CN114897294A - Method and device for determining oil well test oil yield of shale oil and storage medium - Google Patents
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Abstract
The application provides a method and a device for determining oil well test oil yield of shale oil and a storage medium. Acquiring a sample data set of a sample oil well, wherein the sample data set comprises the gaseous hydrocarbon content, the liquid hydrocarbon content and the test oil yield of the sample oil well; constructing an oil-containing evaluation model according to the sample data set; acquiring a target data set of a target oil well, wherein the target data set comprises the gaseous hydrocarbon content and the liquid hydrocarbon content of the target oil well; and processing the target data set of the target oil well by using the oil-bearing property evaluation model to obtain the oil testing yield of the target oil well. The oil-bearing property evaluation model constructed in the mode fully considers the influence of hydrocarbon content on the yield of the test oil, and the result of the target yield of the test oil calculated by the oil-bearing property evaluation model is more accurate, so that the development efficiency of the shale oil field is improved conveniently.
Description
Technical Field
The application relates to the field of oil exploitation, in particular to a method and a device for determining oil well test oil yield of shale oil and a storage medium.
Background
In the field of oil exploitation, the oil content of a shale oil field is an important parameter for determining a production zone and calculating the resource amount, and is also a key index for evaluating the enrichment degree of the oil field.
In the prior art, the analysis mode of the oil content of the oil field mainly comprises a dry distillation method and an organic geochemical method.
However, the problem of light hydrocarbon loss is not considered in the various analysis modes, so that the accuracy of the oil-bearing analysis result of the oil field obtained by using the modes is not high, and the subsequent development efficiency of the shale oil formation is seriously influenced.
Disclosure of Invention
The application provides a method, a device and a storage medium for determining the oil well test oil yield of shale oil, which are used for solving the problem that the accuracy of the oil-containing analysis result of the current shale oil formation is not high.
In a first aspect, the present application provides a method for determining a well test yield of shale oil, comprising:
acquiring a sample data set of a sample oil well, wherein the sample data set comprises the gaseous hydrocarbon content, the liquid hydrocarbon content and the oil testing yield of the sample oil well;
respectively performing data fitting processing on the gaseous hydrocarbon content and the liquid hydrocarbon content in the sample data set and the test oil yield to obtain a first fitting function and a second fitting function;
constructing an oil-bearing property evaluation model according to the first fitting function and the second fitting function;
acquiring a target data set of a target oil well, wherein the target data set comprises the gaseous hydrocarbon content and the liquid hydrocarbon content of the target oil well;
and processing the target data set of the target oil well by using the oil-bearing evaluation model to obtain the oil testing yield of the target oil well.
In a second aspect, the present application provides an apparatus for determining oil well test production, comprising:
the model construction module is used for acquiring a sample data set of a sample oil well, wherein the sample data set comprises the gaseous hydrocarbon content, the liquid hydrocarbon content and the oil test yield of the sample oil well; and performing data fitting processing on the content of the gaseous hydrocarbons and the content of the liquid hydrocarbons in the sample data set and the yield of the test oil respectively to obtain a first fitting function and a second fitting function; constructing an oil-content evaluation model according to the first fitting function and the second fitting function;
the calculation module is used for acquiring a target data set of a target oil well, wherein the target data set comprises the gaseous hydrocarbon content and the liquid hydrocarbon content of the target oil well; and processing the target data set of the target oil well by using the oil-bearing property evaluation model to obtain the oil testing yield of the target oil well.
In a third aspect, the present application provides an electronic device, comprising:
a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes the computer-executable instructions stored by the memory to implement the method as previously described.
In a fourth aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions for implementing the method as described above when executed by a processor.
In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the method as described above.
The application provides a method, a device and a storage medium for determining oil well test oil yield, wherein a sample data set of a sample oil well is obtained, and the sample data set comprises the gaseous hydrocarbon content, the liquid hydrocarbon content and the test oil yield of the sample oil well; respectively performing data fitting treatment on the gaseous hydrocarbon content and the liquid hydrocarbon content in the sample data set and the oil test yield to obtain a first fitting function and a second fitting function; constructing an oil-containing evaluation model according to the first fitting function and the second fitting function; acquiring a target data set of a target oil well, wherein the target data set comprises the gaseous hydrocarbon content and the liquid hydrocarbon content of the target oil well; and processing the target data set of the target oil well by using the oil-bearing property evaluation model to obtain the oil testing yield of the target oil well. The oil-bearing performance evaluation model constructed in the mode fully considers the influence of the hydrocarbon content on the oil test yield, and the result of the target oil test yield calculated by the oil-bearing performance evaluation model is more accurate, so that the development efficiency of an oil field is improved conveniently.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic diagram of a scenario architecture upon which the present application is based;
FIG. 2 is a schematic flow chart of a method for determining a well test yield of shale oil provided herein;
FIG. 3A is a graphical illustration of a linear relationship between gaseous hydrocarbon content and production run according to an embodiment of the present disclosure;
FIG. 3B is a schematic diagram illustrating an exponential relationship between the content of gaseous hydrocarbons and the yield of test oil according to an embodiment of the present disclosure;
FIG. 3C is a graphical illustration of the relationship between the amount of gaseous hydrocarbons and the yield of test oil as a function of the logarithm of the yield of the test oil according to an embodiment of the present disclosure;
FIG. 3D is a diagram illustrating a relationship between a content of gaseous hydrocarbons and a yield of test oil as a quadratic function according to an embodiment of the present disclosure;
FIG. 4A is a graphical illustration of a linear relationship between liquid hydrocarbon content and production run according to an embodiment of the present disclosure;
FIG. 4B is a schematic diagram illustrating an exponential relationship between liquid hydrocarbon content and production run provided by an embodiment of the present application;
FIG. 4C is a graphical illustration of a liquid hydrocarbon content versus a log of production runs according to an embodiment of the present disclosure;
FIG. 4D is a diagram illustrating a relationship between liquid hydrocarbon content and yield of test oil according to a second order function according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram of an oil-bearing model generation process provided herein;
FIG. 6 is a schematic structural diagram of an apparatus for determining a test oil yield of a shale oil well according to the present application;
fig. 7 is a schematic diagram of a hardware structure of an electronic device provided in the present application.
With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. The drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the concepts of the application by those skilled in the art with reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
In the field of shale oil exploitation, the test oil yield of an oil well is an important parameter for determining a production zone and calculating the resource amount, and is also a key index for evaluating the enrichment degree of the oil field. Before the shale oil field is exploited, the oil testing yield of the oil field needs to be analyzed and calculated, and the exploitation scheme of the shale oil field is formulated by utilizing the oil testing yield obtained through analysis and calculation.
In the prior art, the analysis mode of the oil well test oil yield mainly comprises a dry distillation method, an organic geochemical method, a physical testing method, a geophysical method and other methods. Wherein the dry distillation method is to obtain the liquid hydrocarbon content in the shale oil formation through low-temperature dry distillation; the organic geochemical method refers to the characterization of the oiliness of the formation by chloroform bitumen "A" and free hydrocarbons S1.
In the actual production process, the phenomenon of light hydrocarbon loss can appear in the oil well, and through actual verification, the phenomenon of light hydrocarbon loss can influence the oil testing output of oil well. However, the problems that the light hydrocarbon loss phenomenon and the test oil yield have certain relevance are not considered in the various analysis methods, so that the test oil yield of the oil field obtained by the method is inconsistent with the actual test oil yield of the oil field, and the subsequent development efficiency of the shale oil formation is seriously influenced.
Based on the above problems, the method fully considers the influence of the hydrocarbon content on the oil testing yield, not only considers the influence of the gaseous hydrocarbon content on the oil testing yield of the oil well, but also considers the influence of the liquid hydrocarbon content on the oil testing yield of the oil well, and analyzes the influence of the gaseous hydrocarbon content and the liquid hydrocarbon content in the sample oil field on the oil testing yield to generate a corresponding oil-bearing property evaluation model, and then analyzes the oil testing yield of the target oil well by using the oil-bearing property model.
The oil-bearing model is generated by analyzing the influence of the content of gaseous hydrocarbon and the content of liquid hydrocarbon in the sample oil field on the yield of the test oil. The test oil yield calculated by using such an oil-bearing model will have a certain correlation with the gaseous and liquid hydrocarbon contents of the target field. Compared with the test oil yield obtained by adopting a dry distillation method and an organic geochemistry method in the prior art, the test oil yield obtained by calculating through the oil-containing property model is closer to the actual test oil yield of the target oil field, the accuracy is higher, and the shale oil field development efficiency is convenient to improve.
The following describes technical solutions of embodiments of the present application and how to solve the above technical problems with specific embodiments. These several specific embodiments may be combined with each other below, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a schematic diagram of a network architecture on which the present application is based, and as shown in fig. 1, the network architecture includes a server 1 and a target well 2.
The server 1 is specifically a server cluster capable of processing mass data, and a device for determining the oil well test oil yield provided by the application can be integrated or installed in the server cluster, and the device for determining the oil well test oil yield can call the operation logic and the processing logic in the server 1 to operate and process the acquired data.
The target oil well 2 is specifically one or more oil wells waiting to be mined in the actual mining process, and the data of the gas hydrocarbon content, the liquid hydrocarbon content and the like of the oil field are obtained by measuring the data of the target oil field 2, and the data are uploaded to the server 2 through the communication link for processing by the server 2.
In the embodiment of the present application, after receiving the relevant data of the target oil field 2, the server 1 calls the oil-content evaluation model constructed by the relevant data of the target oil field 2, and performs calculation processing on the relevant data of the target oil field 2, thereby calculating the test oil yield of the target oil field 2. By using the test oil yield of the pair of target oil fields 2, the mining personnel can formulate the mining strategy of the target oil field 2 so as to carry out shale oil mining work.
Example one
Fig. 2 is a schematic flow chart of a method for determining the oil well test yield of shale oil provided by the present application, as shown in fig. 2, the method includes:
And step 203, constructing an oil-containing property evaluation model according to the first fitting function and the second fitting function.
And 205, processing the target data set of the target oil well by using the oil-bearing evaluation model to obtain the oil testing yield of the target oil well.
It should be noted that the execution subject of the method for determining the oil well test yield provided by the present application is the aforementioned determining device, and as mentioned above, the determining device may be specifically installed or carried in the aforementioned server 1.
Specifically, the server in which the determining device is located may be configured with a database of oil fields in advance, where the database of oil fields includes related data of oil fields, such as: logging data, crude oil physical property analysis data, production data and the like. The logging data specifically comprises data such as an oil saturation curve of an oil field, a porosity curve of rock, a density curve of rock, a gas logging curve and the like; the crude oil physical analysis data specifically comprises crude oil density data in an oil field; the production data may specifically include test oil production data for the oil field.
When the scheme is executed, the determining device reads the database of the oil field from the server, and performs screening analysis on the data in the database to generate a sample data set including the gaseous hydrocarbon content, the liquid hydrocarbon content and the test oil yield of the sample oil well.
Wherein the determining means is directly obtainable by using the gas log in the logging data for the gaseous hydrocarbon content of the sample well. For the test oil yield of the sample well, the determining means will obtain it directly using the test oil yield data in the production data. The determining device is used for obtaining the liquid hydrocarbon content of the sample oil well by performing operation processing on relevant data such as an oil saturation curve, a porosity curve of rock, a density curve of rock, crude oil density data and the like in logging data by using the following formula (1).
Wherein, the formula (1) can be expressed as:
wherein the rho HC Is the density of liquid hydrocarbon in rock, and the unit is g/cm 3 (ii) a The rho b Is the density of the rock in g/cm 3 (ii) a Said S o Is the oil saturation in%; phi is porosity and is expressed in units of%; said C is L Is the liquid hydrocarbon content in the rock per unit mass, and the unit is%.
With the above method, the determination device will obtain a sample well data set, and table 1 is a schematic representation of the sample well data set.
TABLE 1
Among them, the number of sample fields as shown in table 1 may be plural, for example, W1-W11. And aiming at each sample oil field in the sample data set, the corresponding test oil yield, liquid hydrocarbon content and gaseous hydrocarbon content exist.
After the data set of the sample oil well is obtained, the determining device performs data fitting processing on the content of the gaseous hydrocarbons and the yield of the test oil in the sample data set to obtain a first fitting function; and synchronously or asynchronously with the processing process of the first fitting function, the determining device also performs data fitting processing on the liquid hydrocarbon content and the oil test yield of the sample data set to obtain a second fitting function.
Specifically, for the process of fitting the content of the gaseous hydrocarbons and the yield of the test oil in the sample data set to the first fitting function, the determining device may draw a two-dimensional scatter diagram of the content of the gaseous hydrocarbons and the yield of the test oil by using the data analysis tool.
Wherein, the scatter diagram also comprises a trend line which can be used for representing the relation between the content of the gaseous hydrocarbon and the yield of the test oil. By fitting an algorithm to the trend line, the determination device will acquire a mathematical expression that can be used to represent the trend line, and the mathematical expression will be the first functional relation in the present embodiment.
It can be known that, in the process of drawing the scatter diagram by the data analysis tool called by the determination device, based on the difference of the trend line fitting algorithm, the shapes of the trend lines in the obtained scatter diagram are different, and the trend lines of different shapes can be used for expressing different relations between the content of the gaseous hydrocarbon and the yield of the test oil, such as a linear relation, an exponential relation, a logarithmic relation, a quadratic relation and the like. The determining device acquires a mathematical expression corresponding to the trend line of each shape, and each first functional relation corresponding to the trend line of each shape forms a first functional relation set.
That is, the function types of the first set of functional relationships include: linear relation, exponential relation, logarithmic relation, quadratic relation and the like, wherein the linear relation is a mathematical expression corresponding to a trend line of the linear relation between the content of the gaseous hydrocarbon and the yield of the test oil; the exponential relation is a mathematical expression corresponding to an exponential trend line of the relation between the content of the gaseous hydrocarbon and the yield of the test oil; the logarithmic relation is a mathematical expression corresponding to a trend line of the logarithmic relation between the content of the gaseous hydrocarbon and the yield of the test oil; the second order relation is a mathematical expression corresponding to the trend line of the second order relation between the content of the gaseous hydrocarbon and the yield of the test oil.
After the first set of functional relationships is obtained, the determining device further needs to calculate a correlation coefficient of each first functional relationship.
Optionally, the determining device further calls a data analysis tool to calculate the correlation coefficient of the relational expression to obtain the correlation coefficient of each first functional relational expression. The determining device selects the first functional relation with the largest correlation coefficient from the first functional relations as the first fitting function.
For example, fig. 3A is a schematic diagram of a linear function relationship between the content of gaseous hydrocarbons and the production of test oil according to an embodiment of the present application, as shown in fig. 3A, a trend line in the diagram can be used to represent the linear relationship between the content of gaseous hydrocarbons and the production of test oil; from the trend line, a linear function is obtained using a data analysis tool, and a mathematical expression of the obtained linear function is expressed as y 1 ＝0.0001x 1 + 0.1478; the expression is subjected to a correlation coefficient R 2 Analysis of (3) reveals that R 2 ＝0.2814。
FIG. 3B is a schematic diagram of an exponential relationship between the content of gaseous hydrocarbons and the yield of test oil according to an embodiment of the present disclosure, as shown in FIG. 3B, a trend line in the diagram can be used to represent the exponential relationship between the content of gaseous hydrocarbons and the yield of test oil; according to the trend line, an exponential function can be obtained by using a data analysis tool, and a mathematical expression of the obtained exponential function can be expressed as
The expression is subjected to a correlation coefficient R 2 Analysis of (3) reveals that R 2 ＝0.2809。
FIG. 3C is a graph illustrating a logarithmic function of the content of gaseous hydrocarbons and the yield of test oil according to an embodiment of the present invention, and a trend line can be used to represent the logarithmic relation between the content of gaseous hydrocarbons and the yield of test oil in the graph, as shown in FIG. 3C; according to the trend line, a logarithmic function can be obtained by using a data analysis tool, and the mathematical expression of the obtained logarithmic function is expressed as y 1 ＝0.2448ln(x 1 ) -1.3975; performing R on the expression 2 Analysis of (3) reveals that R 2 ＝0.2425。
FIG. 3D is a graph illustrating a second order relationship between the content of gaseous hydrocarbons and the yield of test oil according to the embodiment of the present disclosure, as shown in FIG. 3D, a trend line can be used to represent the second order relationship between the content of gaseous hydrocarbons and the yield of test oil; according to the trend line, a quadratic function can be obtained by using a data analysis tool, and the obtained mathematical expression of the quadratic function is y 1 ＝3×10 -0.8 x 1 2 +(3×10 -5 )x 1 + 0.2504; performing R on the expression 2 Analysis of (3) reveals that R 2 ＝0.2812。
Note that x is used in this example 1 To indicate the content of gaseous hydrocarbons, y 1 Can be used for representing the yield of the test oil; the set of expressions shown in fig. 3A, 3B, 3C, and 3D constitutes a first set of functional relationships.
After the first set of functional relationships is acquired, the determining device compares the magnitude of the numerical value of the correlation coefficient of the mathematical expression of each functional relationship in the first set of functional relationships, and selects the functional relationship with the maximum numerical value of the correlation coefficient as the first fitting function.
Illustratively, the numerical magnitudes of the correlation coefficients of the mathematical expressions for the respective functional relationships of the gaseous hydrocarbon content and the test oil production shown in fig. 3A, 3B, 3C, and 3D are compared. It can be seen that the correlation coefficient of the mathematical expression of the linear function relationship between the gaseous hydrocarbon content and the test oil yield shown in fig. 3A is the largest, i.e. the first fitting function of the gaseous hydrocarbon content and the test oil yield is y 1 ＝0.0001x 1 +0.1478。
Similarly, for the process of fitting the liquid hydrocarbon content and the test oil yield in the sample data set to the second fitting function, the determining device may draw a two-dimensional scatter diagram of the liquid hydrocarbon content and the test oil yield by using the data analysis tool.
The scatter diagram also comprises a trend line for representing the relation between the liquid hydrocarbon content and the test oil yield. The determination means may obtain a mathematical expression for representing that the curve is a line, which will be the first functional relationship in the present embodiment, using a fitting algorithm of the trend line.
It can be known that, in the process of drawing the scatter diagram by calling the data analysis tool by the determination device, based on the difference of the trend line fitting algorithm, the shapes of the trend lines in the obtained scatter diagram are different, and the trend lines of different shapes can be used for expressing different relations between the liquid hydrocarbon content and the test oil yield, such as a logarithmic relation, an exponential relation, a binomial relation, a linear relation and the like. The determining device acquires a mathematical expression corresponding to the trend line of each shape, and the second functional relation corresponding to the trend line of each shape is a set of second functional relation expressions.
That is, the function types of the second set of functional relationships include: the linear relation is a mathematical expression corresponding to a trend line of the linear relation between the liquid hydrocarbon content and the test oil yield; the exponential relation is a mathematical expression corresponding to an exponential trend line of the relation between the liquid hydrocarbon content and the test oil yield; the logarithmic relation is a mathematical expression corresponding to a trend line of the logarithmic relation between the liquid hydrocarbon content and the test oil yield; the quadratic relation is a mathematical expression corresponding to a trend line of the quadratic relation between the liquid hydrocarbon content and the test oil yield.
After the second set of functional relationships is obtained, the determining device further needs to calculate the correlation coefficient of each second functional relationship.
Optionally, the determining device further calls a data analysis tool to calculate the correlation coefficient of the relational expression to obtain the correlation coefficient of each second functional relational expression. The determining device selects the second functional relation with the largest correlation coefficient from the second functional relations as the second fitting function.
Exemplary, suppose x 2 Is used to denote the liquid hydrocarbon content, y 2 Can be used to express the yield of the test oil. Fig. 4A is a diagram illustrating a relationship between liquid hydrocarbon content and production of test oil according to a linear function according to an embodiment of the present disclosure. As shown in fig. 4A, the trend line in the graph can be used to show the linear relationship between the liquid hydrocarbon content and the production of the test oil; from the trend line, a linear function is obtained using a data analysis tool, and a mathematical expression of the linear function is expressed as y 2 ＝0.4646x 2 -0.603; the expression is subjected to a correlation coefficient R 2 Analysis of (3) reveals that R 2 ＝0.5496。
Fig. 4B is a schematic diagram of an exponential function relationship between liquid hydrocarbon content and yield of oil test according to an embodiment of the present disclosure. As shown in fig. 4B, the trend line in the graph can be used to represent the exponential relationship between the liquid hydrocarbon content and the production of the test oil; obtaining an exponential function using a data analysis tool, the obtained exponential functionIs expressed as
For the expression correlation coefficient R 2 Analysis of (3) reveals that R 2 ＝0.459。
Fig. 4C is a diagram illustrating a relationship between liquid hydrocarbon content and a log function of production of a test oil according to an embodiment of the present disclosure. As shown in fig. 4C, the trend line in the graph can be used to represent the logarithmic relationship between the liquid hydrocarbon content and the production of the test oil in the graph; the data analysis tool can obtain a logarithmic function, and the functional relation expression of the obtained logarithmic function can be represented by y in a graph 2 ＝1.0174ln(x 2 ) -0.3654; r in the figure 2 0.5411 represents the correlation coefficient of the log function equation calculated by the data analysis tool.
Fig. 4D is a schematic diagram illustrating a relationship between liquid hydrocarbon content and yield of test oil according to a second-order function according to an embodiment of the present disclosure. As shown in fig. 4D, the trend line in the graph can be used to represent the quadratic relationship between the liquid hydrocarbon content and the production of the test oil; the data analysis tool can obtain a quadratic function with a mathematical expression of y 2 ＝-0.0346x 2 2 +0.6235x 2 -0.7796; to the expression and perform R 2 Analysis of (3) reveals that R 2 ＝0.5506。
Similarly, the set of expressions shown in fig. 4A, 4B, 4C, 4D constitutes a second set of functional relationships.
After the second function relational expression set is obtained, the determining device compares the magnitude of the numerical value of the correlation coefficient of the mathematical expression of each function relation in the first function relational expression set, and selects the second function relational expression with the maximum numerical value of the correlation coefficient as a second fitting function.
As in the above example, the correlation coefficients of the mathematical expressions of the respective functional relationships between the liquid hydrocarbon content and the production of the test oil shown in fig. 4A, 4B, 4C, and 4D are compared. It can be seen that the correlation coefficient of the mathematical expression of the quadratic function relationship shown in FIG. 4D is the largest, i.e. the second fitting function of the liquid hydrocarbon content and the yield of the test oil is y 2 ＝-0.0346x 2 2 +0.6235x 2 -0.7796。
Then, the determining device will construct an oil-bearing property evaluation model according to the obtained first fitting function and the second fitting function.
Illustratively, the first fitting function for the gaseous hydrocarbon content and the production of test oil is y 1 ＝0.0001x 1 +0.1478, and the second fitting function of liquid hydrocarbon content to test oil production is y 2 ＝-0.0346x 2 2 +0.6235x 2 0.7796, constructing an oil-bearing property evaluation model to establish a relation between the production of the test oil and the contents of the gaseous hydrocarbon and the liquid hydrocarbon which have different functional relations with the production of the test oil.
Considering that the correlation between the hydrocarbon content and the test oil yield of different types is different, the determining device may further analyze the sample data set to obtain the correlation degree between the gaseous hydrocarbon content and the test oil yield, and the correlation degree between the liquid hydrocarbon content and the test oil yield, respectively.
Fig. 5 is a schematic diagram of a generation process of an oil-bearing property model provided in the present application, and as shown in fig. 5, after a determination device obtains a sample data set of a sample oil well, the determination device performs data fitting processing on data in the sample data set to obtain a fitting function; meanwhile, a gray correlation analysis method and a normalization sorting method are used for processing the sample data set to obtain relative weights (such as relative weights A and B shown in FIG. 5), and finally, an oil content evaluation model is obtained by performing operation processing on the fitting function and the relative weights.
In particular, the determination of the degree of association may be based on a grey association analysis algorithm. Specifically, the step 203 may specifically include: performing correlation analysis processing on the content of gaseous hydrocarbons, the content of liquid hydrocarbons and the yield of test oil in the sample data set by using a grey correlation analysis algorithm to obtain a grey correlation coefficient, performing operation processing on the grey correlation coefficient by using a sorting normalization algorithm to obtain a relative weight value, and constructing an oil-bearing property evaluation model according to the first fitting function, the second fitting function and the relative weight value.
In this example, the gray correlation analysis method refers to a method of analyzing a correlation between factors of two different systems. Through a grey correlation analysis method, the correlation between the content of the gaseous hydrocarbon and the yield of the test oil and the correlation between the content of the liquid hydrocarbon and the yield of the test oil can be analyzed, so that a relative weight value which can be used for representing the correlation can be obtained. And the oil-bearing performance evaluation model can be constructed by combining the relative weight values with the first fitting function and the second fitting function.
The process of obtaining the relative weight value based on the gray correlation analysis algorithm and constructing the oil-content evaluation model by using the relative weight value will be further described as follows:
firstly, the determining device analyzes the correlation between the content of the gaseous hydrocarbon and the yield of the test oil and the correlation between the content of the liquid hydrocarbon and the yield of the test oil respectively by using a gray correlation analysis method to obtain a gray correlation coefficient.
The grey correlation coefficient comprises a first correlation coefficient and a second correlation coefficient.
In the process of analyzing the grey correlation coefficient, the determining device can firstly perform normalization processing on the sample data to obtain the content of the normalized gaseous hydrocarbon, the content of the normalized liquid hydrocarbon and the yield of the normalized test oil.
The normalization processing refers to processing the dimensional data into dimensionless data. The following equation (2) is used to characterize the normalization process:
wherein, X is norm The data after normalization processing, X is the original data before normalization processing, and X is the original data after normalization processing max The data with the largest value in the raw data, X min The data with the minimum value in the original data.
During calculation, the liquid hydrocarbon content, the gaseous hydrocarbon content and the test oil yield in the sample data set can be respectively substituted into the formula (2) for calculation, so that the normalized liquid hydrocarbon content, the normalized gaseous hydrocarbon content and the normalized test oil yield are obtained.
Taking the liquid hydrocarbon contents in Table 1 as an example, X is the liquid hydrocarbon content norm Expressed as normalized liquid hydrocarbon content, X is the original liquid hydrocarbon content before normalization, X max The data with the largest numerical value in the original liquid hydrocarbon content, X min The data with the minimum numerical value in the content of the original liquid hydrocarbon. And (3) substituting the values into the formula (2) respectively to obtain the normalized liquid hydrocarbon content.
For other data in the sample data set, the gaseous hydrocarbon content and the oil test yield, similar processing modes will be adopted, and the detailed description thereof is omitted in the embodiments of the present application.
By the normalization processing, the processing results shown in table 2 can be obtained.
TABLE 2
After the normalization processing is completed to obtain a normalization result shown in table 2, the determining device further calculates a first correlation coefficient by using the content of the normalized gaseous hydrocarbons and the yield of the normalized test oil; and calculating to obtain a second correlation coefficient by utilizing the liquid hydrocarbon content after the normalization treatment and the test oil yield after the normalization treatment.
Wherein the first correlation coefficient is used for representing the correlation coefficient of the content of the gaseous hydrocarbon and the yield of the test oil of each oil well in the sample oil well; and the second correlation coefficient is used for representing the correlation coefficient of the liquid hydrocarbon content and the test oil yield of each oil well in the sample oil well.
Further, the determining means may be implemented by using equation (3) as follows when calculating the first correlation coefficient:
wherein r is i (k) Specifically can be r 1 (k) And r 2 (k)，r 1 (k) The method comprises the steps that a first correlation coefficient of a kth single well in a sample oil well is shown, and the first correlation coefficient is obtained after grey correlation analysis is carried out on the content of gaseous hydrocarbons and the yield of test oil of the kth single well;
x i (k) may be specifically x 1 (k) And x 2 (k) Wherein x is 1 (k) The content of the gaseous hydrocarbon after the normalization treatment of the kth single well in the sample oil well is shown; x is the number of 2 (k) The liquid hydrocarbon content x after the normalization treatment of the kth single well in the sample oil well is shown 3 (k) The production of the tested oil after the normalization treatment of the kth single well in the sample oil well is shown.
Specifically, the method aims at the calculation process of the correlation coefficient of the normalized gaseous hydrocarbon and the normalized test oil yield. The determining device firstly calculates the difference values of the normalized gaseous hydrocarbon of each oil well in the sample oil well and the normalized gaseous hydrocarbon and the normalized test oil yield according to a formula (3); then, screening out the maximum value and the minimum value in the absolute value of the difference value according to the absolute value of the difference value; and finally, substituting the absolute value of the difference, the maximum value in the absolute values of the differences and the minimum value in the absolute values of the differences into a formula (3) to obtain a first correlation coefficient.
For example, for calculating the correlation coefficient between the normalized gaseous hydrocarbon content and the normalized test oil yield in table 2, the determining device first calculates the difference between the normalized gaseous hydrocarbon content and the normalized test oil yield, and calculates the absolute value of the difference, where the obtained result is shown in table 3.
TABLE 3
From table 3, the absolute values of the difference between the normalized gaseous hydrocarbon content and the normalized liquid hydrocarbon content of each well in the well sample and the normalized test oil yield are obtained, where the absolute value of the difference between the normalized liquid hydrocarbon content and the normalized test oil yield of the well with the well ID number W7 is the minimum data, that is, when x is the minimum, x is i (k) In particular x 2 (7)， x 3 (k) In particular x 3 (7) When the temperature of the water is higher than the set temperature,
is 0.01; the absolute value of the difference between the normalized gaseous hydrocarbon content and the normalized test oil yield of the oil well having the oil well ID number W11 of 0.60 is the maximum, namely, when x is i (k) In particular x 1 (11)，x 3 (k) In particular x 3 (11) When the temperature of the water is higher than the set temperature,
is 0.60.
| x of the difference between the normalized gaseous hydrocarbon content and the normalized test oil yield for each well in Table 3 1 (k)-x 3 (k)|、
And substituting the obtained value into a formula (3) to obtain a first correlation coefficient, wherein the value of k is 1-11.
Taking the well ID W1 as an example, the data of the group of data is 0.05, 0.01, 0.60, and substituting it into the above equation (3) results in the correlation coefficient W1 being 0.89, i.e., the first correlation coefficient being 0.89.
Similarly, the same processing operations as described above were performed on W2-W11 in Table 3 to obtain first correlation coefficients of W2-W11, respectively, and the results are shown in Table 4.
TABLE 4
Similarly, based on the data shown in table 3: | x 2 (k)-x 3 (k)|、
The determining device calculates a second correlation coefficient to obtain a second correlation coefficient of the normalized liquid hydrocarbon content and the normalized test oil yield, and the result is as shown in table 5.
TABLE 5
After the determining device calculates the gray correlation coefficient, the gray correlation coefficient is further subjected to operation processing by using a sorting normalization algorithm to obtain a relative weight value. Specifically, the first correlation coefficient determining device performs arithmetic processing by using a sorting normalization algorithm to obtain a first weight value; and the second association coefficient determining device obtains a second weight value by utilizing the operation of the sorting normalization algorithm.
Specifically, the determining means may obtain the first weight value using the following equation (5).
Wherein r is 1 Expressed is the mean value of the first correlation coefficient, r 1 (k) Denotes a first correlation coefficient, r 2 Expressed is the mean value of the second correlation coefficient, r 2 (k) Denoted is the second correlation coefficient, w 1 Is a first weight value.
That is, the determination device acquires an average value of the first correlation coefficient according to the first correlation coefficient; similarly, according to the second correlation coefficient, obtaining an average value of the second correlation coefficient; the determination means calculates the sum of the average value of the first correlation coefficient and the average value of the second correlation coefficient based on the average values; and obtaining a first weight value by utilizing the ratio of the average value of the first flat correlation coefficient to the sum of the average value of the first correlation coefficient and the average value of the second correlation coefficient.
Illustratively, the first weight value is found for the above example. In this case, n in the formula (5) takes a value of 1 to 11. Calculating the average value of the first correlation coefficient according to the table 4 to obtain the average value of the first correlation coefficient of 0.61; calculating the average value of the second correlation coefficient according to table 5 to obtain the average value of the second correlation coefficient of 0.67; calculating the average value sum of the first correlation coefficient average value and the second correlation coefficient, wherein the sum is 1.28; the first weight value is calculated to be 0.61/1.28, i.e., the first weight value is 0.48.
Similarly, the determining means may obtain the second weight value using the following equation (6).
Wherein r is 1 Expressed is the mean value of the first correlation coefficient, r 1 (k) Representing a first correlation coefficient, r 2 Expressed is the mean value of the second correlation coefficient, r 2 (k) Denoted is the second correlation coefficient, w 2 Is the second weight value.
According to the above exemplary process of obtaining the first weight value, details are not repeated here, and the second weight value is obtained to be 0.67/1.28, that is, the first weight value is 0.52.
Based on the obtained first fitting function, the second fitting function, the first weight value and the second weight value, the determining device is used for constructing an oil-containing evaluation model.
Specifically, a first product of the first fitting function and the first weight value and a second product of the second fitting function and the second weight value are calculated; and obtaining the oil-bearing property evaluation model according to the first product and the second product.
Let SOC denote test oil yield, C L Denotes the liquid hydrocarbon content, C G Indicating the gaseous hydrocarbon content. According to the first fitting function and the second fitting function and the first weight value and the second weight value obtained in the above example, the obtained oil-content evaluation model is expressed as the following formula (7):
after the oil-bearing property evaluation model is obtained, the determination device can calculate and analyze the oil test yield of any target oil well by using the model.
Specifically, in the process, the determining device needs to acquire a target data set of the target oil well, wherein the target data set includes the gaseous hydrocarbon content and the liquid hydrocarbon content of the target oil well, and the gaseous hydrocarbon content and the liquid hydrocarbon content can be obtained through exploration measurement analysis of the target oil well.
Then, the determination device calculates the test oil yield of the target oil well by using the oil-bearing property evaluation model obtained in the above embodiment and combining the gaseous hydrocarbon content and the liquid hydrocarbon content of the target oil well. It is known that, since the test oil yield of the target oil well is calculated by using the gaseous hydrocarbon content and the liquid hydrocarbon content based on the oil-bearing property evaluation model, the influence of the gaseous hydrocarbon content and the liquid hydrocarbon content on the test oil yield is considered in the calculation process. Compared with the test oil yield obtained by adopting a dry distillation method and an organic geochemistry method in the prior art, the test oil yield obtained by the implementation method is closer to the actual test oil yield of the target oil field, the accuracy is higher, and the development efficiency of the oil field is convenient to improve.
The application provides a method for determining oil well test oil yield of shale oil. Acquiring a sample data set of a sample oil well, wherein the sample data set comprises the gaseous hydrocarbon content, the liquid hydrocarbon content and the test oil yield of the sample oil well; constructing an oil-containing evaluation model according to the sample data set; acquiring a target data set of a target oil well, wherein the target data set comprises the gaseous hydrocarbon content and the liquid hydrocarbon content of the target oil well; and processing the target data set of the target oil well by using the oil-bearing property evaluation model to obtain the oil testing yield of the target oil well. The influence of the hydrocarbon content on the oil testing yield is fully considered by the oil-bearing performance evaluation model constructed in the mode, and the result of the target oil testing yield calculated by the oil-bearing performance evaluation model is more accurate, so that the development efficiency of the shale oil field is improved conveniently.
Example two
Corresponding to the method of the present application, fig. 6 is a schematic structural diagram of a device for determining the oil well test yield of shale oil provided by the present application. For ease of illustration, only the portions relevant to the present application are shown.
Referring to fig. 6, the processing apparatus includes:
the model building module 10 is used for obtaining a sample data set of a sample oil well, wherein the sample data set comprises the gaseous hydrocarbon content, the liquid hydrocarbon content and the oil testing yield of the sample oil well; and performing data fitting processing on the content of the gaseous hydrocarbons and the content of the liquid hydrocarbons in the sample data set and the yield of the test oil respectively to obtain a first fitting function and a second fitting function; constructing an oil-bearing property evaluation model according to the first fitting function and the second fitting function;
a calculation module 20, configured to obtain a target data set of a target oil well, where the target data set includes a gaseous hydrocarbon content and a liquid hydrocarbon content of the target oil well; and processing the target data set of the target oil well by using the oil-bearing property evaluation model to obtain the oil testing yield of the target oil well.
The model building module 10 is specifically configured to:
performing correlation analysis processing on the content of the gaseous hydrocarbon, the content of the liquid hydrocarbon and the yield of the test oil in the sample data set by using a grey correlation analysis algorithm to obtain a grey correlation coefficient;
performing operation processing on the grey correlation coefficient by using a sorting normalization algorithm to obtain a relative weight value;
and constructing an oil-content evaluation model according to the first fitting function, the second fitting function and the relative weight value.
The model building module 10 is further configured to:
respectively carrying out normalization processing on the gaseous hydrocarbon content, the liquid hydrocarbon content and the test oil yield in the sample data set to obtain the gaseous hydrocarbon content after the normalization processing, the liquid hydrocarbon content after the normalization processing and the test oil yield after the normalization processing;
calculating to obtain the first correlation coefficient by utilizing the content of the gaseous hydrocarbon after the normalization treatment and the yield of the test oil after the normalization treatment;
calculating to obtain the second correlation coefficient by using the normalized liquid hydrocarbon content and the normalized test oil yield;
performing operation processing on the first correlation coefficient by using a sorting normalization algorithm to obtain a first weight value;
and performing operation processing on the second correlation coefficient by using a sorting normalization algorithm to obtain a second weight value.
The model building module 10 is further configured to:
calculating a first product of the first fitting function and the first weight value, and a second product of the second fitting function and the second weight value;
and obtaining the oil-bearing property evaluation model according to the first product and the second product.
The model building module 10 is further configured to:
respectively performing data fitting processing on the content of the gaseous hydrocarbons and the content of the liquid hydrocarbons in the sample data set and the yield of the test oil to obtain a first function set and a second function set; wherein the first function set comprises a plurality of first function relations related to the content of the gaseous hydrocarbons and the yield of the test oil, and the second function set comprises a plurality of second function relations related to the content of the liquid hydrocarbons and the yield of the test oil;
respectively calculating a function correlation coefficient of each first function relation and a function correlation coefficient of each second function relation;
the functional relation with the maximum function correlation coefficient in each first functional relation is used as the first fitting function, and the functional relation with the maximum function correlation coefficient in each second functional relation is used as the second fitting function.
Optionally, the multiple first functional relationships in the first function set include: exponential, linear, logarithmic and quadratic relations;
the plurality of second functional relationships in the second set of functions includes: exponential, linear, logarithmic, and quadratic relationships.
The implementation principle of the determination apparatus provided in the present application is similar to that in any of the above embodiments, and is not described herein again.
The application provides a confirming device of oil well oil test output of shale oil. The determining device comprises a model building module and a calculating module, the model building module processes sample oil field data to obtain a row-containing evaluation model, and the calculating module can call the target oil well data set to obtain the oil testing yield of the target oil well.
The influence of the hydrocarbon content on the test oil yield is fully considered, the result of the target test oil yield calculated by using the oil-bearing property evaluation model is more accurate, and the oil field development efficiency of the shale oil is conveniently improved.
EXAMPLE III
Fig. 7 is a schematic diagram of a hardware structure of the electronic device provided in the present application, and for convenience of description, only a part related to the present application is shown.
Referring to fig. 7, a schematic structural diagram of an electronic device 1000 suitable for implementing an embodiment of the present application is shown, where the electronic device 1000 may be a terminal device. Among them, the terminal Device may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a Digital broadcast receiver, a Personal Digital Assistant (PDA), a tablet computer (PAD), a Portable Multimedia Player (PMP), a car mounted Device (e.g., car navigation terminal), etc., and a fixed terminal such as a Digital TV, a desktop computer, etc. The electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 7, the electronic device 1000 may include an output device (e.g., a central processing unit, a graphics processor, etc.) 1007 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage device 1008 into a Random Access Memory (RAM) 1003. In the RAM1003, various programs and data necessary for the operation of the electronic apparatus 1000 are also stored. The processing device 1001, the ROM 1002, and the RAM1003 are connected to each other by a bus 1004. An input/output (I/O) interface 1005 is also connected to bus 1004.
Generally, the following devices may be connected to the I/O interface 1005: input devices 1006 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 1007 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 1008 including, for example, magnetic tape, hard disk, and the like; and a communication device 1009. The communication device 1009 may allow the electronic device 1000 to communicate with other devices wirelessly or by wire to exchange data. While fig. 7 illustrates an electronic device 1000 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 1009, or installed from the storage means 1008, or installed from the ROM 1002. When executed by the processing device 1001, the computer program performs the functions defined in the method of the embodiment of the present application.
The embodiment of the present application provides a computer program product, which, when the computer program product runs on an electronic device, enables the electronic device to execute the technical solutions in the above embodiments. The principle and technical effects are similar to those of the related embodiments, and are not described herein again.
The embodiment of the present application provides a computer-readable storage medium, on which program instructions are stored, and when the program instructions are executed by an electronic device, the electronic device is enabled to execute the technical solutions of the above embodiments. The principle and technical effects are similar to those of the related embodiments, and are not described herein again.
The above embodiments are provided to explain the purpose, technical solutions and advantages of the present application in further detail, and it should be understood that the above embodiments are merely illustrative of the present application and are not intended to limit the scope of the present application, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present application should be included in the scope of the present application.
Claims (10)
1. A method for determining a well test yield of shale oil, comprising:
acquiring a sample data set of a sample oil well, wherein the sample data set comprises the gaseous hydrocarbon content, the liquid hydrocarbon content and the oil testing yield of the sample oil well;
respectively performing data fitting processing on the content of the gaseous hydrocarbons and the content of the liquid hydrocarbons in the sample data set and the yield of the test oil to obtain a first fitting function and a second fitting function;
constructing an oil-bearing property evaluation model according to the first fitting function and the second fitting function;
acquiring a target data set of a target oil well, wherein the target data set comprises the gaseous hydrocarbon content and the liquid hydrocarbon content of the target oil well;
and processing the target data set of the target oil well by using the oil-bearing property evaluation model to obtain the oil testing yield of the target oil well.
2. The method of claim 1, wherein said constructing an oil-bearing evaluation model based on said first fitting function and said second fitting function comprises:
performing correlation analysis processing on the content of the gaseous hydrocarbon, the content of the liquid hydrocarbon and the yield of the test oil in the sample data set by using a grey correlation analysis algorithm to obtain a grey correlation coefficient;
performing operation processing on the grey correlation coefficient by using a sorting normalization algorithm to obtain a relative weight value;
and constructing an oil-bearing property evaluation model according to the first fitting function, the second fitting function and the relative weight value.
3. The method of claim 2, wherein the gray correlation coefficients comprise a first correlation coefficient and a second correlation coefficient; the relative weight values comprise a first weight value and a second weight value;
performing correlation analysis processing on the gaseous hydrocarbon content, the liquid hydrocarbon content and the test oil yield in the sample data set by using a gray correlation analysis algorithm to obtain a gray correlation coefficient, wherein the gray correlation analysis algorithm comprises:
respectively carrying out normalization processing on the gaseous hydrocarbon content, the liquid hydrocarbon content and the test oil yield in the sample data set to obtain the gaseous hydrocarbon content after the normalization processing, the liquid hydrocarbon content after the normalization processing and the test oil yield after the normalization processing;
calculating to obtain the first correlation coefficient by utilizing the content of the gaseous hydrocarbon after the normalization treatment and the yield of the test oil after the normalization treatment;
calculating to obtain the second correlation coefficient by using the normalized liquid hydrocarbon content and the normalized test oil yield;
the operation processing is performed on the gray correlation coefficient by using a sorting normalization algorithm to obtain a relative weight value, and the method comprises the following steps:
performing operation processing on the first correlation coefficient by using a sorting normalization algorithm to obtain a first weight value;
and performing operation processing on the second correlation coefficient by using a sorting normalization algorithm to obtain a second weight value.
4. The method of claim 3, wherein constructing an oil-content evaluation model based on the first fitting function, the second fitting function, and the relative weight values comprises:
calculating a first product of the first fitting function and the first weight value, and a second product of the second fitting function and the second weight value;
and obtaining the oil-bearing property evaluation model according to the first product and the second product.
5. The method according to any one of claims 1 to 4, wherein said fitting said gaseous hydrocarbon content and said liquid hydrocarbon content in said sample data set to said test oil production to obtain a first fitting function and a second fitting function comprises:
respectively performing data fitting processing on the content of the gaseous hydrocarbons and the content of the liquid hydrocarbons in the sample data set and the yield of the test oil to obtain a first function set and a second function set; wherein the first function set comprises a plurality of first function relations related to the content of the gaseous hydrocarbons and the yield of the test oil, and the second function set comprises a plurality of second function relations related to the content of the liquid hydrocarbons and the yield of the test oil;
respectively calculating a function correlation coefficient of each first function relation and a function correlation coefficient of each second function relation;
the functional relation with the maximum function correlation coefficient in each first functional relation is used as the first fitting function, and the functional relation with the maximum function correlation coefficient in each second functional relation is used as the second fitting function.
6. The method of claim 5, wherein the plurality of first functional relationships in the first set of functions comprises: exponential, linear, logarithmic and quadratic relations;
the plurality of second functional relationships in the second set of functions includes: exponential, linear, logarithmic, and quadratic relationships.
7. An apparatus for determining a well test yield of shale oil, comprising:
the model construction module is used for acquiring a sample data set of a sample oil well, wherein the sample data set comprises the gaseous hydrocarbon content, the liquid hydrocarbon content and the oil test yield of the sample oil well; and performing data fitting processing on the content of the gaseous hydrocarbons and the content of the liquid hydrocarbons in the sample data set and the yield of the test oil respectively to obtain a first fitting function and a second fitting function; constructing an oil-bearing property evaluation model according to the first fitting function and the second fitting function;
the calculation module is used for acquiring a target data set of a target oil well, wherein the target data set comprises the gaseous hydrocarbon content and the liquid hydrocarbon content of the target oil well; and processing the target data set of the target oil well by using the oil-bearing property evaluation model to obtain the oil testing yield of the target oil well.
8. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the method of any of claims 1-6.
9. A computer-readable storage medium having computer-executable instructions stored therein, which when executed by a processor, are configured to implement the method of any one of claims 1-6.
10. A computer program product comprising a computer program which, when executed by a processor, carries out the method of any one of claims 1-6.
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