CN114462323A - Oil reservoir flow field characterization method based on multi-attribute field fusion - Google Patents
Oil reservoir flow field characterization method based on multi-attribute field fusion Download PDFInfo
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Abstract
The invention provides a reservoir flow field characterization method based on multi-attribute field fusion, which comprises the steps of reservoir flow capacity division and characterization; extracting characteristic parameters of an oil reservoir attribute field; constructing a fluid attribute potential energy field model; a fusion method, calculating a comprehensive potential energy field; and (4) converting the instantaneous potential energy field and performing vector characterization. The problems of insufficient fineness of the distribution and characteristics of the flow field of the oil field and the applicability of the oil field in the medium-high water cut period are solved by the method, and the vectorization of various instantaneous flow field attributes, flow directions and strength can be solved by the method. The method can be applied to the oil reservoir flow field characterization of various reservoir types. Compared with a common streamline numerical simulation method, the new method improves the streamline representation result to a single grid unit, and the oil reservoir flow field representation precision is greatly improved by fusing various attribute characteristics.
Description
Technical Field
The invention relates to the technical field of petroleum and natural gas, in particular to a reservoir flow field characterization method based on multi-attribute field fusion.
Background
The flow field reflects the spatial distribution of reservoir fluid in the underground porous medium due to fluid flow, the size and direction of the spatial distribution can be characterized by the strength of the flow field, and the change of the flow line of the flow field is characterized by evolution along with the production period and development. Therefore, in the initial development stage, due to the fact that the injection-production system is incomplete, streamline distribution is stable; the injection and production system gradually tends to be perfect in the middle stage of development, and additionally, the heterogeneity of the reservoir stratum and other geological oil reservoir characteristics are influenced, the heterogeneity of the flow field strength is gradually displayed, and dominant flow fields such as large channels and the like are gradually formed; and at the later stage of development, the problems of gradual and stable distribution of the strength and the direction of the flow field, small ineffective water circulation and spread degree and the like are gradually shown. At present, the flow field comprehensive characterization relates to a plurality of theoretical methods, and mainly comprises an index evaluation method, a flow performance prediction method, a flow potential characterization method and the like.
In the field production of the current flow field characterization technology, the flow field reforming and next step diving technology can provide important yield increase basis, but index evaluation methods in the process have many problems: firstly, screening of flow field evaluation indexes is not uniform, and indexes and screening methods for evaluating flow field distribution are more, including water content or water saturation, displacement multiple, flow field strength index, water injection efficiency, pore permeation parameters, water passing multiple and the like, so that the field popularization of a flow field characterization theory is difficult, and a related systematic system is not formed; at present, more parameter weight distribution methods aiming at flow field evaluation indexes are adopted, including an analytic hierarchy process, a neural network method and the like, but various methods are greatly influenced by subjectivity, so that the final evaluation method is unreasonable; at present, no better flow field grading method exists, especially for the grading of the strength of the flow field, the uncertainty is increased due to the limitation of the type and the boundary and the influence of subjective factors, and the flow field characterization and adjustment in the next step are limited. No matter the factors are static factors or dynamic factors for development, the single factor can represent the strength and the change of a fluid flow field only through the side surface and cannot comprehensively describe the flow field, so that parameter optimization is needed to comprehensively represent the oil reservoir flow field.
Regarding the aspect of flow property prediction, the mainstream method is the research of introducing the three-phase fluid potential of oil, gas and water into the action of the underground fluid, and has an important role in researching the reservoir oil and gas migration rule and the control action thereof. And judging the relationship between the oil and gas reservoir and the fluid migration track through the analysis of the fluid potential so as to determine whether the reservoir fluid is in or has a dominant path and a dominant pointing area in the fluid migration process and further judge the change of the fluid migration streamline. The fluid potential field and the fluid flow line are important forms for representing the spatial distribution characteristics of reservoir variables, and the change condition of the reservoir fluid flow line can be effectively represented by reservoir pressure energy, fluid potential energy, fluid kinetic energy, interface potential energy and the like according to a classical fluid potential theory and by combining an oil-gas-water three-phase fluid potential calculation formula. However, at present, the fluid potential is still drawn in the aspect of formula derivation, and the research for representing the reservoir flow field strength by establishing a potential energy model is less.
The reservoir flow unit plays an important guiding role in the aspects of understanding reservoir seepage rule, residual oil distribution and the like in deepening, and the current flow potential characterization related research methods can be roughly summarized as the following methods: firstly, a stratum method is carried out in sequence; clustering mathematical analysis; reservoir geological modeling; and fourthly, a geophysical method and the like. The research of the flow unit under different methods has important significance on the research thought and precision of the flow unit, but the applicability of each method to specific problems needs to be continuously checked and verified. There are also many problems in current flow cell research, and since understanding and understanding of the reservoir flow cell concept is not uniform yet, it is also different when selecting the partitioning parameters. Meanwhile, the research is mostly based on statistical cluster analysis, and the current deterministic formula and the partitioning method are few. In addition, the flow unit research method based on the mathematical means has high quantification degree, but the data selection requirement of quantitative analysis is high, and the three-dimensional prediction characterization research is less.
Disclosure of Invention
The invention provides an oil reservoir flow field characterization method based on multi-attribute field fusion, which overcomes the defects in the prior art, namely the problems that the traditional streamline simulation cannot carry out vectorization when representing the three-dimensional space of an injection and production streamline of an oil field, and the characterization precision of flow attributes among injection and production wells is insufficient. The vectorization of various instantaneous flow field attributes, flow directions and intensities can be solved through the method, the fineness of the distribution and characteristics of the oil reservoir flow field is improved, and the method can be applied to the oil reservoir flow field representation of various reservoir types.
The purpose of the invention is realized by the following technical scheme.
A reservoir flow field characterization method based on multi-attribute field fusion is carried out according to the following steps:
step 1, reservoir mobility division and characterization: calculating a reservoir quality index by porosity and permeability of a target reservoir small layer, dividing the reservoir flow unit index by using a K value clustering method according to the average pore throat radius, the logging phase characteristics and the reservoir quality index, and further, interpolating and representing an average pore throat radius model and a reservoir mobility model by using a geostatistical method;
step 2, extracting characteristic parameters of the oil reservoir property field: based on an ECLIPSE software FRONTSIM module, further extracting attributes such as the central altitude, the initial flow pressure, the initial flow speed, the initial saturation and the like of the grid unit on the basis of oil reservoir streamline simulation, and converting the initial saturation of the grid into an initial grid density model;
step 3, constructing a fluid attribute potential energy field model: according to the normalization idea, the initial flow velocity, the water saturation, the gravity potential energy field, the pressure potential energy field, the kinetic energy potential energy field and the interface potential energy field are subjected to dimensionless processing, and an initial model is extracted in a positive and negative direction standardization mode by considering the potential energy to the oil reservoir development power and resistance through a potential energy formula;
in the process of representing the comprehensive flow field developed by the oil field in the high water cut period, introducing a fluid potential calculation formula, extracting a streamline numerical model to obtain a saturation model, converting the saturation model into a density model, fusing an average pore throat radius model, and further calculating a gravity potential energy field and an interface potential energy field by using a geological modeling method; meanwhile, extracting instantaneous pressure, instantaneous conductivity and instantaneous flow velocity according to a streamline numerical simulation model, further calculating a pressure potential energy field and a velocity potential energy field by using a geological modeling method, taking positive values for pressure potential energy and gravitational potential energy velocity potential energy of a flow driving force, taking negative values for an interface potential energy field of flow resistance, and carrying out weighted calculation on a related standardized field model.
And 4, fusing the method, calculating the comprehensive potential energy field: on the basis of the standard potential field attribute model obtained in the step 2 and the step 3, obtaining a reasonable weight set of 7 parameter values by using an orthogonal analysis method and a fuzzy entropy weight method, obtaining a comprehensive potential field set by weighting calculation, comparing the coincidence rate of the comprehensive potential field set with a reservoir mobility model, and screening orthogonal scheme parameters and models with high coincidence degree;
and 5, instantaneous potential energy field conversion and vector characterization: according to the screening result in the step 4, parameters such as initial grid pressure, flow velocity, flow pressure, fluid density, conductivity and the like are iteratively updated to corresponding instantaneous parameters of an oil reservoir digital-analog model, the initial comprehensive model is converted into an instantaneous comprehensive model according to orthogonal scheme weight parameters with high goodness of fit, and finally the magnitude and the direction of the oil reservoir flow field strength with time-sequence change are respectively obtained through the instantaneous comprehensive model and the instantaneous flow velocity model;
wherein the standardized attribute field model fuses the solution of the weight parameters. Analyzing fusion factors influencing the comprehensive attribute field according to orthogonal experimental design, applying a 7-factor 3 horizontal orthogonal design table, performing weighted calculation on different attribute models according to different weights of each factor of the design table, determining an initial fusion model of a design scheme, and completing 18 corresponding fusion models; firstly, counting the number of the utilization grids of different fusion models and calculating the target utilization rate on the basis of the comparison of reservoir flow unit models; secondly, according to a fuzzy mathematics entropy weight method, a parameter set evaluation matrix of the 7 attribute parameters and the flow unit model combined by using the grid rate is established, the sensitivity relation between the 7 attribute parameters and the grid activity rate is analyzed, 18 groups of orthogonal design comprehensive evaluation scores are obtained, and sequencing is carried out according to the height of the comprehensive evaluation scores, so that the experimental scheme with the highest coincidence rate and the corresponding fusion weight are determined.
The invention has the beneficial effects that: compared with the two separate methods, the method has the advantages that the consideration factors are more comprehensive, the method is more suitable for the flow field characteristics of the oil reservoir in the medium and high water cut periods, the characterization precision can reach the single grid level, and the characterization precision of the flow field prediction is improved.
Drawings
FIG. 1 is a graph illustrating a part of a digital-to-analog attribute model normalization in an embodiment of the present invention;
FIG. 2 is a partial attribute model weighted fusion graph according to an embodiment of the present invention;
FIG. 3 is a three-dimensional vector diagram of a weighted fusion model according to an embodiment of the present invention;
FIG. 4 is a three-dimensional diagram of a conventional streamline numerical simulation in an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further illustrated by the following specific examples.
Example (b): b, oil field oil deposit flow field characterization calculation:
the oil field B enters a medium-high water-cut period, the comprehensive water content of the oil field B exceeds 80%, the oil reservoir extraction degree is low, the residual oil is rich in types and is scattered in distribution, the reservoir plane heterogeneity is strong, the communication relation among injection and extraction wells is difficult to recognize, the plane injection and extraction contradiction is obvious, and the fine characterization and the improvement recognition of an oil reservoir flow field are urgently needed to be strengthened.
According to the step 1, calculating a reservoir quality index by explaining the porosity and permeability of the logging curve of each layer system; further, calculating a reservoir flow index according to the reservoir quality index; and (3) comprehensively analyzing the radius of the pore throat of the reservoir in the mercury intrusion test, extracting label parameters by combining a rock phase and a logging phase, establishing an index of a single well flow unit of the oil field B by using a parameter clustering method, and establishing three-dimensional geological modeling of the flow unit by using geostatistical modeling interpolation.
According to the step 2 and the step 3, an initial saturation field model, an initial flow velocity field and an initial pressure conductivity model are respectively extracted from the operation result of the reservoir streamline numerical simulation B, the saturation field model is converted into an initial density field model, the modeling of a gravity potential energy field, a pressure potential energy field, a kinetic energy potential energy field and an interface potential energy field is completed by using a three-dimensional geological modeling method, and all models are normalized as shown in figure 1.
According to the step 4, based on various normalized three-dimensional models, firstly, an orthogonal experiment design is utilized to design 7-factor 3-level orthogonal designs, as shown in table 1, the three levels respectively take values of 0.1, 0.5 and 0.9, three different grades of weights representing factors are obtained, and 18 groups of orthogonal design scheme weighted three-dimensional geological models are formed, as shown in fig. 2.
TABLE 1 orthogonal design details
According to the step 5, the weighting model is compared with the flow unit model, the weight value with the highest grid utilization rate of the flow unit model is determined, the weight value and the streamline numerical model are brought in, the initial model is updated, the time-series weight model is formed through expansion, and the three-dimensional representation of the flow strength of the oil reservoir I, J, K in the direction is completed, as shown in fig. 3.
Through the calculation method in the steps 1-5, the traditional streamline numerical simulation prediction result is compared with the multi-attribute fusion flow field representation prediction result, as shown in fig. 4. According to the comparison of actual oil reservoir data, the streamline numerical simulation can only integrally reflect the streamline space distribution of the layer system, the fusion characterization method can improve the precision of flow field characterization to a single grid, the plane characterization precision is improved to more than 10 times, the longitudinal characterization precision is improved to more than 15-20 times, and the flow strength and direction between wells can be reflected.
Compared with the traditional streamline simulation method, the novel method for representing the block oil reservoir flow field is found out that streamline numerical simulation can only integrally reflect the saturation of the layer system and the pressure space distribution according to the comparison of actual oil reservoir data, and the fusion representation method can further reflect the action of driving energy on oil reservoir development on the basis of reflecting single attributes such as flow speed and the like. Meanwhile, the method can further reflect the cause of the actual reservoir heterogeneous medium-high water-cut oil reservoir flow field through the maximum value proofreading of the fusion reservoir flow unit mobility rate, and the applicability is high.
The invention has been described in an illustrative manner, and it is to be understood that any simple variations, modifications or other equivalent changes which can be made by one skilled in the art without departing from the spirit of the invention fall within the scope of the invention.
Claims (7)
1. A reservoir flow field characterization method based on multi-attribute field fusion is characterized by comprising the following steps: the method comprises the following steps:
step 1, dividing and characterizing reservoir flow capacity;
step 2, extracting characteristic parameters of an oil reservoir property field;
step 3, constructing a fluid attribute potential energy field model;
step 4, a fusion method is adopted to calculate a comprehensive potential energy field;
and 5, converting the instantaneous potential energy field and performing vector characterization.
2. The reservoir flow field characterization method based on multi-attribute field fusion as claimed in claim 1, wherein: the specific steps of the step 1 are that a reservoir quality index is calculated through porosity and permeability of a target reservoir small layer, a K value clustering method is used for dividing the reservoir flow unit index according to the average pore throat radius, the logging phase characteristics and the reservoir quality index, and furthermore, a geostatistical method is used for interpolating and representing an average pore throat radius model and a reservoir mobility model.
3. The reservoir flow field characterization method based on multi-attribute field fusion as claimed in claim 1, wherein: the specific step of the step 2 is to extract the attributes of the grid unit such as the central altitude, the initial flow pressure, the initial flow speed and the initial saturation on the basis of the oil reservoir streamline simulation based on an ECLIPSE software FRONTSIM module, and convert the grid initial saturation into a grid initial density model.
4. The reservoir flow field characterization method based on multi-attribute field fusion as claimed in claim 1, wherein: and 3, according to the normalization idea, carrying out dimensionless processing on the initial flow velocity, the water saturation, the gravity potential energy field, the pressure potential energy field, the kinetic energy potential energy field and the interface potential energy field, and taking the potential energy into consideration to develop the power and resistance of the oil reservoir through a potential energy formula to extract an initial model in a positive-negative standardized manner.
5. The reservoir flow field characterization method based on multi-attribute field fusion as claimed in claim 1, wherein: and the concrete steps of the step 4 are that a reasonable weight set of 7 parameter values is obtained by using an orthogonal analysis method and a fuzzy entropy weight method on the basis of the standard potential field attribute model, a comprehensive potential field set is obtained by weighting calculation, the coincidence rate of the comprehensive potential field set and the reservoir mobility model is compared, and orthogonal scheme parameters and models with high coincidence degree are screened.
6. The reservoir flow field characterization method based on multi-attribute field fusion as claimed in claim 1, wherein: and step 5, iteratively updating initial grid pressure, flow velocity, flow pressure, fluid density, conductivity and other parameters into corresponding instantaneous parameters of an oil reservoir digital-to-analog model, converting the initial comprehensive model into an instantaneous comprehensive model according to orthogonal scheme weight parameters with high goodness of fit, and finally respectively obtaining the magnitude and direction of the oil reservoir flow field strength with time-sequence change through the instantaneous comprehensive model and the instantaneous flow velocity model.
7. The application of the method for characterizing the oil deposit flow field based on the multi-attribute field fusion in the fine characterization of the high-water-cut period flow field in the oil deposit according to any one of claims 1 to 6.
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| CN117521529B (en) * | 2024-01-04 | 2024-03-29 | 中国石油大学(华东) | Learning dynamic analysis method of injection and production split machine embedded with physical meaning |
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