CN113642656B - Method and related device for determining exploitation mode of hypotonic sandstone reservoir - Google Patents

Method and related device for determining exploitation mode of hypotonic sandstone reservoir Download PDF

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CN113642656B
CN113642656B CN202110949666.1A CN202110949666A CN113642656B CN 113642656 B CN113642656 B CN 113642656B CN 202110949666 A CN202110949666 A CN 202110949666A CN 113642656 B CN113642656 B CN 113642656B
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陈浩
蒋东梁
邢建鹏
王群一
商琳
孙彦春
崔健
赵耀
左名圣
杨明洋
于海增
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China University of Petroleum Beijing
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Abstract

The application discloses a method and a device for determining a low-permeability sandstone reservoir mining mode, electronic equipment and a readable storage medium, wherein the method comprises the following steps: acquiring a numerical value of a main control factor of a hypotonic sandstone reservoir to be classified, wherein the main control factor of the hypotonic sandstone reservoir is determined and obtained based on the relevance between each physical characteristic of the hypotonic sandstone reservoir and a preset core index, and the preset core index is determined and obtained according to the permeability, the porosity and the starting pressure gradient of the hypotonic sandstone reservoir; the numerical value of each main control factor is brought into a preset comprehensive classification algorithm, and an actual quantized value is calculated; determining target categories corresponding to target numerical value intervals according to the target numerical value intervals to which the actual quantized values belong, wherein the numerical value intervals of different categories are determined by combining a clustering algorithm of a given clustering center number with probability distribution based on the actual quantized values of the sample hypotonic sandstone reservoir; and mining the hypotonic sandstone reservoir to be classified according to the mining mode corresponding to the target category. The method can more accurately determine the corresponding exploitation mode.

Description

Method and related device for determining exploitation mode of hypotonic sandstone reservoir
Technical Field
The present application relates to the field of request processing, and in particular, to a method and apparatus for determining a mining mode of a hypotonic sandstone reservoir, an electronic device, and a computer readable storage medium.
Background
The accurate classification of reservoirs is the basis for the exploration and development of oil and gas fields. The hypotonic sandstone oil in China has the advantages of rich resources, wide distribution, huge resources and wide exploration prospect, and becomes a research hotspot.
Compared with conventional oil reservoirs, hypotonic sandstone commonly undergoes destructive diagenetic effects such as strong compaction, cementing, and the like, and is prone to forming smaller pores and complex pore-throat connections. Reservoir quality is the key to low permeability dense sandstone reservoir evaluation and determines reservoir oil content and capacity. However, these reservoirs typically have very poor porosity and permeability relationships. For example, a reservoir with lower porosity may have higher permeability, while a reservoir with higher porosity may exhibit ultra-low permeability.
Therefore, the conventional reservoir classification method is not suitable for evaluating and classifying the hypotonic sandstone reservoirs, so that how to accurately classify the hypotonic sandstone reservoirs suitable for different exploitation modes more scientifically and reasonably is a problem to be solved urgently by those skilled in the art.
Disclosure of Invention
The application aims to provide a method and a device for determining a low-permeability sandstone reservoir mining mode, electronic equipment and a computer-readable storage medium.
To achieve the above object, the present application provides in a first aspect a method for determining a mining mode of a hypotonic sandstone reservoir, the method comprising:
acquiring a numerical value of a main control factor of a hypotonic sandstone reservoir to be classified; the method comprises the steps that a main control factor of a hypotonic sandstone reservoir is determined and obtained based on the relevance between each physical characteristic of the hypotonic sandstone reservoir and a preset core index, and the preset core index is determined and obtained according to the permeability, the porosity and the starting pressure gradient of the hypotonic sandstone reservoir;
the numerical value of each main control factor is brought into a preset comprehensive classification algorithm, and an actual quantized value is calculated;
determining a target category corresponding to the target numerical value interval according to the target numerical value interval to which the actual quantized value belongs; the numerical intervals of different categories are determined based on actual quantized values of the sample hypotonic sandstone reservoir through a clustering algorithm of a given clustering center number and probability distribution;
mining the low-permeability sandstone reservoir to be classified according to a mining mode corresponding to the target category; wherein, the exploitation mode corresponding to each type of hypotonic sandstone reservoir is preset.
Optionally, determining the preset core index according to the permeability, the porosity and the starting pressure gradient of the hypotonic sandstone reservoir includes:
the preset core index is calculated by the following formula:
wherein GFZI refers to a preset core index, K refers to the permeability of the hypotonic sandstone reservoir, phi refers to the porosity of the hypotonic sandstone reservoir, and S refers to the initiation pressure gradient of the hypotonic sandstone reservoir fluid.
Optionally, determining the master factor based on the correlation of each physical characteristic of the hypotonic sandstone reservoir with the preset core index includes:
calculating the association degree of each physical characteristic of the sample hypotonic sandstone reservoir and a preset core index respectively;
and selecting a physical characteristic with the highest association degree from each aspect of the size of the throat of the reservoir, the fluid flow capacity, the structure of the reservoir and the difficulty level of displacement as a main control factor.
Optionally, determining the numerical intervals of the hypotonic sandstone reservoirs of different categories based on the actual quantized values of the sample hypotonic sandstone reservoirs through a clustering algorithm with a given clustering center number and probability distribution comprises the following steps:
calculating a first quantized value of each sample hypotonic sandstone reservoir according to a comprehensive classification algorithm;
Generating an increment sample and a second quantized value corresponding to the increment sample according to probability distribution of the first quantized value;
inputting quantized values of the original sample hypotonic sandstone reservoir and the incremental sample hypotonic sandstone reservoir into a K-means clustering algorithm with the clustering center number of 3, and determining three continuous value intervals; wherein each numerical interval corresponds to a class of hypotonic sandstone reservoir.
Optionally, the method for determining the exploitation mode of the hypotonic sandstone reservoir further comprises the following steps:
and generating an abnormal exploitation alarm through a preset path according to exploitation abnormal feedback received in an actual exploitation stage.
In order to achieve the above object, the present application provides in a second aspect a low permeability sandstone reservoir mining mode determining device, comprising:
the main control factor value acquisition unit is used for acquiring the values of main control factors of the hypotonic sandstone reservoir to be classified; the method comprises the steps that a main control factor of a hypotonic sandstone reservoir is determined and obtained based on the relevance between each physical characteristic of the hypotonic sandstone reservoir and a preset core index, and the preset core index is determined and obtained according to the permeability, the porosity and the starting pressure gradient of the hypotonic sandstone reservoir;
the actual quantized value calculation unit is used for bringing the numerical value of each main control factor into a preset comprehensive classification algorithm to calculate an actual quantized value;
The target class determining unit is used for determining a target class corresponding to the target numerical value interval according to the target numerical value interval to which the actual quantized value belongs; the numerical intervals of different categories are determined based on actual quantized values of the sample hypotonic sandstone reservoir through a clustering algorithm of a given clustering center number and probability distribution;
the exploitation unit is used for exploitation of the low-permeability sandstone reservoir to be classified according to the exploitation mode corresponding to the target class; wherein, the exploitation mode corresponding to each type of hypotonic sandstone reservoir is preset.
Optionally, the device for determining the exploitation mode of the hypotonic sandstone reservoir further comprises: a core indicator determination unit for jointly determining a preset core indicator according to the permeability, the porosity and the starting pressure gradient of the hypotonic sandstone reservoir, wherein the core indicator determination unit comprises:
the calculating subunit according to the formula is used for calculating and obtaining a preset core index according to the following formula:
wherein GFZI refers to a preset core index, K refers to the permeability of the hypotonic sandstone reservoir, phi refers to the porosity of the hypotonic sandstone reservoir, and S refers to the initiation pressure gradient to the hypotonic sandstone reservoir fluid.
Optionally, the device for determining the exploitation mode of the hypotonic sandstone reservoir further comprises: a main control factor determining unit for determining a main control factor based on the relevance of each physical characteristic of the hypotonic sandstone reservoir and a preset core index, wherein the main control factor determining unit comprises:
The association degree calculating subunit is used for calculating the association degree of each physical characteristic of the sample hypotonic sandstone reservoir and a preset core index respectively;
the four-aspect selecting subunit is used for selecting a physical characteristic with highest association degree from each aspect of the size of the throat of the reservoir, the fluid flow capacity, the structure of the reservoir and the difficulty level of displacement as a main control factor.
Optionally, the device for determining the exploitation mode of the hypotonic sandstone reservoir further comprises: a numerical interval determining unit for determining numerical intervals of the hypotonic sandstone reservoirs of different categories based on the actual quantized values of the sample hypotonic sandstone reservoirs through a clustering algorithm of a given clustering center number and a probability distribution, wherein the numerical interval determining unit comprises:
the first quantized value calculating subunit is used for calculating the first quantized value of each sample hypotonic sandstone reservoir according to a comprehensive classification algorithm;
a second quantized value calculating subunit, configured to generate an incremental sample and a second quantized value corresponding to the incremental sample according to the probability distribution of the magnitude of each first quantized value;
the continuous value interval determining subunit is used for inputting quantized values of the original sample hypotonic sandstone reservoir and the incremental sample hypotonic sandstone reservoir into a K-means clustering algorithm with the clustering center number of 3 to determine three continuous value intervals; wherein each numerical interval corresponds to a class of hypotonic sandstone reservoir.
Optionally, the device for determining the exploitation mode of the hypotonic sandstone reservoir further comprises:
and the abnormal mining alarm generating unit is used for generating an abnormal mining alarm through a preset path according to mining abnormal feedback received in the actual mining stage.
To achieve the above object, the present application provides, in a third aspect, an electronic apparatus comprising:
a memory for storing a computer program;
a processor for implementing the steps of the method for determining a low permeability sandstone reservoir mining mode as described in any of the embodiments of the first aspect above, when executing a computer program stored on a memory.
To achieve the above object, the present application provides in a fourth aspect a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for determining a low permeability sandstone reservoir mining mode as described in any of the embodiments of the first aspect.
Compared with the prior art, the method for determining the mining mode of the hypotonic sandstone reservoir, provided by the application, has the advantages that on the basis of determining the preset core index according to the permeability and the porosity of the hypotonic sandstone reservoir in a conventional manner, the additional newly added starting pressure gradient parameter is added, so that the actual influence of the starting pressure gradient on the comprehensive evaluation is fully considered, different hypotonic sandstone reservoirs can be more comprehensively and accurately distinguished, the comprehensive classification algorithm based on the relevance determination can be more accurate under the condition that the preset core index is more accurate, the problem of insufficient sample size is well solved under the condition that the clustering algorithm of the given clustering center number is combined with probability distribution, the classification accuracy is improved as much as possible, and the accurate mining mode can be determined.
The application also provides a device for determining the exploitation mode of the hypotonic sandstone reservoir, electronic equipment and a computer readable storage medium, which have the beneficial effects and are not repeated here.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for determining a low permeability sandstone reservoir mining mode according to an embodiment of the present application;
FIG. 2 is a flowchart of a method for determining a main control factor in a method for determining a mining mode of a hypotonic sandstone reservoir according to an embodiment of the present application;
FIG. 3 is a flowchart of a method for determining the value intervals corresponding to different types of hypotonic sandstone reservoirs in a method for determining the exploitation mode of the hypotonic sandstone reservoirs according to an embodiment of the present application;
FIG. 4 is a graph showing the relationship between the index of a gray flow cell and the median pressure and median radius according to an embodiment of the present application;
FIG. 5a is a schematic diagram showing the relationship between the saturation of the movable fluid and the comprehensive classification index Z according to an embodiment of the present application;
FIG. 5b is a schematic diagram showing the relationship between the displacement pressure and the comprehensive classification index Z according to the embodiment of the present application;
FIG. 5c is a schematic diagram showing the relationship between the sorting coefficient and the comprehensive sorting index Z according to the embodiment of the present application;
FIG. 5d is a schematic diagram showing the relationship between the radius of the main flow throat and the comprehensive classification index Z according to the embodiment of the present application;
FIG. 6 is a graph showing the cumulative probability distribution of the comprehensive classification index Z provided by the embodiment of the application;
FIG. 7 is a graph showing a comparison between classification limits of a conventional clustering method and a clustering algorithm after combining probability distribution, which is provided by an embodiment of the present application;
fig. 8 is a block diagram of a device for determining a mining mode of a hypotonic sandstone reservoir according to an embodiment of the present application.
Detailed Description
The application aims to provide a method, a system, a device and a computer readable storage medium for determining the exploitation mode of a hypotonic sandstone reservoir, which are used for solving the problem of insufficient sample size in a mode of combining probability distribution by a clustering algorithm with a given clustering center number, and improving the classification accuracy as much as possible so as to determine the accurate exploitation mode by fully considering the actual influence of the start pressure gradient on the comprehensive assessment.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, fig. 1 is a flowchart of a method for determining a mining mode of a hypotonic sandstone reservoir according to an embodiment of the present application, which includes the following steps:
step 101: acquiring a numerical value of a main control factor of a hypotonic sandstone reservoir to be classified;
the method comprises the step of acquiring the numerical value of the main control factor of the actual hypotonic sandstone reservoir to be classified by an execution main body (such as a local server or a cloud server for data processing and analysis) suitable for executing the hypotonic sandstone reservoir mining mode determination method.
The main control factors of the hypotonic sandstone reservoir are determined based on the relevance between each physical characteristic of the hypotonic sandstone reservoir and a preset core index, namely the main control factors are certain physical characteristics with higher relevance to the preset core index, and the preset core index is an evaluation index for comprehensively representing different types of hypotonic sandstone reservoirs, namely the preset core index is expected to comprehensively represent the effective seepage capacity and the storage space of the hypotonic sandstone reservoir, wherein the preset core index is determined according to the permeability, the porosity and the starting pressure gradient of the hypotonic sandstone reservoir. Under the conventional mode, the core index only has determined permeability and porosity, but the hypotonic sandstone reservoir is special in property and has poor correlation of permeability and porosity, so that the core index under the conventional mode cannot have a better classification effect. In order to solve the problem, the application additionally increases the parameter of starting pressure gradient which can better distinguish different types of hypotonic sandstone reservoirs on the basis of permeability and porosity to jointly form the preset core index.
The hypotonic sandstone reservoir has a plurality of different physical properties, such as permeability and porosity which can be measured by a pulse permeability meter, different hypotonic core starting pressure gradients which can be obtained by using a starting pressure gradient test, different hypotonic core movable fluid saturation which can be measured by using nuclear magnetism, different hypotonic core clay content which can be determined by mineral quantitative analysis, and different hypotonic core main flow throat radius, average throat radius, maximum throat radius, sorting coefficient, homogeneity coefficient, median pressure, median radius, displacement pressure, mercury removal efficiency, structural coefficient, characteristic structural coefficient, lithology coefficient and the like which can be obtained by using a constant-speed mercury-pressing experiment.
The pressure gradient is started, and when fluid seeps in the low-permeability oil reservoir, an additional pressure gradient is needed to overcome the resistance caused by the adsorption film or the hydration film on the surface of the rock so as to flow, and the better the physical property of the oil reservoir is, the smaller the resistance is, so that the parameter is increased when the core index is constructed.
Step 102: the numerical value of each main control factor is brought into a preset comprehensive classification algorithm, and an actual quantized value is calculated;
based on step 101, the present step aims to bring the numerical value of each main control factor into a preset comprehensive classification algorithm by the execution subject, and calculate an actual quantized value capable of being represented in a quantized form. The comprehensive classification algorithm is a calculation mode capable of comprehensively reflecting the influence of each main control factor on the final actual quantized value as much as possible, and because the main control factors are usually multiple in number and possibly involve multiple evaluation dimensions, a weighted thought can be introduced to construct the comprehensive classification algorithm, and the specific structure of the algorithm can be obtained by itself under the guidance of the construction thought according to the actual situation, and is not particularly limited herein.
An implementation, including but not limited to, may be:
the comprehensive classification algorithm can be expressed as the formula: z=m·g, where Z represents a comprehensive classification index, M represents a multiple evaluation index, G represents a gray evaluation index, i.e., the comprehensive classification index Z is the product of the multiple evaluation index M and the gray evaluation index G, i.e., the comprehensive classification index is calculated by multiplying two sub-indexes.
Specifically, the multivariate evaluation index M may specifically express the following formula:
wherein a is 1 、a 2 …a n A is a main control factor value positively related to reservoir physical properties 1max 、a 2max …a nmax Is the maximum value of the main control factors positively related to the physical properties of the reservoir; b 1 、b 2 …b n B is a master factor value negatively related to reservoir physical properties 1max 、b 2max …b nmax Is the maximum value of the main control factors positively related to the physical properties of the reservoir.
The gray evaluation index G can be expressed specifically as the following formula:
wherein, c 1 、c 2 …c n Normalized value for master control factor, r c1 、r c2 …r cn Is the weight of the master control factor.
It should be noted that, in the above formula, the multiple evaluation index M and the gray evaluation index G may be actually used as the integrated classification index independently, and in the above embodiment, the multiple evaluation index M and the gray evaluation index G are used as the integrated classification index simultaneously in a product manner, so as to improve the accuracy of the integrated classification index Z by combining two calculation manners as much as possible.
Step 103: determining a target category corresponding to the target numerical value interval according to the target numerical value interval to which the actual quantized value belongs;
based on step 102, this step aims at determining, by the execution body, a target category corresponding to the target value interval according to the target value interval to which the actual quantized value belongs. The numerical intervals of different categories are determined based on actual quantized values of the sample hypotonic sandstone reservoir through a clustering algorithm of a given clustering center number and probability distribution.
The clustering algorithm is used for determining the classification boundary when different samples are clustered near the clustering center of a given clustering center number in a clustering mode, and the aim of combining probability distribution is to solve the problem of misjudgment caused by discontinuous classification boundary due to small sample total amount caused by large sample acquisition difficulty, so that sample increment is tried to be performed as far as possible through the probability distribution condition of the actual quantized value of the sample, and the continuous classification boundary is obtained through the clustering algorithm by combining the actual quantized value of the sample after increment.
Step 104: and mining the hypotonic sandstone reservoir to be classified according to the mining mode corresponding to the target category.
On the basis of step 103, the present step aims at mining the hypotonic sandstone reservoir to be classified according to the mining mode corresponding to the target category by the execution subject, that is, the corresponding relation between the hypotonic sandstone reservoir of each category and different mining modes is preset. For example, the exploitation mode of water injection corresponding to a conventional hypotonic sandstone reservoir with good physical properties, the exploitation mode of gas injection corresponding to an ultra-hypotonic sandstone reservoir with poor physical properties, and the exploitation mode of throughput corresponding to an ultra-hypotonic reservoir with worst physical properties.
Compared with the prior art, the method for determining the low-permeability sandstone reservoir mining mode, provided by the application, has the advantages that on the basis of determining the preset core index according to the permeability and the porosity of the low-permeability sandstone reservoir in a conventional manner, the additional newly-increased starting pressure gradient parameter is added, so that the actual influence of the starting pressure gradient on the comprehensive evaluation is fully considered, different low-permeability sandstone reservoirs can be more comprehensively and accurately distinguished, the comprehensive classification algorithm determined based on the association degree is more accurate under the condition that the preset core index is more accurate, the problem of insufficient sample size is well solved under the condition that the clustering algorithm of the given clustering center number is combined with probability distribution, the classification accuracy is improved as much as possible, and the accurate mining mode is determined.
Further, an abnormal mining alert may also be generated via a preset path based on mining anomaly feedback received during the actual mining phase. Specifically, the preset path may be expressed in different forms according to channels provided in actual application scenarios, such as an audible and visual alarm, an interface popup window, an alarm short message, an alarm mail, and so on.
On the basis of the above embodiment, the present embodiment provides a flowchart of a method for determining a master factor through fig. 2, which specifically includes the following steps:
step 201: determining a preset core index according to the permeability, the porosity and the starting pressure gradient of the hypotonic sandstone reservoir;
one implementation, including but not limited to, may derive a preferred pre-set core indicator by the following formula:
wherein GFZI refers to a preset core index, K refers to the permeability of the hypotonic sandstone reservoir, phi refers to the porosity of the hypotonic sandstone reservoir, S refers to the starting pressure gradient of the hypotonic sandstone reservoir fluid, wherein 0.0314 is a fixed value which is a uniform unit when the parameters participate in the operation, and isIs a similar value to (a) in the above.
Step 202: calculating the association degree of each physical characteristic of the sample hypotonic sandstone reservoir and a preset core index respectively;
In this embodiment, the calculation of the association degree follows a gray association method to calculate the gray association degree between each physical characteristic and GFZI. In addition, in some scenes, the association method which has the same or similar effect with the gray association method can be flexibly replaced according to actual conditions.
Step 203: and selecting a physical characteristic with the highest association degree from each aspect of the size of the throat of the reservoir, the fluid flow capacity, the structure of the reservoir and the difficulty level of displacement as a main control factor.
Based on step 202, the step aims to select the physical characteristic parameter set with the highest association degree from the four aspects of the size of the throat of the reservoir, the fluid flow capacity, the structure of the reservoir and the difficulty level of displacement, so that four parameters are selected as main control factors.
The correlation test data based on the primary samples can be seen in table 1 below:
TABLE 1 Gray correlation between physical Property parameters and GFZI
From table 1, it can be seen that the highest gray correlation among the above four aspects are the main flow throat radius, the movable fluid saturation, the sorting coefficient, and the displacement pressure, respectively.
On the basis of the above embodiment, the present embodiment provides a flowchart of a method for determining the value intervals corresponding to different types of hypotonic sandstone reservoirs respectively according to fig. 2, which specifically includes the following steps:
Step 301: calculating a first quantized value of each sample hypotonic sandstone reservoir according to a comprehensive classification algorithm;
the first quantized value is a quantized value Z calculated by the comprehensive classification algorithm of each sample hypotonic sandstone reservoir.
Step 302: generating an increment sample and a second quantized value corresponding to the increment sample according to probability distribution of the first quantized value;
based on step 301, this step aims to try to generate incremental samples from existing samples according to probability distribution situations of the magnitudes of the first quantized values, and further calculate second quantized values of the incremental samples.
Step 303: and inputting quantized values of the original sample hypotonic sandstone reservoir and the incremental sample hypotonic sandstone reservoir into a K-means clustering algorithm with the clustering center number of 3, and determining three continuous numerical intervals.
Wherein each numerical interval corresponds to a class of hypotonic sandstone reservoir.
In this embodiment, in combination with the number of mining modes that can be provided currently, the number of clustering centers is determined to be 3, and the goal of the sample increment is to enable the quantized values of the original sample hypotonic sandstone reservoir and the incremental sample hypotonic sandstone reservoir to be determined through a clustering algorithm, that is, assuming that the whole numerical interval is 1-100, then the three continuous numerical intervals are continuous, that is, the lower boundary numerical value and the upper boundary numerical value of the index adjacent numerical interval, and the whole numerical interval of 1-100 is completely divided into 3 parts.
In order to deepen the understanding of the technical scheme provided by the application, the application also provides a comprehensive classification evaluation mode of the hypotonic sandstone reservoir, which comprises the following steps of:
step one: establishing a hypotonic reservoir stratum core physical property database;
(1) Measuring different permeabilities and porosities by using a pulse permeameter;
(2) Obtaining different hypotonic core starting pressure gradients by using a starting pressure gradient test;
(3) Measuring the saturation of the movable fluid of different hypotonic cores by using nuclear magnetism;
(4) Determining the clay content of different hypotonic cores through quantitative analysis of minerals;
(5) Different low-permeability core main flow throat radius, average throat radius, maximum throat radius, sorting coefficient, homogeneity coefficient, median pressure, median radius, displacement pressure, mercury removal efficiency and structural coefficient are obtained through a constant-speed mercury-pressing experiment.
Table 2 hypotonic reservoir core analysis database
Sequence number Parameters (parameters) Data source Sequence number Parameters (parameters) Data source
1 Permeability of Pulse permeability meter 10 Homogeneity coefficient Constant speed mercury pressure test
2 Porosity of the porous body Pulse permeability meter 11 Sorting coefficient Constant speed mercury pressure test
3 Radius of main flow throat Constant speed mercury pressure test 12 Lithology coefficient Constant speed mercury pressure test
4 Coefficient of variation Constant speed mercury pressure test 13 Median pressure Constant speed mercury pressure test
5 Saturation of movable fluid Nuclear magnetic test 14 Median radius Constant speed mercury pressure test
6 Clay content Quantitative analysis of minerals 15 Pressure of exhaust Constant speed mercury pressure test
7 Average throat radius Constant speed mercury pressure test 16 Coefficient of structure Constant speed mercury pressure test
8 Maximum throat radius Constant speed mercury pressure test 17 Characteristic structural coefficient Constant speed mercury pressure test
9 Mercury removal efficiency Constant speed mercury pressure test 18 Initiating a pressure gradient Initiating a pressure gradient test
Step two: establishing gray flow cell indicators (i.e., GFZI in the above embodiments) that characterize the effective seepage capability and reservoir space of the low permeability reservoir;
(1) Establishing a gray flow unit index (GFZI) to comprehensively characterize the effective seepage capability and the reservoir space of the low-permeability reservoir;
GFZI was calculated as follows, as shown in fig. 4, and has a better correlation with median pressure, median radius, indicating that GFZI can comprehensively characterize the effective percolation capacity and reservoir space of a low permeability reservoir.
Wherein GFZI refers to a gray flow unit index, K refers to permeability, and the unit is mD;refer to porosity in decimal, S refers to the initiation pressure gradient in MPa/m.
Step three: the GFZI is used as a main sequence, gray correlation degrees of all influence factors are determined by using a gray correlation method, and the main control factors are evaluated by optimizing hypotonic reservoir classification in aspects of fluid flow capacity, displacement difficulty, rock structure and the like;
Determining the association degree of each parameter by a gray association method
The gray correlation analysis is a method for measuring the correlation degree among the factors according to the similarity degree or the difference degree of the development trend among the factors, the main sequence of the correlation analysis is to arrange one of the factors according to a certain sequence, the main sequence can reflect the property of the object to be judged, and the relation between the object to be judged and the influencing factors is analyzed and judged from the data information. The main sequence was gray-correlated with GFZI, and the magnitude of the correlation between each parameter and GFZI was determined as shown in table 1.
The main control factors are evaluated according to gray association degree from the aspects of fluid flow capacity, displacement difficulty, rock structure and the like by optimizing hypotonic reservoir classification, and the preferable results are as follows: the radius of the main flow throat, the saturation of the movable fluid, the separation coefficient and the displacement pressure.
Step four: respectively carrying out multi-element analysis and grey evaluation on the sample, and carrying out information superposition on the result to obtain a comprehensive classification index (Z) so as to complete comprehensive quantitative evaluation of the reservoir;
(1) The samples were evaluated by multiplex analysis according to the following formula:
wherein M represents a multiple evaluation index; r refers to the radius of the main flow throat, and the unit is mu m; rmax refers to the maximum characteristic throat radius in μm; so refers to the movable fluid saturation in units of; somax refers to the maximum movable fluid saturation in units of; f refers to a sorting coefficient; fmax refers to the maximum sorting coefficient, P refers to displacement pressure in MPa; pmax refers to the maximum displacement pressure in MPa.
(2) The samples were gray evaluated according to the following formula:
the weights of the main control factors are calculated according to the following formula, and the calculation results are shown in Table 3.
Where ri refers to the master factor weight and αi refers to the master factor gray correlation.
TABLE 3 Master factor weights
Master control factor Radius of main flow throat μm Displacement pressure MPa Saturation of mobile fluid% Sorting coefficient
Weighting of 0.257 0.248 0.242 0.243
Multiplying the normalized value of each main control factor by the weight according to the following formula, and then summing to obtain a gray evaluation index:
wherein G represents a gray evaluation index; c1 and c2 … cn are normalization values of the main control factors, and rc1 and rc2 … rcn are weights of the main control factors.
(3) Information superposition is carried out on the results according to the following formula to obtain a comprehensive classification index (Z) so as to complete comprehensive quantitative evaluation of the hypotonic reservoir stratum:
Z＝M·G
wherein Z refers to comprehensive classification indexes; m refers to a multi-element evaluation index; g represents a gray evaluation index.
As can be seen from the schematic diagrams shown in fig. 5a, 5b, 5c and 5d, the master factor has a good correlation with Z. The movable fluid saturation, displacement pressure, sorting coefficient and R2 value of the radius of the main flow throat are 0.8709, 0.7399, 0.9643 and 0.9155 respectively, which shows that Z can comprehensively and quantitatively represent the physical properties of the reservoir.
Step five: and combining a K-means clustering algorithm with probability distribution to determine a Z limit, and determining the limit of each main control factor according to the relation between each main control factor and the Z.
(1) As shown in fig. 5, the actual cumulative probability distribution of the comprehensive classification index Z is assigned according to the probability distribution characteristics of the comprehensive classification index (Z), and the number of assigned points is greater than 1000.
The K-means clustering algorithm is an iterative cluster analysis algorithm that divides M points in the N dimension into K clusters such that the sum of squares within the clusters is minimized. The data are divided into K groups, K objects are randomly selected as initial clustering centers, then the distance between each object and each seed clustering center is calculated, and each object is distributed to the nearest clustering center. The termination condition may be that no (or minimum) objects are reassigned to different clusters, that cluster centers are no longer changing, and that the square error and local minima.
The Z classification limit is determined by classifying the assignment data by using a K-means clustering algorithm, as shown in fig. 6, and compared with the traditional K-means clustering algorithm, the boundary of the K-means clustering algorithm combined with the probability distribution function is clearer.
(2) The classification boundaries of each master factor determined by the relationship between Z and each master factor are shown in Table 4:
TABLE 4 comprehensive classification system for hypotonic reservoirs
Parameters (parameters) Ⅰ Ⅱ Ⅲ Formula (VI)
Radius of main flow throat μm >0.85 0.53～0.85 <0.53 R＝0.9771Z+0.4043
Saturation of mobile fluid% >27.82 18.00～27.82 <18.00 S o ＝29.759Z+14.134
Displacement pressure MPa <0.35 0.35～0.44 >0.44 P＝0.3082Z -0.159
Sorting coefficient >0.49 0.28～0.49 <0.28 F＝0.6384Z+0.1984
Comprehensive classification index Z >0.46 0.13～0.46 <0.13 -
Subsequently, the classification of the hypotonic sandstone reservoir to be classified can be accurately determined by using the comprehensive classification evaluation mode, and the corresponding exploitation mode can be determined.
Namely, the embodiment can objectively and optimally select the main control factors of reservoir evaluation by establishing a gray flow unit index (GFZI) as a gray associated main sequence; respectively carrying out multi-component analysis and grey evaluation on the samples, and carrying out information superposition on the results to obtain comprehensive classification indexes (Z) so as to complete comprehensive quantitative evaluation on the hypotonic reservoir; the K-means clustering algorithm is combined with probability distribution, so that the classification boundary of each main control factor is clearly determined, and a hypotonic reservoir classification evaluation system is further established. Compared with the traditional quantitative reservoir classification technical scheme, the method can objectively optimize the hypotonic reservoir classification evaluation main control factors, can determine clear classification limits, improves the result accuracy, and has important significance for hypotonic reservoir classification development.
Because of the complexity and cannot be illustrated by one, those skilled in the art will recognize that many examples of the basic method principles provided in accordance with the present application may exist in combination with the actual situation, and should be within the scope of the present application without performing enough inventive effort.
Referring now to fig. 8, fig. 8 is a block diagram illustrating a low permeability sandstone reservoir mining mode determining apparatus 800 according to an embodiment of the present application, where the low permeability sandstone reservoir mining mode determining apparatus 800 may include:
a main control factor value obtaining unit 801, configured to obtain a value of a main control factor of a hypotonic sandstone reservoir to be classified; the method comprises the steps that a main control factor of a hypotonic sandstone reservoir is determined and obtained based on the relevance between each physical characteristic of the hypotonic sandstone reservoir and a preset core index, and the preset core index is determined and obtained according to the permeability, the porosity and the starting pressure gradient of the hypotonic sandstone reservoir;
an actual quantized value calculation unit 802, configured to bring the numerical value of each main control factor into a preset comprehensive classification algorithm, and calculate an actual quantized value;
a target class determining unit 803, configured to determine a target class corresponding to a target value interval to which the actual quantized value belongs according to the target value interval; the numerical intervals of different categories are determined based on actual quantized values of the sample hypotonic sandstone reservoir through a clustering algorithm of a given clustering center number and probability distribution;
A mining unit 804 according to a corresponding mining mode, configured to mine the low-permeability sandstone reservoir to be classified according to a mining mode corresponding to the target class; wherein, the exploitation mode corresponding to each type of hypotonic sandstone reservoir is preset.
Optionally, the apparatus 800 for determining a mining mode of a hypotonic sandstone reservoir may further include: a core indicator determination unit for jointly determining a preset core indicator according to the permeability, the porosity and the starting pressure gradient of the hypotonic sandstone reservoir, wherein the core indicator determination unit comprises:
the calculating subunit according to the formula is used for calculating and obtaining a preset core index according to the following formula:
wherein GFZI refers to a preset core index, K refers to the permeability of the hypotonic sandstone reservoir, phi refers to the porosity of the hypotonic sandstone reservoir, and S refers to the initiation pressure gradient to the hypotonic sandstone reservoir fluid.
Optionally, the device for determining the exploitation mode of the hypotonic sandstone reservoir may further include: a main control factor determining unit for determining a main control factor based on the relevance of each physical characteristic of the hypotonic sandstone reservoir and a preset core index, wherein the main control factor determining unit comprises:
the association degree calculating subunit is used for calculating the association degree of each physical characteristic of the sample hypotonic sandstone reservoir and a preset core index respectively;
The four-aspect selecting subunit is used for selecting a physical characteristic with highest association degree from each aspect of the size of the throat of the reservoir, the fluid flow capacity, the structure of the reservoir and the difficulty level of displacement as a main control factor.
Optionally, the device for determining the exploitation mode of the hypotonic sandstone reservoir may further include: a numerical interval determining unit for determining numerical intervals of the hypotonic sandstone reservoirs of different categories based on the actual quantized values of the sample hypotonic sandstone reservoirs through a clustering algorithm of a given clustering center number and a probability distribution, wherein the numerical interval determining unit comprises:
the first quantized value calculating subunit is used for calculating the first quantized value of each sample hypotonic sandstone reservoir according to a comprehensive classification algorithm;
a second quantized value calculating subunit, configured to generate an incremental sample and a second quantized value corresponding to the incremental sample according to the probability distribution of the magnitude of each first quantized value;
the continuous value interval determining subunit is used for inputting quantized values of the original sample hypotonic sandstone reservoir and the incremental sample hypotonic sandstone reservoir into a K-means clustering algorithm with the clustering center number of 3 to determine three continuous value intervals; wherein each numerical interval corresponds to a class of hypotonic sandstone reservoir.
Optionally, the device for determining the exploitation mode of the hypotonic sandstone reservoir may further include:
and the abnormal mining alarm generating unit is used for generating an abnormal mining alarm through a preset path according to mining abnormal feedback received in the actual mining stage.
Compared with the prior art, the low-permeability sandstone reservoir mining mode determining device provided by the application has the advantages that on the basis of determining the preset core index according to the permeability and the porosity of the low-permeability sandstone reservoir in a conventional manner, the parameter of the starting pressure gradient is additionally and newly increased, so that the actual influence of the starting pressure gradient on the comprehensive evaluation is fully considered, different low-permeability sandstone reservoirs can be more comprehensively and accurately distinguished, the comprehensive classification algorithm determined based on the association degree is more accurate under the condition that the preset core index is more accurate, the problem of insufficient sample size is well solved under the condition that the clustering algorithm of the given clustering center number is combined with the probability distribution, the classification accuracy is improved as much as possible, and the accurate mining mode can be determined.
Based on the above embodiment, the present application further provides an electronic device, where the electronic device may include a memory and a processor, where the memory stores a computer program, and the processor may implement the steps provided in the above embodiment when calling the computer program in the memory. Of course, the electronic device may also include various necessary network interfaces, power supplies, and other components, etc.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by an execution terminal or processor, performs the steps provided by the above embodiments. The storage medium may include: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In the description, each embodiment is described in a progressive manner, and each embodiment is mainly described by the differences from other embodiments, so that the same similar parts among the embodiments are mutually referred. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The principles and embodiments of the present application have been described herein with reference to specific examples, the description of which is intended only to facilitate an understanding of the method of the present application and its core ideas. It will be apparent to those skilled in the art that various changes and modifications can be made to the present application without departing from the principles of the application, and such changes and modifications fall within the scope of the appended claims.
It should also be noted that in this specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.

Claims (6)

1. A method for determining a low permeability sandstone reservoir mining mode, comprising:
acquiring a numerical value of a main control factor of a hypotonic sandstone reservoir to be classified; the method comprises the steps that a main control factor of a hypotonic sandstone reservoir is determined and obtained based on the relevance between each physical characteristic of the hypotonic sandstone reservoir and a preset core index, and the preset core index is determined and obtained according to the permeability, the porosity and the starting pressure gradient of the hypotonic sandstone reservoir;
the numerical value of each main control factor is brought into a preset comprehensive classification algorithm, and an actual quantized value is calculated;
determining a target category corresponding to the target numerical value interval according to the target numerical value interval to which the actual quantized value belongs; the numerical intervals of different categories are determined based on actual quantized values of the sample hypotonic sandstone reservoir through a clustering algorithm of a given clustering center number and probability distribution;
mining the low-permeability sandstone reservoir to be classified according to a mining mode corresponding to the target category; the exploitation modes corresponding to each type of hypotonic sandstone reservoir are preset;
wherein determining the predetermined core indicator according to the permeability, porosity and starting pressure gradient of the hypotonic sandstone reservoir comprises:
The preset core index is calculated by the following formula:
wherein GFZI refers to the preset core index, K refers to the permeability of the hypotonic sandstone reservoir, phi refers to the porosity of the hypotonic sandstone reservoir, S refers to the starting pressure gradient of the hypotonic sandstone reservoir fluid;
wherein determining the master factor based on the correlation of each physical characteristic of the hypotonic sandstone reservoir with a preset core index comprises:
calculating the association degree of each physical characteristic of the sample hypotonic sandstone reservoir and the preset core index respectively;
and selecting one physical characteristic with the highest association degree from each aspect of the size of the throat of the reservoir, the fluid flow capacity, the structure of the reservoir and the difficulty level of displacement as the main control factor.
2. The method of claim 1, wherein determining numerical intervals for different classes of hypotonic sandstone reservoirs based on a clustering algorithm that combines actual quantized values of sample hypotonic sandstone reservoirs with probability distributions by a given number of cluster centers, comprises:
calculating a first quantized value of each sample hypotonic sandstone reservoir according to the comprehensive classification algorithm;
generating an increment sample and a second quantized value corresponding to the increment sample according to the probability distribution of the first quantized value;
Inputting quantized values of the original sample hypotonic sandstone reservoir and the incremental sample hypotonic sandstone reservoir into a K-means clustering algorithm with the clustering center number of 3, and determining three continuous value intervals; wherein each numerical interval corresponds to a class of hypotonic sandstone reservoir.
3. The method according to claim 1 or 2, further comprising:
and generating an abnormal exploitation alarm through a preset path according to exploitation abnormal feedback received in an actual exploitation stage.
4. A hypotonic sandstone reservoir mining mode determining device, comprising:
the main control factor value acquisition unit is used for acquiring the values of main control factors of the hypotonic sandstone reservoir to be classified; the method comprises the steps that a main control factor of a hypotonic sandstone reservoir is determined and obtained based on the relevance between each physical characteristic of the hypotonic sandstone reservoir and a preset core index, and the preset core index is determined and obtained according to the permeability, the porosity and the starting pressure gradient of the hypotonic sandstone reservoir;
the actual quantized value calculation unit is used for bringing the numerical value of each main control factor into a preset comprehensive classification algorithm to calculate an actual quantized value;
the target class determining unit is used for determining a target class corresponding to the target numerical value interval according to the target numerical value interval to which the actual quantized value belongs; the numerical intervals of different categories are determined based on actual quantized values of the sample hypotonic sandstone reservoir through a clustering algorithm of a given clustering center number and probability distribution;
The exploitation unit is used for exploitation of the low-permeability sandstone reservoir to be classified according to the exploitation mode corresponding to the target category; the exploitation modes corresponding to each type of hypotonic sandstone reservoir are preset;
a core indicator determination unit for jointly determining the preset core indicator according to the permeability, the porosity and the starting pressure gradient of the hypotonic sandstone reservoir, wherein the core indicator determination unit comprises:
the calculating subunit according to a formula is used for calculating and obtaining the preset core index according to the following formula:
wherein GFZI refers to the preset core index, K refers to the permeability of the hypotonic sandstone reservoir, phi refers to the porosity of the hypotonic sandstone reservoir, S refers to the starting pressure gradient for the hypotonic sandstone reservoir fluid;
a main control factor determining unit for determining the main control factor based on the relevance of each physical characteristic of the hypotonic sandstone reservoir and a preset core index, wherein the main control factor determining unit comprises:
the association degree calculating subunit is used for calculating the association degree of each physical characteristic of the sample hypotonic sandstone reservoir and the preset core index respectively;
The four-aspect selecting subunit is configured to select, as the main control factor, one physical characteristic with the highest association degree from each aspect of the size of the throat of the reservoir, the fluid flow capacity, the structure of the reservoir, and the ease of displacement.
5. An electronic device, comprising:
a memory for a computer program;
a processor for implementing the steps of the hypotonic sandstone reservoir mining mode determination method of any of claims 1 to 3 when executing a computer program stored on the memory.
6. A readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the steps of the method for determining the exploitation pattern of a hypotonic sandstone reservoir according to any of claims 1 to 3.
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