factors of the lithologic reservoir dominant facies quantitative evaluation parameters, and the other 4 types of parameters as sub-factors of the lithologic reservoir dominant facies quantitative evaluation parameters; s4: normalizing the 5 types of parameters in the step S3; s5: calculating the normalized parameters in the step S4 by adopting a grey correlation analysis method, and solving weight coefficients of the 5 parameters in quantitative evaluation of lithologic reservoir dominant facies; s6: calculating reservoir quality average factors of sand bodies in a certain horizon of a single well by adopting the weight coefficient calculated in the step S5 and the parameter value normalized in the step S4; s7: calculating a comprehensive evaluation factor of the single well reservoir quality of the horizon by adopting the average factor of the single well reservoir quality of a certain horizon sand reservoir calculated in the step S6 and combining the overall development condition of the horizon sand, and establishing a lithologic reservoir dominant reservoir facies zone division standard according to the evaluation factor; s8: and compiling a single-well reservoir quality comprehensive evaluation factor contour map of the same layer of the research area by adopting the different single-well reservoir quality comprehensive evaluation factors calculated in the step S7, and predicting the lithologic reservoir dominant reservoir facies zone plane distribution.
In steps S1-S3, a single well reservoir evaluation interpretation result table of 40 single wells at the same position of the research area is selected, five types of parameters including burial depth (D), sand thickness (M), porosity (phi), permeability (k) and shale content (P) are selected as lithologic reservoir dominant facies quantitative evaluation parameters, a data matrix of evaluation parameters of different depth points at the same position of the multiple single wells is established, as shown in Table 3, porosity is selected as a lithologic reservoir dominant facies quantitative evaluation parameter main factor, and other 4 types of parameters are selected as lithologic reservoir dominant facies quantitative evaluation parameter sub-factors.
TABLE 3
In step S4, the normalization process is performed on the five types of parameters in the matrix. In the lithologic reservoir quantitative evaluation, the physical meanings of all parameters are different, and the dimensions of data are also different, so the purpose is realized by standardizing the original data, namely all evaluation index parameters by adopting a maximum value standardization method. The five index parameters are standardized by adopting a maximum value standardization method, so that the influence of different parameters on evaluation due to different physical meanings and dimensions can be eliminated, and the influence is shown in table 4.
TABLE 4
In step S5, the normalized parameters are calculated by using a gray correlation analysis method, and weight coefficients of the 5 parameters in quantitative evaluation of lithologic reservoir dominant facies are obtained.
Firstly, calculating absolute difference values between each child factor and parent factor value at the same depth of different single wells in a matrix in a table 3, and extreme values delta max and delta min of the absolute difference values, wherein the delta max is 0.997, and the delta min is 0;
further, the extreme value is adopted to calculate the correlation degree between the sub-factor (K/D/M/P) and the main factor (phi) of the sample with the same depth of different single wells, as shown in the table 5.
TABLE 5
The relevance R (X, phi) of the same sub-factor in the matrix is noted: x represents any one parameter of permeability K, burial depth D, reservoir net thickness M and shale content P), and averaging to obtain
Thus obtaining the correlation degree sequence R of the permeability K, the burial depth D, the net thickness M of the reservoir and the shale content P to the porosity phi as (1, 0.55, 0.71, 0.59 and 0.64);
normalizing the correlation coefficients in the correlation sequence R to obtain weight coefficients A (X) of comprehensive evaluation parameters of permeability (K), burial depth (D), reservoir net thickness (M) and shale content (P) on the reservoir, so as to obtain a correlation sequence A of the permeability K, the burial depth D, the reservoir net thickness M and the shale content P on the porosity phi, which is (0.287, 0.158, 0.203, 0.169 and 0.183);
in step S6, the calculated weight coefficients of different factors and the normalized parameter values of the factors are used to calculate the reservoir quality average factor of sand in a certain horizon of a single well.
Firstly, the weighting coefficients are respectively multiplied by the normalized single parameter values of the corresponding single parameters of the same depth point of the same single well to obtain single weighing scores, and then the single weighing coefficients of the depth point are added to obtain the reservoir quality evaluation factor Q1 of the sand body of the depth point, as shown in Table 6.
TABLE 6
Further, the Q1 of all depth point samples in the same horizon of a single well is obtained by averaging according to intervals
The average evaluation factor of the reservoir quality of the horizon of the well;
in the step S7, calculating a comprehensive evaluation factor Q of the quality of the single-well reservoir at a certain horizon by adopting the average factor Q1 of the quality of the single-well reservoir at a certain horizon calculated in the step S6 and combining the overall development condition of the sand at the horizon, and establishing a lithologic reservoir dominant reservoir facies zone division standard according to the evaluation factor Q;
firstly, calculating the sandstone content percentage SP of the same layer of a single well, and assigning values according to the SP value, thereby obtaining the weight coefficient SPW of the sandstone development condition of a certain layer of the single well, as shown in a table 7;
TABLE 7
| Sandstone percentage SP | 0＜SP≦10 | 10＜SP≦20 | 20＜SP≦30 | 30＜SP≦40 | 40＜SP≦50 | 50＜SP |
| Weight coefficient SPW | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 |
Further, adopt
And finally calculating the comprehensive lithologic reservoir evaluation factor Q of a certain layer of the single well with the SPW, wherein the larger the Q value is, the lithologic reservoir evaluation factor Q indicates the lithologic reservoir evaluationThe better the fertility.
Wherein the content of the first and second substances,
representing the average evaluation factor of the reservoir quality of a certain horizon of a single well; SPW represents the weight coefficient of the development condition of sandstone at a certain layer of a single well.
Finally, establishing a classification standard according to the size of the quality comprehensive evaluation factor Q of the single-well reservoir, and dividing the lithologic reservoir into I-V classes as shown in the table 8;
TABLE 8
In step S8, the comprehensive evaluation factors of different single-well reservoir qualities calculated in step S7 are used to compile contour maps of the comprehensive evaluation factors of different single-well reservoir qualities at the same layer of the research area, and predict the planar distribution of the lithologic reservoir dominant reservoir facies, where the range (i.e., i to iii) defined by the comprehensive evaluation factor Q of reservoir quality greater than or equal to 0.3 is the lithologic reservoir dominant development facies, as shown in fig. 2. As can be seen from fig. 2, the lithologic reservoirs at the landmark mosque, the xiaogong, and the small gathering place are dominant facies zones (Q values are all equal to or greater than 0.3), while the lithologic reservoir at the georgia zone is a non-dominant facies zone (Q value is equal to or less than 0.2).
The lithologic reservoir superiority facies prediction method provided by the specific embodiment of the invention can fully adopt various parameters in a single-well reservoir evaluation interpretation result table to realize quantitative evaluation of the lithologic reservoir, and has the advantages of simple calculation, small workload, short research period and timely and rapid guidance of lithologic oil and gas reservoir exploration through research results.
The implementation basis of the various embodiments of the present invention is realized by programmed processing performed by a device having a processor function. Therefore, in engineering practice, the technical solutions and functions thereof of the embodiments of the present invention can be packaged into various modules. Based on the actual situation, on the basis of the embodiments, the embodiments of the present invention provide a lithologic reservoir dominant facies prediction apparatus, which is used for executing the lithologic reservoir dominant facies prediction method in the above method embodiments. Referring to fig. 3, the apparatus includes:
the evaluation parameter and weight coefficient acquisition module 301 is configured to obtain a plurality of reservoir development evaluation parameters according to the reservoir evaluation result data of each well unit well, construct an evaluation parameter matrix of different depth points of each well unit well according to the plurality of reservoir development evaluation parameters, divide the plurality of reservoir development evaluation parameters into reservoir development evaluation primary parameters and reservoir development evaluation secondary parameters, and acquire weight coefficients of the plurality of reservoir development evaluation parameters after normalization;
the comprehensive evaluation factor module 302 is configured to obtain a reservoir quality average factor of a layer of sand of each well unit well according to the normalized plurality of reservoir development evaluation parameters and the weight coefficient, and obtain a reservoir quality comprehensive evaluation factor of the layer by combining a development state of the layer of sand;
and the dominant facies band prediction module 303 is used for constructing a lithologic reservoir dominant facies band division standard according to the reservoir quality comprehensive evaluation factor of the layer, constructing a layer level reservoir quality comprehensive evaluation factor contour map, and predicting the dominant facies band of the lithologic reservoir.
The lithologic reservoir dominant facies band prediction device provided by the embodiment of the invention adopts the evaluation parameter and weight coefficient acquisition module, the comprehensive evaluation factor module and the dominant facies band prediction module, obtains the reservoir quality average factor and the reservoir quality comprehensive evaluation factor on the basis of constructing a plurality of normalized reservoir development evaluation parameters and weight coefficients, and further constructs the lithologic reservoir dominant facies band division standard and the lithologic reservoir quality comprehensive evaluation factor contour map, can carry out quantitative prediction on the stratum lithologic reservoir dominant facies band, and effectively improves the reserve exploration reliability of a lithologic target stratum.
It should be noted that, the apparatus in the apparatus embodiment provided by the present invention may be used for implementing methods in other method embodiments provided by the present invention, except that corresponding function modules are provided, and the principle of the apparatus embodiment provided by the present invention is basically the same as that of the apparatus embodiment provided by the present invention, so long as a person skilled in the art obtains corresponding technical means by combining technical features on the basis of the apparatus embodiment described above, and obtains a technical solution formed by these technical means, on the premise of ensuring that the technical solution has practicability, the apparatus in the apparatus embodiment described above may be modified, so as to obtain a corresponding apparatus class embodiment, which is used for implementing methods in other method class embodiments. For example:
based on the content of the above device embodiment, as an optional embodiment, the lithology reservoir dominant facies band prediction device provided in the embodiment of the present invention further includes: the lithologic reservoir dominant facies zone division standard module is used for determining the lithologic reservoir to be a class if Q is greater than 0.5; if Q is more than or equal to 0.4 and less than or equal to 0.5, the lithologic reservoir is of two types; if Q is more than or equal to 0.3 and less than 0.4, the lithologic reservoir is divided into three types; if Q is more than or equal to 0.2 and less than 0.3, the lithologic reservoir is of four types; if Q is less than 0.2, the lithologic reservoir is divided into five types; wherein, one to three types are determined as lithologic reservoir dominant facies.
The method of the embodiment of the invention is realized by depending on the electronic equipment, so that the related electronic equipment is necessarily introduced. To this end, an embodiment of the present invention provides an electronic apparatus, as shown in fig. 4, including: at least one processor (processor)401, a communication Interface (Communications Interface)404, at least one memory (memory)402 and a communication bus 403, wherein the at least one processor 401, the communication Interface 404 and the at least one memory 402 are configured to communicate with each other via the communication bus 403. The at least one processor 401 may call logic instructions in the at least one memory 402 to perform the following method: obtaining a plurality of reservoir development evaluation parameters according to the reservoir evaluation result data of each well unit well, constructing an evaluation parameter matrix of different depth points of each well unit well according to the plurality of reservoir development evaluation parameters, dividing the plurality of reservoir development evaluation parameters into reservoir development evaluation main parameters and reservoir development evaluation secondary parameters, and obtaining weight coefficients of the plurality of reservoir development evaluation parameters after normalization; acquiring a reservoir quality average factor of a layer of sand body of each well unit well according to the normalized plurality of reservoir development evaluation parameters and the weight coefficient, and acquiring a reservoir quality comprehensive evaluation factor of the layer by combining the development state of the layer of sand body; and constructing a lithologic reservoir dominant facies zone division standard according to the reservoir quality comprehensive evaluation factor of the layer, and predicting the lithologic reservoir dominant facies zone according to the layer reservoir quality comprehensive evaluation factor contour map.
Furthermore, the logic instructions in the at least one memory 402 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. Examples include: obtaining a plurality of reservoir development evaluation parameters according to the reservoir evaluation result data of each well unit well, constructing an evaluation parameter matrix of different depth points of each well unit well according to the plurality of reservoir development evaluation parameters, dividing the plurality of reservoir development evaluation parameters into reservoir development evaluation main parameters and reservoir development evaluation secondary parameters, and obtaining weight coefficients of the plurality of reservoir development evaluation parameters after normalization; acquiring a reservoir quality average factor of a layer of sand body of each well unit well according to the normalized plurality of reservoir development evaluation parameters and the weight coefficient, and acquiring a reservoir quality comprehensive evaluation factor of the layer by combining the development state of the layer of sand body; and constructing a lithologic reservoir dominant facies zone division standard according to the reservoir quality comprehensive evaluation factor of the layer, and predicting the lithologic reservoir dominant facies zone according to the layer reservoir quality comprehensive evaluation factor contour map. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. Based on this recognition, each block in the flowchart or block diagrams may represent a module, a program segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In this patent, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A lithologic reservoir dominant facies band prediction method is characterized by comprising the following steps:
obtaining a plurality of reservoir development evaluation parameters according to the reservoir evaluation result data of each well unit well, constructing an evaluation parameter matrix of different depth points of each well unit well according to the plurality of reservoir development evaluation parameters, dividing the plurality of reservoir development evaluation parameters into reservoir development evaluation main parameters and reservoir development evaluation secondary parameters, and obtaining weight coefficients of the plurality of reservoir development evaluation parameters after normalization;
acquiring a reservoir quality average factor of a layer of sand body of each well unit well according to the normalized plurality of reservoir development evaluation parameters and the weight coefficient, and acquiring a reservoir quality comprehensive evaluation factor of the layer by combining the development state of the layer of sand body;
and constructing a lithologic reservoir dominant facies zone division standard according to the reservoir quality comprehensive evaluation factor of the layer, and predicting the lithologic reservoir dominant facies zone according to the layer reservoir quality comprehensive evaluation factor contour map.
2. The lithologic reservoir dominant facies prediction method of claim 1, wherein the plurality of reservoir development evaluation parameters comprise:
depth of burial, sand thickness, porosity, permeability and argillaceous content;
wherein, the porosity is a main parameter for evaluating the reservoir development; the buried depth, the sand thickness, the permeability and the argillaceous content are secondary parameters for evaluating the reservoir development.
3. The lithologic reservoir dominant band prediction method of claim 2, wherein the evaluation parameter matrix comprises:
wherein D is the buried depth; m is the thickness of the sand body; phi is porosity; k is the permeability; p is the mud content; each row of elements of the matrix is five reservoir development evaluation parameters of the same single well and the same depth.
4. The lithologic reservoir dominant band prediction method of claim 3, wherein the weight coefficients of the normalized reservoir development evaluation parameters comprise:
ΔYi(X,Φ)＝|Yi(X)-Yi(Φ)|
Δmax＝max(ΔYi(X,Φ))
Δmin＝min(ΔYi(X,Φ))
wherein A (Φ) is a porosity weight coefficient; x is buried depth, sand thickness, permeability or argillaceous content; a (X) is a burial depth weight coefficient, a sand thickness weight coefficient, a permeability weight coefficient or a argillaceous content weight coefficient corresponding to X;
is the average value of Ri; ri is the correlation degree of each secondary reservoir development evaluation parameter and the primary reservoir development evaluation parameter; rho is a resolution coefficient; i is 1,2, …, n, which is the ith depth of the same single well.
5. The lithologic reservoir dominant band prediction method of claim 4, wherein the obtaining of the reservoir quality average factor of a layer sand body of each unit well comprises:
Qi＝Yi(Φ)A(Φ)+Yi(K)A(K)+Yi(D)A(D)+Yi(M)A(M)+Yi(P)A(P)
wherein the content of the first and second substances,
average cause of reservoir quality for a layer of sand body of a single well of each wellA seed; qi is a reservoir quality evaluation factor of a layer of sand body of a layer of each well unit well.
6. The lithologic reservoir dominant phase zone prediction method of claim 5, wherein the obtaining of the reservoir quality comprehensive evaluation factor of the horizon by combining the development state of the sand of the horizon comprises:
wherein, SPW is the weight coefficient of the development state of the sand body at the layer; q is a reservoir quality comprehensive evaluation factor of the layer.
7. The lithologic reservoir dominant facies band prediction method of claim 6, wherein the constructing lithologic reservoir dominant facies band partitioning criteria according to the reservoir quality comprehensive evaluation factor of the one horizon comprises:
if Q is greater than 0.5, the lithologic reservoir is a type;
if Q is more than or equal to 0.4 and less than or equal to 0.5, the lithologic reservoir is of two types;
if Q is more than or equal to 0.3 and less than 0.4, the lithologic reservoir is divided into three types;
if Q is more than or equal to 0.2 and less than 0.3, the lithologic reservoir is of four types;
if Q is less than 0.2, the lithologic reservoir is divided into five types;
wherein, one to three types are determined as lithologic reservoir dominant facies.
8. A lithology reservoir dominant facies prediction device is characterized by comprising:
the evaluation parameter and weight coefficient acquisition module is used for obtaining a plurality of reservoir development evaluation parameters according to the reservoir evaluation result data of each well unit well, constructing an evaluation parameter matrix of different depth points of each well unit well according to the plurality of reservoir development evaluation parameters, dividing the plurality of reservoir development evaluation parameters into reservoir development evaluation main parameters and reservoir development evaluation secondary parameters, and acquiring the weight coefficients of the plurality of reservoir development evaluation parameters after normalization;
the comprehensive evaluation factor module is used for obtaining a reservoir quality average factor of a layer of sand body of each well unit well according to the normalized plurality of reservoir development evaluation parameters and the weight coefficient, and obtaining the reservoir quality comprehensive evaluation factor of the layer by combining the development state of the layer of sand body;
and the dominant facies zone prediction module is used for constructing a lithologic reservoir dominant facies zone division standard according to the reservoir quality comprehensive evaluation factor of the layer, constructing a layer level reservoir quality comprehensive evaluation factor contour map and predicting the dominant facies zone of the lithologic reservoir.
9. An electronic device, comprising:
at least one processor, at least one memory, a communication interface, and a bus; wherein the content of the first and second substances,
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions executable by the processor, the processor calling the program instructions to perform the method of any of claims 1 to 7.
10. A non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the method of any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114021804A (en) * | 2021-11-02 | 2022-02-08 | 东北石油大学 | Construction method of fault-lithology oil and gas reservoir oil and gas reserve prediction model |
| US11680480B2 (en) | 2021-05-25 | 2023-06-20 | Saudi Arabian Oil Company | Multi-layer gas reservoir field development system and method |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2012337383A1 (en) * | 2011-11-15 | 2014-05-08 | Chevron U.S.A. Inc. | Method of using spatially independent subsets of data to calculate vertical trend curve uncertainty of spatially correlated reservoir data |
| CN104564041A (en) * | 2014-07-24 | 2015-04-29 | 中国石油大学(华东) | Low-permeability clastic rock reservoir effectiveness evaluation method based on development permeability lower limit |
| CN104747185A (en) * | 2015-03-19 | 2015-07-01 | 成都北方石油勘探开发技术有限公司 | Heterogeneous reservoir stratum synthetic classifying evaluation method |
| US20160230548A1 (en) * | 2013-09-16 | 2016-08-11 | Schlumberger Technology Corporation | Formation Evaluation Using Stochastic Analysis Of Log Data |
| CN106803010A (en) * | 2016-08-10 | 2017-06-06 | 中国石油大学(华东) | For the Fuzzy Grey comprehensive evaluation method and device of low permeability reservoir quantitative assessment |
| CN107422374A (en) * | 2017-04-21 | 2017-12-01 | 中国石油大港油田勘探开发研究院 | A kind of sags rich in oil and gas slope belt fine granularing scalability method |
| CN108960651A (en) * | 2018-07-11 | 2018-12-07 | 西南石油大学 | A kind of integrated evaluating method of densification oil-gas reservoir multistage fracturing horizontal well completion efficiency |
| CN108999608A (en) * | 2018-06-14 | 2018-12-14 | 中国石油天然气股份有限公司 | A kind of low permeable glutenite rock reservoir predominant pathway recognition methods and system |
| CN109492860A (en) * | 2018-09-26 | 2019-03-19 | 中国石油天然气股份有限公司 | A kind of tight sandstone reservoir multi-parameter quantitative evaluation method based on gas well deliverability |
| US20190100997A1 (en) * | 2017-09-30 | 2019-04-04 | Petrochina Company Limited | Oil and gas zone effectiveness evaluation method and apparatus |
| CN109779624A (en) * | 2019-03-11 | 2019-05-21 | 西安石油大学 | Crack-hole type carbonate reservoir method is evaluated based on karst parametric synthesis |
| CN110288258A (en) * | 2019-07-02 | 2019-09-27 | 中国石油化工股份有限公司 | A kind of high water-cut reservoir Tapping Residual Oil method |
-
2019
- 2019-11-15 CN CN201911118469.4A patent/CN111101924A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2012337383A1 (en) * | 2011-11-15 | 2014-05-08 | Chevron U.S.A. Inc. | Method of using spatially independent subsets of data to calculate vertical trend curve uncertainty of spatially correlated reservoir data |
| US20160230548A1 (en) * | 2013-09-16 | 2016-08-11 | Schlumberger Technology Corporation | Formation Evaluation Using Stochastic Analysis Of Log Data |
| CN104564041A (en) * | 2014-07-24 | 2015-04-29 | 中国石油大学(华东) | Low-permeability clastic rock reservoir effectiveness evaluation method based on development permeability lower limit |
| CN104747185A (en) * | 2015-03-19 | 2015-07-01 | 成都北方石油勘探开发技术有限公司 | Heterogeneous reservoir stratum synthetic classifying evaluation method |
| CN106803010A (en) * | 2016-08-10 | 2017-06-06 | 中国石油大学(华东) | For the Fuzzy Grey comprehensive evaluation method and device of low permeability reservoir quantitative assessment |
| CN107422374A (en) * | 2017-04-21 | 2017-12-01 | 中国石油大港油田勘探开发研究院 | A kind of sags rich in oil and gas slope belt fine granularing scalability method |
| US20190100997A1 (en) * | 2017-09-30 | 2019-04-04 | Petrochina Company Limited | Oil and gas zone effectiveness evaluation method and apparatus |
| CN108999608A (en) * | 2018-06-14 | 2018-12-14 | 中国石油天然气股份有限公司 | A kind of low permeable glutenite rock reservoir predominant pathway recognition methods and system |
| CN108960651A (en) * | 2018-07-11 | 2018-12-07 | 西南石油大学 | A kind of integrated evaluating method of densification oil-gas reservoir multistage fracturing horizontal well completion efficiency |
| CN109492860A (en) * | 2018-09-26 | 2019-03-19 | 中国石油天然气股份有限公司 | A kind of tight sandstone reservoir multi-parameter quantitative evaluation method based on gas well deliverability |
| CN109779624A (en) * | 2019-03-11 | 2019-05-21 | 西安石油大学 | Crack-hole type carbonate reservoir method is evaluated based on karst parametric synthesis |
| CN110288258A (en) * | 2019-07-02 | 2019-09-27 | 中国石油化工股份有限公司 | A kind of high water-cut reservoir Tapping Residual Oil method |
Non-Patent Citations (11)
| Title |
|---|
| BINGBING SHI，ETC: ""Quantitative evaluation model for tight sandstone reservoirs based on statistical methods - A case study of the Triassic Chang 8 tight sandstones, Zhenjing area, Ordos Basin, China"", 《JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING》 * |
| 余振宇: ""大牛地气田大47-大70井区下石盒子组一段储层评价"", 《中国优秀硕士论文全文库工程科技I辑》 * |
| 周立宏，等: ""陆相断陷油气勘探有利目标区三元定量评价方法及应用——以沧东凹陷古近系孔二段为例"", 《成都理工大学学报(自然科学版)》 * |
| 姚宗全: ""红山嘴地区中下侏罗统含煤层系沉积成因与储层表征"", 《中国优秀博士论文全文库工程科技I辑》 * |
| 孙洪志，刘吉余: ""储层综合定量评价方法研究"", 《大庆石油地质与开发》 * |
| 徐艳梅，等: ""塔里木盆地克拉2气田储层综合定量评价"", 《石油地质与工程》 * |
| 李旭: ""鄂尔多斯盆地镇北—演武地区长3油层组储层特征研究"", 《中国优秀硕士论文全文库工程科技I辑》 * |
| 王雅楠: ""苏里格气田苏东41-33区块盒8和山1段储层综合评价"", 《中国优秀硕士论文全文库工程科技I辑》 * |
| 蒲秀刚，等: ""黄骅坳陷沧东凹陷孔二段高位体系域细粒相区岩性特征及地质意义"", 《中国石油勘探》 * |
| 陈培元，等: ""基于地质成因的多参数碳酸盐岩储层定量评价"", 《西南石油大学学报(自然科学版)》 * |
| 高俊: ""史深100沙二10储层综合评价研究"", 《中国优秀硕士论文全文库工程科技I辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11680480B2 (en) | 2021-05-25 | 2023-06-20 | Saudi Arabian Oil Company | Multi-layer gas reservoir field development system and method |
| CN114021804A (en) * | 2021-11-02 | 2022-02-08 | 东北石油大学 | Construction method of fault-lithology oil and gas reservoir oil and gas reserve prediction model |
| CN114021804B (en) * | 2021-11-02 | 2022-07-19 | 东北石油大学 | Construction method of fault-lithology oil and gas reservoir oil and gas reserve prediction model |
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