CN116432511A - Modeling method, device, equipment and medium for dolomite reservoir fractal dimension - Google Patents
Modeling method, device, equipment and medium for dolomite reservoir fractal dimension Download PDFInfo
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Abstract
The invention discloses a modeling method, device, equipment and medium for a dolomite reservoir fractal dimension. The method comprises the following steps: acquiring reservoir characteristics of a sample dolomite reservoir; the reservoir characteristics at least comprise rock type, experimental fractal dimension, experimental porosity, experimental permeability and logging data information; inputting the rock types in the reservoir characteristics and the experimental fractal dimension into a preset neural network model to obtain a fractal dimension classification result; the fractal dimension classification result refers to a fractal dimension prediction result based on rock types; and establishing a fractal dimension relation model between the porosity, permeability, logging data information and the fractal dimension based on the rock type according to the fractal dimension prediction result and the reservoir characteristics. By adopting the technical scheme, the fractal dimension of the dolomite reservoir without experiment can be accurately obtained through the fractal dimension relation model, and the fractal dimension obtaining efficiency and convenience are improved.
Description
Technical Field
The invention relates to the technical field of modeling of dolomite reservoir fractal dimension, in particular to a method, a device, equipment and a medium for modeling of dolomite reservoir fractal dimension.
Background
Fractal dimensions are widely used to characterize irregularities and complexity of solid surfaces and structures. The prior researches show that the pore system of the rock has fractal characteristics, the effective fractal dimension is between 2 and 3, and the larger the fractal dimension is, the more complex the pore structure is, so the fractal dimension is an important parameter for expressing the complexity of the pore of the reservoir, and the complexity of the pore structure of the reservoir can be directly reflected.
The fractal dimension is used for representing shale and sandstone reservoirs at present, and is not applied to carbonate reservoirs represented by dolomite, and based on an experimental measurement method, on one hand, the experimental cost is high, and on the other hand, the fractal dimension is controlled by sampling, sample feeding intervals and processes, and uncontrollable factors are more, so that a method with low cost and no influence of the processes and other problems is needed.
Disclosure of Invention
The invention provides a modeling method, device, equipment and medium for the fractal dimension of a dolomite reservoir, which are used for solving the problems of high cost and dependence on regional environment in the detection of the fractal dimension of the dolomite reservoir.
According to an aspect of the present invention, there is provided a modeling method of a fractal dimension of a dolomite reservoir, the method comprising:
acquiring reservoir characteristics of a sample dolomite reservoir; wherein the reservoir characteristics at least comprise rock type, experimental fractal dimension, experimental porosity, experimental permeability and logging data information;
Inputting the rock types in the reservoir characteristics and the experimental fractal dimension into a preset neural network model to obtain a fractal dimension classification result; the fractal dimension classification result refers to a fractal dimension prediction result based on rock types;
establishing a fractal dimension relation model between porosity, permeability, logging data information and fractal dimension based on rock types according to the fractal dimension prediction result and other reservoir characteristics; wherein the other reservoir characteristics include remaining reservoir characteristics other than the experimental fractal dimension.
According to another aspect of the present invention, there is provided a modeling apparatus for a fractal dimension of a dolomite reservoir, the method comprising:
the reservoir characteristic acquisition module is used for acquiring reservoir characteristics of the sample dolomite reservoir; wherein the reservoir characteristics at least comprise rock type, experimental fractal dimension, experimental porosity, experimental permeability and logging data information;
the classification result acquisition module is used for inputting the rock types in the reservoir characteristics and the experimental fractal dimension into a preset neural network model to obtain a fractal dimension classification result; the fractal dimension classification result refers to a fractal dimension prediction result based on rock types;
The relation model building module is used for building a fractal dimension relation model between the porosity, permeability, logging data information and the fractal dimension based on the rock type according to the fractal dimension prediction result and other reservoir characteristics; wherein the other reservoir characteristics include remaining reservoir characteristics other than the experimental fractal dimension.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of modeling the fractal dimension of dolomite reservoir according to any of the embodiments of the present invention.
According to another aspect of the invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement a method of modeling a fractal dimension of a dolomite reservoir according to any of the embodiments of the present invention when executed.
According to the technical scheme, the fractal dimension classification result is obtained by acquiring the reservoir characteristics of the sample dolomite reservoir and inputting the reservoir characteristics into the preset neural network model, so that the preset neural network model can be sufficiently trained, and the accuracy and the stability of the calculation result are further ensured. The fractal dimension relation model based on the porosity, permeability, logging data information and fractal dimension of the rock type is established according to the fractal dimension prediction result and the reservoir characteristics, so that the fractal dimension of the dolomite reservoir which is not subjected to experiments can be accurately obtained through the fractal dimension relation model, the efficiency and convenience of obtaining the fractal dimension are improved, and the problem of experiment cost in the process of determining the reservoir characteristics of the dolomite reservoir is reduced.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for modeling the fractal dimension of a dolomite reservoir according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a modeling apparatus for fractal dimension of dolomite reservoir according to the third embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device implementing a method for modeling the fractal dimension of a dolomite reservoir according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "candidate," "target," and the like in the description and claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a method for modeling a fractal dimension of a dolomite reservoir, which is provided in an embodiment of the present invention, where the method may be performed by a device for modeling a fractal dimension of a dolomite reservoir, which may be implemented in hardware and/or software, and the device for modeling a fractal dimension of a dolomite reservoir may be configured in an electronic device having data processing capability, when the fractal dimension of the dolomite reservoir is analyzed without using an experimental method. As shown in fig. 1, the method includes:
s110, acquiring reservoir characteristics of a sample dolomite reservoir; the reservoir characteristics at least comprise rock type, experimental fractal dimension, experimental porosity, experimental permeability and logging data information.
With the continuous innovation and development of oil and gas exploration practice and theoretical knowledge, the exploration and development degree of dolomite reservoirs is continuously improved. Because the formation of dolomite reservoirs often needs to undergo multiple depositions, diagenetic and reconstruction of the structural function, in order to comprehensively evaluate such heterogeneous reservoirs with complex pore throat morphology, quantitative evaluation of the pore structure of the dolomite reservoirs is needed to carry out the works of reservoir capacity and productivity evaluation and the like based on the quantitative evaluation.
Fractal dimension can quantitatively characterize the complexity of clastic reservoir pore structures, but is not much applied to carbonate reservoirs, typified by dolomite reservoirs. The fractal dimension is generally obtained based on experiments such as a pressure pump method and a nitrogen adsorption method, but the experiment cost is high, and the processes such as sampling, sample feeding and waiting for results are required to be carried out, so that a method is required to replace an experimental measurement method to determine the fractal dimension of the dolomite reservoir. The fractal dimension of the dolomite reservoir is obtained through the fractal dimension relation model in the embodiment, the fractal dimension is not limited by the experimental type and specific sampling requirements, and the accuracy of the fractal dimension, the obtaining efficiency and the obtaining convenience are improved on the basis of the existing data. For example, in the prior art, the fractal dimension data of the experiment can be determined only by performing a nitrogen adsorption experiment, but after the fractal dimension relation model in the embodiment is determined, the fractal dimension can be determined according to the existing other reservoir characteristics without performing a specific nitrogen adsorption or mercury injection experiment, and under the condition that the experiment is inconvenient or impossible, the fractal dimension result is favorable for the optimization of the high-quality dolomite reservoir by determining the fractal dimension relation model, so that the reservoir section of the dolomite reservoir with potential high reservoir performance and capacity is obtained.
The reservoir characteristics can include rock type, experimental fractal dimension, experimental porosity, experimental permeability, logging data information, order, surface porosity, and the like.
After the sample dolomite is obtained, classifying the obtained sample dolomite to obtain four types of sample dolomite. The method comprises the steps of determining the crystal form size of sample dolomite by photographing and the like, classifying according to the determined crystal form size, classifying the classified rock types into coarse crystals, medium crystals, fine crystals and mud powder crystals, and analyzing other reservoir characteristics of a sample dolomite reservoir on the basis of four types of sample dolomite.
Photographing may result in sample dolomite crystal form size errors. However, different types of sample dolomite have different order values due to different occupation conditions of calcium cations and magnesium cations in the crystal structure. Therefore, after the sample dolomite is classified according to the crystal form size, an order analysis experiment is carried out on the sample dolomite, and the original classification is combined with the order data, so that the classification of the dolomite is further refined and optimized.
After refining and optimizing the four types of sample dolomite, the fractal dimension of the four types of sample dolomite can be obtained through a nitrogen adsorption experiment, and the information such as the porosity and the permeability of the four types of sample dolomite can be obtained by means of a high-pressure mercury injection experiment. The fractal dimension can be used for characterizing the heterogeneity of pores and the complexity of pore structures, and the nitrogen adsorption experiment is an experiment for determining the fractal dimension of complex shapes. The high-pressure mercury intrusion test is a method for measuring the pore size distribution of partial mesopores and macropores and the permeability.
Because the high-pressure mercury injection experiment is aimed at a large sample dolomite reservoir, the obtained experimental porosity and experimental permeability are an integral range, and the surface porosity can reflect the local porosity characteristics. Because of certain correlation and error of the whole and local characteristics of the sample dolomite, the experimental porosity, the experimental permeability and the surface porosity are required to be combined, and the high-pressure mercury injection experimental result is required to be further refined and optimized.
The face rate determination method comprises the following steps: and (3) grinding the four types of sample dolomite, so that the four types of sample dolomite can be observed under an optical microscope and a scanning electron microscope. And photographing the four types of sample dolomite after the grinding treatment, and performing black and white binarization on the photographed photos through the existing software technology, so that the photographed photos only display black and white colors. Wherein, the black pixel point in the black-and-white binarized photo is the porosity, and the total area of the black pixel point is S1; the white pixel points are particles, the total area of the white pixel points is S2, and then the surface porosity in the dolomite of four types of samples can be calculated, and the calculation method comprises the following steps: the areal ratio=s1/(s1+s2). The black-and-white binarization may refer to modifying other colors in the photo, and finally obtaining a photo with only two colors of black and white.
In determining characteristics of a sample dolomite reservoir, it is necessary to detect wells from which the sample dolomite was obtained in addition to analyzing the sample dolomite. The well from which the sample dolomite is obtained is detected through the prior art, a corresponding detection result is obtained, and the result is determined to be logging data information. The logging data information comprises information such as natural gamma, natural point positions, well diameters, acoustic time differences, densities, neutrons, resistivity and the like in a sample dolomite reservoir well.
S120, inputting the rock types and experimental fractal dimensions in reservoir characteristics into a preset neural network model to obtain a fractal dimension classification result; the fractal dimension classification result refers to a fractal dimension prediction result based on rock types.
The predetermined neural network model may be a predetermined neural network model for mapping data in the reservoir characteristics according to rock type. The fractal dimension classification result can be a fractal dimension result corresponding to each rock type, which is determined by iteratively analyzing the experimental fractal dimension based on the rock type. Optionally, in order to improve accuracy of the fractal dimension classification result, rock types in reservoir characteristics, experimental fractal dimensions, order degree and the like are input into the neural network model, and the fractal dimension results corresponding to different rock types are obtained under the auxiliary training of the order degree characteristics, and the order degree and the rock types have certain rules, so that the order degree is also input into the neural network model, and the accuracy of the fractal dimension classification result is improved.
After the reservoir characteristics of the sample dolomite reservoir are obtained, rock types and experimental fractal dimensions are input into a neural network model, the experimental fractal dimensions are corresponding to each other based on the rock types through the neural network model, and fractal dimension classification results are obtained, so that the effect of training a preset neural network model is achieved.
In an alternative, before the rock type and the experimental fractal dimension in the reservoir characteristics are input into the preset neural network model to obtain the fractal dimension classification result, the method further comprises:
extracting a sample dolomite reservoir with a preset proportion from the sample dolomite reservoir as a training dolomite reservoir, and the rest sample dolomite reservoirs as correction dolomite reservoirs so as to obtain fractal dimension classification results according to reservoir characteristics of the training dolomite reservoir.
Correspondingly, correcting the candidate fractal dimension relationship result of the target rock type according to the reservoir characteristics of the sample dolomite reservoir, including:
and correcting the candidate fractal dimension relation result of the target rock type according to the reservoir characteristics of the corrected dolomite reservoir.
The preset proportion may be a specific proportion preset to divide the two kinds of sample dolomite reservoirs, for example, the preset proportion may be 90%, and specific values are not limited herein, and may be determined according to actual scenes.
Since the rock types and experimental fractal dimension of all sample dolomite are input into the preset neural network model for training, the situation that a large error occurs in the fractal dimension classification result due to insufficient learning process of the preset neural network model may occur.
Therefore, before the rock types in the reservoir characteristics and the experimental fractal dimension are input into a preset neural network model to obtain a fractal dimension classification result, the sample dolomite reservoir is required to be classified according to a preset proportion. One part is used for inputting the preset neural network model to train the preset neural network model, and the other part is used for correcting the calculation result of the preset neural network model after the training of the preset neural network model, so that the calculation accuracy and stability of the preset neural network model are ensured.
S130, building a fractal dimension relation model between porosity, permeability, logging data information and fractal dimension based on rock types according to a fractal dimension prediction result and other reservoir characteristics.
The fractal dimension relation model can be a preset neural network model with accurate calculation results after training. Other reservoir characteristics include rock type, experimental porosity, experimental permeability, logging data information, and the like. Because the high-pressure mercury injection experiment is aimed at a large sample dolomite reservoir, the obtained experimental porosity and experimental permeability are an overall range, and the surface porosity reflects the local porosity characteristics. Because of certain correlation and error of the integral and local characteristics of the sample dolomite, for example, if the sample dolomite reservoir subjected to the high-pressure mercury injection test has factors influencing the experimental result, such as crack factors, the experimental data are error, i.e. the experimental porosity data and the surface porosity data of the sample dolomite reservoir of the same rock type have larger errors. Therefore, the experimental porosity, the experimental permeability and the surface porosity are combined, and the high-pressure mercury injection experimental result is further refined and optimized according to the relation between the local characteristics and the integral characteristics of dolomite. Optionally, in order to improve accuracy of experimental porosity and experimental permeability of the sample dolomite, rock type, experimental porosity, experimental permeability and face porosity are input into the neural network model, so that a relation among the experimental porosity, experimental permeability and face porosity based on the rock type is obtained, further experimental porosity and experimental permeability are screened and refined according to the relation, and a subsequent porosity relation model and a permeability relation model are determined based on the screened and refined experimental porosity and experimental permeability.
Because the experimental porosity, the experimental permeability and the surface porosity have certain rules, the surface porosity is also input into the neural network model, so that the accuracy of the experimental porosity and the experimental permeability after screening is improved, and further the accuracy of the fractal dimension relation model is improved.
After the fractal dimension classification result is obtained, a fractal dimension relation model between porosity, permeability, logging data information and fractal dimension is established according to the obtained fractal dimension classification result and reservoir characteristics. And in different rock types, the corresponding relation between the fractal dimension classification result in the fractal dimension relation model and the reservoir characteristics is different from the corresponding relation between the porosity, the permeability, the logging data information and the fractal dimension.
And a fractal dimension relation model based on the porosity, permeability, logging data information and fractal dimension of the rock type is established through the fractal dimension prediction result and the reservoir characteristics, so that the analysis can be performed through the fractal dimension relation model when the analysis of the fractal dimension of the dolomite reservoir is performed later, and the cost expense of the experiment is reduced.
In an alternative, the step of establishing a fractal dimension relationship model between the porosity, permeability, logging data information and the fractal dimension based on the rock type based on the fractal dimension prediction result and the reservoir characteristics may comprise the steps of A1-A2:
And A1, establishing a porosity relation model based on the porosity of the rock type and the logging data information and a permeability relation model based on the permeability of the rock type and the logging data information according to the rock type, the experimental porosity, the experimental permeability and the logging data information.
And A2, building a fractal dimension relation model according to the fractal dimension prediction result, the porosity relation model and the permeability relation model.
After the rock type, the experimental porosity, the experimental permeability and the logging data information are obtained, an equation can be combined by establishing a multiple linear regression equation and utilizing the porosity and the permeability obtained by a high-pressure mercury injection experiment and logging curve data, so that a porosity relation model based on the rock type porosity and the logging data information and a permeability relation model based on the rock type permeability and the logging data information are obtained respectively.
The method for establishing the equation is as follows:
porosity = natural gamma a1+natural potential b1+borehole diameter c1+acoustic moveout well warp d1+density e1+neutron f1+resistivity log G1
Permeability = natural gamma a2+natural potential b2+borehole diameter c2+acoustic moveout well warp d2+density e2+neutron f2+resistivity log G2
After determining a porosity relation model based on the porosity of the rock type and the logging data information and a permeability relation model based on the permeability of the rock type and the logging data information, a fractal dimension relation model is established according to the fractal dimension prediction result, the porosity relation model and the permeability relation model.
In an alternative, building a fractal dimension relationship model based on the fractal dimension prediction result, the porosity relationship model, and the permeability relationship model may include steps a21-a23:
and A21, determining the fractal dimension average value and the average range of the target rock type according to the fractal dimension prediction result.
And step A22, determining a candidate fractal dimension relationship result of the target rock type according to the fractal dimension average value and the fractal dimension average range of the target rock type, the porosity relationship model and the permeability relationship model.
And A23, determining a fractal dimension relation model according to a candidate fractal dimension relation result of the target rock type and a comparison result of reservoir characteristics of a sample dolomite reservoir.
After a porosity relation model based on the rock type, experimental porosity, experimental permeability and logging data information and a permeability relation model based on the rock type and logging data information are established according to the rock type, the fractal dimension average value and the average range corresponding to the target rock type are determined according to the target rock type pair, a database is established to store the fractal dimension average value and the average range of the target rock type, and further the average value and the average range of the information such as the experimental porosity, the experimental permeability and the logging data information in different target rock types are obtained.
Therefore, the corresponding relation between the fractal dimension average value and the average range under different target rock types and the porosity and the permeability can be determined according to the fractal dimension average value and the average range of the target rock types, the porosity relation model and the permeability relation model, and then the candidate fractal dimension relation result of the target rock types can be determined according to the fractal dimension average value and the average range of the target rock types, the porosity relation model and the permeability relation model.
After determining the candidate fractal dimension relationship result of the target rock type and the reservoir characteristics of the sample dolomite reservoir, the candidate fractal dimension relationship result and the reservoir characteristics of the sample dolomite reservoir can be compared, and then a fractal dimension relationship model is obtained.
In an alternative, determining the fractal dimension relationship model based on the comparison of the candidate fractal dimension relationship results for the target rock type and the reservoir characteristics of the sample dolomite reservoir may include steps a231-a232:
and step A231, determining a comparison result according to the candidate fractal dimension relation result of the target rock type and the difference degree of the experimental fractal dimension, the experimental porosity, the experimental permeability and the logging data information of the sample dolomite reservoir of the target rock type.
And A232, screening the sample dolomite reservoir if the comparison result is that the difference degree is larger than the difference threshold value, retraining according to reservoir characteristics of the screened sample dolomite reservoir to obtain updated fractal dimension classification results, and determining a fractal dimension relation model again according to the updated fractal dimension classification results and the reservoir characteristics of the screened sample dolomite reservoir.
The difference threshold may be a predetermined candidate fractal dimension relationship result for the target rock type and a maximum difference value for the experimental fractal dimension, experimental porosity, experimental permeability, and logging data information for the sample dolomite reservoir for the target rock type.
In order to verify and/or adjust the calculation accuracy of the preset neural network model, the obtained candidate fractal dimension relation result of the target rock type is compared with the porosity, permeability and fractal dimension obtained through the high-pressure mercury injection experiment and the nitrogen adsorption experiment in the step S110, so that a comparison result is obtained.
When the comparison result is larger than the difference threshold, the calculation accuracy of the preset neural network model is not up to the accuracy of normal use. At the moment, screening the sample dolomite reservoir with abnormal characteristics, and training the screened sample dolomite reservoir again to obtain updated fractal dimension classification results, so that the comparison result of the updated fractal dimension classification results and experimental results is smaller than a difference threshold value, and determining a fractal dimension relation model again by using the updated fractal dimension classification results and reservoir characteristics of the screened sample dolomite reservoir.
In an alternative, after determining the comparison result, the method further comprises step a233:
and step A233, if the comparison result is that the difference degree is smaller than or equal to a difference threshold value, correcting the candidate fractal dimension relation result of the target rock type according to the reservoir characteristics of the sample dolomite reservoir, and obtaining a final fractal dimension relation model.
When the comparison result is smaller than the difference threshold, the calculation accuracy of the preset neural network model is considered to reach the accuracy which can be used normally. However, in order to ensure the calculation accuracy of the preset neural network model, the candidate fractal dimension relationship model is corrected, so that the calculation accuracy of the follow-up preset neural network model is higher, the calculation result is closer to the experimental result, and the final fractal dimension relationship model is obtained after correction. The difference threshold is 20%, and the specific value of the difference threshold is not limited in the present invention, and may be determined according to practical situations.
In an alternative, after establishing a fractal dimension relation model between porosity, permeability, logging data information and fractal dimension based on rock type based on the fractal dimension prediction result and the reservoir characteristics, the method further comprises the steps of A3-A4:
Step A3, obtaining target reservoir characteristics of a target dolomite reservoir; wherein the target reservoir characteristics include at least two of: rock type, experimental porosity, experimental permeability, and logging data information.
And step A4, determining a target fractal dimension of the target dolomite reservoir according to the target reservoir characteristics and the fractal dimension relation model.
The target dolomite reservoir may be a dolomite reservoir waiting to be calculated by the fractal dimension relationship model and not subjected to experimentation.
After the operations of S120 and S130, the calculation result of the fractal dimension relation model can be close to the experimental result, and in addition, the fractal dimension relation model can calculate and obtain the fractal dimension after at least two features in reservoir features are obtained through training of S120 and screening and/or correction of S130.
At this time, when the target dolomite reservoir is analyzed later, at least two reservoir characteristics in the target dolomite reservoir can be input into a fractal dimension relation model, and the target fractal dimension of the target dolomite reservoir is calculated through the fractal dimension relation model.
According to the technical scheme, the fractal dimension classification result is obtained by acquiring the reservoir characteristics of the sample dolomite reservoir and inputting the reservoir characteristics into the preset neural network model, so that the preset neural network model can be sufficiently trained, and the accuracy and the stability of the calculation result are ensured. The fractal dimension relation model based on the porosity, permeability, logging data information and fractal dimension of the rock type is established according to the fractal dimension prediction result and the reservoir characteristics, so that the fractal dimension relation model can ensure that a calculation result is close to an experimental result, and the fractal dimension relation model can be used for replacing an experiment in the subsequent analysis of the dolomite reservoir, thereby reducing the cost problem in the experimental process.
Example two
Another modeling method for a fractal dimension of a dolomite reservoir, provided in the second embodiment, includes:
after the sample dolomite is obtained, classifying the sample dolomite according to the crystal form size to obtain four rock types, namely coarse crystal, medium crystal, fine crystal and mud powder crystal.
After the sample dolomite is classified, the order degree detection is carried out on the four types of sample dolomite on the basis of the sample dolomite classification, and then different lattice ordering corresponding to the four types of sample dolomite is obtained.
And detecting the sampling well according to different sampling depths of the four types of sample dolomite to obtain logging data information of the four types of sample dolomite. The logging data information comprises information such as natural gamma, natural point positions, well diameters, acoustic time differences, densities, neutrons, resistivity and the like in a sample dolomite reservoir well. Wherein the sampling well may be a well in which sample dolomite is obtained.
And respectively carrying out a nitrogen adsorption experiment and a high-pressure mercury injection experiment on the four types of sample dolomite, so as to obtain the fractal dimension, the porosity, the permeability and other information of the four types of sample dolomite.
And (3) grinding the four types of sample dolomite, so that the four types of sample dolomite can be observed under an optical microscope and a scanning electron microscope. And photographing the four types of sample dolomite after the grinding treatment, and performing black and white binarization on the photographed photos through the existing software technology. Wherein, the black pixel point is porosity, and the total area of the black pixel point is S1; the white pixel points are particles, the total area of the white pixel points is S2, and then the face rate in the dolomite of the four types of samples can be calculated.
The obtained order degree and experimental fractal dimension information of the four types of sample dolomite are input into a preset neural network model according to the rock type, the preset neural network model is trained, and the candidate fractal dimension relation result calculated by the preset neural network model is averaged according to the rock type and the average range and stored in a database.
And (3) combining the experimental porosity, experimental permeability and face rate information of the dolomite of the four types of samples, and further refining and optimizing the experimental result of the high-pressure mercury injection.
And the obtained four types of logging data information are combined with the fractal dimension of the four types of sample dolomite obtained through experiments, and the information such as the optimal experimental porosity and experimental permeability, so that the corresponding relation between the logging data information of the four types of sample dolomite and the information such as the fractal dimension, the porosity and the permeability is obtained.
The union method comprises the following steps:
porosity = natural gamma a1+natural potential b1+borehole diameter c1+acoustic moveout well warp d1+density e1+neutron f1+resistivity log G1
Permeability = natural gamma a2+natural potential b2+borehole diameter c2+acoustic moveout well warp d2+density e2+neutron f2+resistivity log G2
And (3) the average value and the average range of the candidate fractal dimension relation results stored in the database according to the rock types are linked with the corresponding relation between the logging data information of the four types of rock dolomite and the information such as fractal dimension, porosity and permeability, so that a final fractal dimension relation model is obtained.
Comparing the calculation result of the fractal dimension relation model with the experimental result, screening the sample dolomite reservoir when the error is larger than a difference threshold value, such as 20%, and carrying out the steps again according to the screened sample dolomite, so that the calculation result of the fractal dimension relation model is closer to the experimental result.
Optionally, 90% of the sample dolomite is used for determining when the neural network training and the fractal dimension relation model are performed, and after the fractal dimension relation model is determined, reservoir characteristics of the remaining 10% of the sample dolomite are used for carrying out contrast correction on the fractal dimension relation model to obtain a final fractal dimension relation model, so that accuracy of the final fractal dimension relation model is improved.
Example III
Fig. 2 is a schematic structural diagram of a modeling apparatus for fractal dimension of dolomite reservoir according to the third embodiment of the present invention. The method and the device can be suitable for the situation that the fractal dimension is obtained without using an experimental method when the fractal dimension of the dolomite reservoir is analyzed. The modeling device of the fractal dimension of the dolomite reservoir can be realized in the form of hardware and/or software, and the modeling device of the fractal dimension of the dolomite reservoir can be configured in electronic equipment with data processing capability. As shown in fig. 2, the modeling apparatus for the fractal dimension of the dolomite reservoir according to the present embodiment may include: a reservoir characteristics acquisition module 210, a classification results acquisition module 220, and a relationship model creation module 230. Wherein:
A reservoir characteristics acquisition module 210 for acquiring reservoir characteristics of a sample dolomite reservoir; the reservoir characteristics at least comprise rock type, experimental fractal dimension, experimental porosity, experimental permeability and logging data information;
the classification result obtaining module 220 is configured to input the rock type in the reservoir characteristic and the experimental fractal dimension into a preset neural network model to obtain a fractal dimension classification result; the fractal dimension classification result refers to a fractal dimension prediction result based on rock types;
the relation model building module 230 is used for building a fractal dimension relation model between the porosity, permeability, logging data information and the fractal dimension based on the rock type according to the fractal dimension prediction result and other reservoir characteristics; wherein the other reservoir characteristics include remaining reservoir characteristics other than the experimental fractal dimension.
Based on the above embodiment, optionally, the relationship model building module 230 includes:
the relation model building unit is used for building a porosity relation model based on the porosity of the rock type and the logging data information according to the rock type, the experimental porosity, the experimental permeability and the logging data information, and building a permeability relation model based on the permeability of the rock type and the logging data information;
The model building unit is used for building a fractal dimension relation model according to the fractal dimension prediction result, the porosity relation model and the permeability relation model.
On the basis of the above embodiment, optionally, the model building unit includes:
the numerical range determining subunit is used for determining a fractal dimension average value and an average range of the target rock type according to the fractal dimension prediction result;
the relation result determining subunit is used for determining a candidate fractal dimension relation result of the target rock type according to the fractal dimension average value and the average range of the target rock type, the porosity relation model and the permeability relation model;
and the relation model determining subunit is used for determining a fractal dimension relation model according to the comparison result of the candidate fractal dimension relation result of the target rock type and the reservoir characteristics of the sample dolomite reservoir.
On the basis of the above embodiment, optionally, the relationship model determining subunit is specifically configured to:
determining a comparison result according to a candidate fractal dimension relation result of the target rock type and the difference degree of experimental fractal dimension, experimental porosity, experimental permeability and logging data information of a sample dolomite reservoir of the target rock type;
And if the comparison result is that the difference degree is larger than the difference threshold value, screening the sample dolomite reservoir, retraining according to reservoir characteristics of the screened sample dolomite reservoir, and obtaining an updated fractal dimension classification result so as to redetermine a fractal dimension relation model according to the updated fractal dimension classification result and the reservoir characteristics of the screened sample dolomite reservoir.
On the basis of the above embodiment, optionally, after determining the comparison result, the method further includes:
and if the comparison result is that the difference degree is smaller than or equal to the difference threshold value, correcting the candidate fractal dimension relation result of the target rock type according to the reservoir characteristics of the sample dolomite reservoir, and obtaining a final fractal dimension relation model.
On the basis of the above embodiment, optionally, before inputting the rock type in the reservoir characteristic and the experimental fractal dimension into a preset neural network model to obtain a fractal dimension classification result, the method further includes:
extracting a sample dolomite reservoir with a preset proportion from the sample dolomite reservoir as a training dolomite reservoir, and the rest sample dolomite reservoirs as correction dolomite reservoirs so as to obtain fractal dimension classification results according to reservoir characteristics of the training dolomite reservoir;
Correspondingly, correcting the candidate fractal dimension relationship result of the target rock type according to the reservoir characteristics of the sample dolomite reservoir, including:
and correcting the candidate fractal dimension relation result of the target rock type according to the reservoir characteristics of the corrected dolomite reservoir.
On the basis of the above embodiment, optionally, after the relationship model building module 230, the method further includes:
the target feature determining module is used for acquiring target reservoir features of the target dolomite reservoir; wherein the target reservoir characteristics include at least two of: rock type, experimental porosity, experimental permeability, and logging data information;
and the target dimension determining module is used for determining the target fractal dimension of the target dolomite reservoir according to the target reservoir characteristic and the fractal dimension relation model.
The modeling device for the fractal dimension of the dolomite reservoir provided by the embodiment of the invention can execute the modeling method for the fractal dimension of the dolomite reservoir provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
The data acquisition, storage, use, processing and the like in the technical scheme meet the relevant regulations of national laws and regulations, and the public sequence is not violated.
Example IV
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 3 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 3, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as modeling the fractal dimension of the dolomite reservoir.
In some embodiments, the modeling method of the dolomite reservoir fractal dimension may be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the modeling method of the dolomite reservoir fractal dimension described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the modeling method of the dolomite reservoir fractal dimension in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above can be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of modeling a fractal dimension of a dolomite reservoir, comprising:
acquiring reservoir characteristics of a sample dolomite reservoir; wherein the reservoir characteristics at least comprise rock type, experimental fractal dimension, experimental porosity, experimental permeability and logging data information;
inputting the rock types in the reservoir characteristics and the experimental fractal dimension into a preset neural network model to obtain a fractal dimension classification result; the fractal dimension classification result refers to a fractal dimension prediction result based on rock types;
Establishing a fractal dimension relation model between porosity, permeability, logging data information and fractal dimension based on rock types according to the fractal dimension prediction result and other reservoir characteristics; wherein the other reservoir characteristics include remaining reservoir characteristics other than the experimental fractal dimension.
2. The method of claim 1, wherein building a fractal dimension relationship model between porosity, permeability, logging data information, and fractal dimension based on rock types based on the fractal dimension predictions and other reservoir characteristics, comprises:
establishing a porosity relation model based on the porosity of the rock type and the logging data information and a permeability relation model based on the permeability of the rock type and the logging data information according to the rock type, the experimental porosity, the experimental permeability and the logging data information;
and establishing a fractal dimension relation model according to the fractal dimension prediction result, the porosity relation model and the permeability relation model.
3. The method of claim 2, wherein building a fractal dimension relationship model from the fractal dimension prediction result, the porosity relationship model, and the permeability relationship model comprises:
Determining a fractal dimension average value and an average range of the target rock type according to the fractal dimension prediction result;
determining a candidate fractal dimension relationship result of the target rock type according to the fractal dimension average value and the fractal dimension average range of the target rock type, the porosity relationship model and the permeability relationship model;
and determining a fractal dimension relation model according to the candidate fractal dimension relation result of the target rock type and the comparison result of reservoir characteristics of the sample dolomite reservoir.
4. The method of claim 3, wherein determining a fractal dimension relationship model based on a comparison of the candidate fractal dimension relationship result for the target rock type and reservoir characteristics of the sample dolomite reservoir comprises:
determining a comparison result according to the candidate fractal dimension relation result of the target rock type and the difference degree of the experimental fractal dimension, the experimental porosity, the experimental permeability and the logging data information of the sample dolomite reservoir of the target rock type;
and if the comparison result is that the difference degree is larger than the difference threshold value, screening the sample dolomite reservoir, retraining according to reservoir characteristics of the screened sample dolomite reservoir, obtaining updated fractal dimension classification results, and determining a fractal dimension relation model again according to the updated fractal dimension classification results and the reservoir characteristics of the screened sample dolomite reservoir.
5. The method of claim 4, wherein after determining the comparison result, the method further comprises:
and if the comparison result is that the difference degree is smaller than or equal to the difference threshold value, correcting the candidate fractal dimension relation result of the target rock type according to the reservoir characteristics of the sample dolomite reservoir to obtain a final fractal dimension relation model.
6. The method of claim 5, wherein prior to inputting the rock type in the reservoir signature and the experimental fractal dimension into a pre-set neural network model to obtain a fractal dimension classification result, the method further comprises:
extracting a sample dolomite reservoir with a preset proportion from the sample dolomite reservoir as a training dolomite reservoir, and the rest sample dolomite reservoirs as correction dolomite reservoirs so as to obtain fractal dimension classification results according to reservoir characteristics of the training dolomite reservoir;
correspondingly, correcting the candidate fractal dimension relationship result of the target rock type according to the reservoir characteristics of the sample dolomite reservoir, including:
and correcting the candidate fractal dimension relation result of the target rock type according to reservoir characteristics of the corrected dolomite reservoir.
7. The method according to any one of claims 1-6, characterized in that after establishing a fractal dimension relationship model between the fractal dimension prediction result and the reservoir characteristics based on the porosity, permeability, logging data information and fractal dimension of the rock type, the method further comprises:
acquiring target reservoir characteristics of a target dolomite reservoir; wherein the target reservoir characteristics include at least two of: rock type, experimental porosity, experimental permeability, and logging data information;
and determining the target fractal dimension of the target dolomite reservoir according to the target reservoir characteristics and the fractal dimension relation model.
8. A modeling apparatus for a fractal dimension of a dolomite reservoir, comprising:
the reservoir characteristic acquisition module is used for acquiring reservoir characteristics of the sample dolomite reservoir; wherein the reservoir characteristics at least comprise rock type, experimental fractal dimension, experimental porosity, experimental permeability and logging data information;
the classification result acquisition module is used for inputting the reservoir characteristics into a preset neural network model to obtain a fractal dimension classification result; the fractal dimension classification result refers to a fractal dimension prediction result based on rock types;
And the relation model building module is used for building a fractal dimension relation model between the porosity, permeability, logging data information and the fractal dimension based on the rock type according to the fractal dimension prediction result and the reservoir characteristics.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of modeling the dolomite reservoir fractal dimension of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to implement the method of modeling a fractal dimension of a dolomite reservoir according to any one of claims 1-7 when executed.
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