CN113807359B - Intelligent identification method for inter-well communication path and electronic equipment - Google Patents
Intelligent identification method for inter-well communication path and electronic equipment Download PDFInfo
- Publication number
- CN113807359B CN113807359B CN202010555476.7A CN202010555476A CN113807359B CN 113807359 B CN113807359 B CN 113807359B CN 202010555476 A CN202010555476 A CN 202010555476A CN 113807359 B CN113807359 B CN 113807359B
- Authority
- CN
- China
- Prior art keywords
- image
- data
- pore
- path
- inter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000004891 communication Methods 0.000 title claims abstract description 30
- 230000009466 transformation Effects 0.000 claims abstract description 29
- 238000012545 processing Methods 0.000 claims abstract description 12
- 239000011148 porous material Substances 0.000 claims description 58
- 230000009969 flowable effect Effects 0.000 claims description 29
- 238000004422 calculation algorithm Methods 0.000 claims description 19
- 230000035699 permeability Effects 0.000 claims description 19
- 238000005516 engineering process Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000005260 corrosion Methods 0.000 claims description 6
- 230000007797 corrosion Effects 0.000 claims description 6
- 238000009825 accumulation Methods 0.000 claims description 3
- 238000009499 grossing Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 6
- 239000003921 oil Substances 0.000 description 18
- 238000010586 diagram Methods 0.000 description 16
- 238000004519 manufacturing process Methods 0.000 description 8
- 238000003860 storage Methods 0.000 description 7
- 238000002347 injection Methods 0.000 description 6
- 239000007924 injection Substances 0.000 description 6
- 239000000243 solution Substances 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 3
- 239000012530 fluid Substances 0.000 description 3
- 239000003208 petroleum Substances 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 239000000700 radioactive tracer Substances 0.000 description 3
- 238000011084 recovery Methods 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 230000002902 bimodal effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 239000010779 crude oil Substances 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 239000006185 dispersion Substances 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000003711 image thresholding Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000006798 recombination Effects 0.000 description 1
- 238000005215 recombination Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Marine Sciences & Fisheries (AREA)
- Mining & Mineral Resources (AREA)
- Agronomy & Crop Science (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Husbandry (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The embodiment of the invention discloses an intelligent identification method for an inter-well communication path and electronic equipment. The intelligent identification method for the communication path between wells comprises the following steps: acquiring image data to be identified, wherein the image data comprises inter-well communication paths; performing binarization processing on the image in the image data to obtain a binarized image; extracting the central axis of the flow path based on the binarized image to obtain a central axis transformation image; calculating the weight of the flow path; and searching a dominant path in the medial axis transformation image based on the weight, and reconnecting the break points in the medial axis transformation image. The image is subjected to binarization processing, the central axis of the flow path is extracted, the weight of the flow path is calculated, and the dominant path is searched based on the weight. The purpose of improving the recognition efficiency and effect is achieved.
Description
Technical Field
The invention belongs to the technical field of petroleum geology, and particularly relates to an inter-well communication path intelligent identification method and electronic equipment.
Background
At present, 70% of the petroleum output in China still comes from old oil fields, and the residual recoverable reserves are still quite considerable. At the same time, old oil fields have been developed for decades, and have generally entered the "double high" (high recovery, high water content) development stage. The central task of the high water content oil field is to improve the recovery ratio of crude oil, the key to improve the recovery ratio of crude oil is to accurately predict the relative enrichment area of residual oil, and the development of the oil field becomes a great challenge facing the petroleum in China. 92% of the oil field reservoirs are heterogeneous reservoirs, and the core of predicting the relative enrichment area of the residual oil of the oil field reservoirs is deepened oil field seismic description. The key to heterogeneous reservoir descriptions is the description of reservoir connectivity. Taking a fracture-cavity type oil reservoir as an example, the communication path identification of the oil reservoir is the basis for developing analysis of the fracture-cavity type reservoir body utilization condition, differential analysis of water (gas) injection effect, later adjustment countermeasures and three-dimensional injection well pattern construction.
At present, methods adopted for identifying communication paths among wells comprise a production dynamic data inversion technology, a tracer monitoring technology and an interference well test monitoring technology.
The production dynamic data inversion technology is based on production data of injection and production wells, analyzes the connectivity among wells, and analyzes the existence of multiple solutions due to complex injection and production well patterns; the tracer monitoring technology is more visual, but has high test cost and long test duration by observing the dynamic change of the tracer produced in the oil production well and researching and analyzing the connectivity between wells; the interference well test monitoring technology changes the pressure of an oil layer by changing a working system in an exciting well, monitors underground high-precision pressure gauge to measure bottom hole pressure data, and determines the connectivity between wells by observing whether the exciting well pressure change signal can be monitored or not.
In order to solve the technical problems, a more mature dynamic and static description technology of the oil reservoir body is adopted, but the identification of the communication path in the dynamic and static description technology of the oil reservoir body is mainly realized by adopting a manual identification method, and a communication diagram is drawn manually, so that the problems of low identification efficiency and poor effect exist.
Disclosure of Invention
Therefore, the embodiment of the invention provides an intelligent identification method for an inter-well communication path and electronic equipment, which at least solve the problems of low identification efficiency and poor effect in the prior art.
In a first aspect, an embodiment of the present invention provides an intelligent identification method for an inter-well communication path, including:
acquiring image data to be identified, wherein the image data comprises inter-well communication paths;
performing binarization processing on the image in the image data to obtain a binarized image;
Extracting the central axis of the flow path based on the binarized image to obtain a central axis transformation image;
calculating the weight of the flow path;
and searching a dominant path in the medial axis transformation image based on the weight, and reconnecting the break points in the medial axis transformation image.
Optionally, the searching for the dominant path in the medial axis transformation image based on the weight and reconnecting the breakpoint in the medial axis transformation image includes:
Searching a dominant path by adopting a Dijkstra algorithm;
And performing breakpoint reconnection on the areas which are not communicated in the medial axis transformation image by adopting a corrosion algorithm.
Optionally, the image data includes: seismic interval slice porosity data and well position coordinate data;
The seismic surface slice porosity data at least comprises 5 columns of data which are respectively used for representing image coordinates, geological coordinates and porosity;
The well location coordinate data at least comprises 4 columns of data which are respectively used for representing geological coordinates and pixel coordinate positions.
Optionally, after the step of acquiring the image data to be identified, the method further includes:
The acquired image data is subjected to inter-data interpolation,
The interpolation between the data is to insert corresponding image data between two adjacent slice image data.
Optionally, the binarizing processing is performed on the image in the image data, including:
counting pore pixels of each picture in the image data;
drawing a pixel histogram based on the aperture pixels;
moving averages smooth the histogram data;
and searching for the valley bottom value based on the histogram data after moving average smoothing, and taking the valley bottom value as a binarization threshold value.
Optionally, the extracting the central axis of the flow path based on the binarized image includes:
Extracting a flow center axis of the binarized image to obtain a refined path image, and describing a flow path while retaining the topological characteristics of a pore channel;
and extracting the central axis of the flow path from the thinned path image by adopting a central axis transformation technology.
Optionally, the calculating the weight of the flow path includes:
the weight of the flow path is the weight accumulation of a basic unit consisting of two vertexes and one edge, and the attribute of the vertexes comprises an overflow area Ai and a permeability ki;
the weighting mode of the overflow area Ai and the permeability ki is as follows:
optionally, calculating the overcurrent area Ai includes:
Splitting a flow pore and a non-flow pore in the medial axis transformation image according to a set threshold;
selecting a neighborhood template, searching whether a non-flowable pore exists in a neighborhood space, and if so, taking the distance of the non-flowable pore closest to a central target pore as the radius of a maximum sphere;
if not, increasing the radius of the neighborhood template, and continuously searching whether a non-flowable pore exists in the neighborhood space;
And using the maximum sphere radius as a neighborhood template radius to reuse the neighborhood template, searching all the flowable pores with the distances smaller than or equal to the maximum sphere radius in the neighborhood template, and taking the flowable pores as the flow coverage area of the flowable pores of the central target pore, and obtaining the overflow area based on the coverage area.
Optionally, the formula for solving the permeability K is specifically:
Phi represents porosity, K represents permeability, c represents a calculation constant, and D represents gravel diameter.
In a second aspect, an embodiment of the present invention further provides an electronic device, including:
A memory storing executable instructions;
and the processor runs the executable instructions in the memory to realize the intelligent identification method of the well communication path according to any one of the first aspect.
According to the invention, through binarization processing of the image, the central axis of the flow path is extracted, the weight of the flow path is calculated, and the dominant path is searched based on the weight. The purpose of improving the recognition efficiency and effect is achieved.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the invention.
FIG. 1 is a flow chart of a method for intelligently identifying communication paths between wells according to a second embodiment of the invention;
FIG. 2a is a schematic view of seismic slice along-layer porosity data according to an embodiment of the invention;
FIG. 2b is a schematic diagram of well position coordinate data according to an embodiment of the present invention;
FIG. 3a is a schematic diagram showing the result of full three-dimensional interpolation according to the first embodiment of the present invention;
FIG. 3b is a schematic diagram showing a two-dimensional interpolation result of a slice according to an embodiment of the present invention;
FIG. 4a is a diagram illustrating a binarization threshold according to an embodiment of the present invention;
FIG. 4b is a diagram showing the image thresholding binarization result according to the first embodiment of the present invention;
FIG. 5 is a schematic diagram showing the result of the medial axis transformation according to the first embodiment of the present invention;
FIG. 6a shows a many-to-many path finding schematic according to an embodiment of the invention;
FIG. 6b is a schematic diagram showing the path parameter results according to the first embodiment of the present invention;
FIG. 7a is a schematic view of a first original picture according to a second embodiment of the present invention;
FIG. 7b is a schematic diagram of an interpolated picture according to a second embodiment of the present invention;
FIG. 7c shows a second original schematic diagram according to a second embodiment of the present invention;
FIG. 8a is a schematic view of a gray scale image of an original aperture according to a second embodiment of the invention;
FIG. 8b is a schematic view of an image after binarization segmentation according to a second embodiment of the present invention;
FIG. 9a is a schematic diagram of a path image after refinement according to a second embodiment of the present invention;
FIG. 9b is a schematic diagram of a path image of a well name according to a second embodiment of the invention;
FIG. 10a shows a single width path schematic diagram according to a second embodiment of the invention;
FIG. 10b is a schematic diagram of a path restored by the maximum sphere method according to the second embodiment of the invention;
FIG. 11a is a schematic diagram showing the calculation result path and parameters according to the second embodiment of the invention;
fig. 11b shows a schematic diagram of a two-dimensional interpolation result of a slice according to a second embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below. While the preferred embodiments of the present invention are described below, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein.
An intelligent identification method for an inter-well communication path comprises the following steps:
acquiring image data to be identified, wherein the image data comprises inter-well communication paths;
performing binarization processing on the image in the image data to obtain a binarized image;
Extracting the central axis of the flow path based on the binarized image to obtain a central axis transformation image;
calculating the weight of the flow path;
and searching a dominant path in the medial axis transformation image based on the weight, and reconnecting the break points in the medial axis transformation image.
Optionally, the searching for the dominant path in the medial axis transformation image based on the weight and reconnecting the breakpoint in the medial axis transformation image includes:
Searching a dominant path by adopting a Dijkstra algorithm;
And performing breakpoint reconnection on the areas which are not communicated in the medial axis transformation image by adopting a corrosion algorithm.
Optionally, the image data includes: seismic interval slice porosity data and well position coordinate data;
The seismic surface slice porosity data at least comprises 5 columns of data which are respectively used for representing image coordinates, geological coordinates and porosity;
The well location coordinate data at least comprises 4 columns of data which are respectively used for representing geological coordinates and pixel coordinate positions.
Optionally, after the step of acquiring the image data to be identified, the method further includes:
The acquired image data is subjected to inter-data interpolation,
The interpolation between the data is to insert corresponding image data between two adjacent slice image data.
The inserted image data is related data of two adjacent slice image data, as shown in fig. 7a to 7c, and fig. 7b is the inserted image data, and the inserted image data is obtained by adopting a spatial mean interpolation algorithm.
Optionally, the binarizing processing is performed on the image in the image data, including:
counting pore pixels of each picture in the image data;
drawing a pixel histogram based on the aperture pixels;
moving averages smooth the histogram data;
and searching for the valley bottom value based on the histogram data after moving average smoothing, and taking the valley bottom value as a binarization threshold value.
Optionally, the extracting the central axis of the flow path based on the binarized image includes:
Extracting a flow center axis of the binarized image to obtain a refined path image, and describing a flow path while retaining the topological characteristics of a pore channel;
and extracting the central axis of the flow path from the thinned path image by adopting a central axis transformation technology.
Optionally, the calculating the weight of the flow path includes:
the weight of the flow path is the weight accumulation of a basic unit consisting of two vertexes and one edge, and the attribute of the vertexes comprises an overflow area Ai and a permeability ki;
the weighting mode of the overflow area Ai and the permeability ki is as follows:
optionally, calculating the overcurrent area Ai includes:
Splitting a flow pore and a non-flow pore in the medial axis transformation image according to a set threshold; the set threshold is obtained based on a threshold calculation and subdivision method.
Selecting a neighborhood template, searching whether a non-flowable pore exists in a neighborhood space, and if so, taking the distance of the non-flowable pore closest to a central target pore as the radius of a maximum sphere;
if not, increasing the radius of the neighborhood template, and continuously searching whether a non-flowable pore exists in the neighborhood space;
And using the maximum sphere radius as a neighborhood template radius to reuse the neighborhood template, searching all the flowable pores with the distances smaller than or equal to the maximum sphere radius in the neighborhood template, and taking the flowable pores as the flow coverage area of the flowable pores of the central target pore, and obtaining the overflow area based on the coverage area.
Optionally, the formula for solving the permeability K is specifically:
Embodiment one:
An intelligent identification method for communication paths among wells utilizes seismic along-layer porosity slice data to conduct intelligent identification technology for communication paths among wells. The method comprises the following steps:
S1, reading and storing data;
The volume of data to be input in the present invention is seismic overburden slice porosity data as shown in figure 2a, while well position coordinate data is required. The slice-along porosity data volume is composed of a series of asc files, each data volume should contain at least 5 columns of data, representing image coordinates (x, y), geological coordinates (x, y) and porosity (corresponding to image gray scale), respectively. As shown in FIG. 2b, the xlsx file contains at least four columns of data representing geological coordinates and pixel coordinate locations, respectively
S2, interpolation between data;
the seismic surface slice data in S1 can cause partial information loss due to the problem of seismic resolution, and a piece of picture data is inserted between two adjacent slice data by adopting a linear interpolation method widely used at present so as to improve the image recognition precision. As shown in fig. 3a and 3 b.
S3, binarizing the image;
As shown in fig. 4a and 4b, the image needs to be binarized before being analyzed. The binarization method needs to search a threshold value, and adopts a binarization method based on a histogram by combining an image gray level histogram and an oil reservoir pore classification theory to count all pores into the histogram, so as to search a bimodal valley bottom as the threshold value. The specific steps of image binarization include:
(1) Counting pore pixels of each picture;
(2) Drawing a pixel histogram;
(3) Moving average smoothed histogram data;
(4) The valley bottom value is found as the binarization threshold.
S4, extracting a flow center axis of the image features;
The refinement of the binarized image in S3, also called flow axis extraction, aims to preserve the topological features of the pore channels while describing the flow path. The central axis of the flow path in S3 is extracted by using a medial axis transformation, also called a grass-fire technique. As shown in fig. 5.
S5, establishing weights;
S4, after the flow center axis is extracted, calculating the weight of the flow channel in the form of discrete Darcy' S law. The weight of the flow channel is actually the sum of the weights of the base units formed by two vertices plus one edge. For the cell weights, the length of the edge represents Δx, and the overflow area Ai and the permeability ki are both vertex attributes weighted as follows:
Ai represents the flow area, and in the two-dimensional flow mode represents the radius of the vertical line of the flow channel, and is calculated by using the maximum sphere method.
Ki denotes the permeability of the flow block, and the solution of ki is calculated by using the K-C formula.
Phi represents porosity, K represents permeability, c represents a calculation constant, and D represents gravel diameter.
S6, searching an optimal path.
And according to the weight value determined in the step S4, searching an optimal path which is an advantageous channel by adopting a Dijkstra algorithm, and reconnecting the breakpoint in the step S4 image by adopting a corrosion algorithm in consideration of the influence of factors such as seismic resolution, noise and the like. As shown in fig. 6a and 6 b.
Embodiment two:
as shown in fig. 1, an intelligent identification method for an inter-well communication path, based on pore structure classification evaluation of a fractal dimension of a logging curve, comprises the following steps:
S1, reading and storing data;
The data volume to be input in the invention is seismic surface slice porosity data, and well position coordinate data is needed. The slice-along porosity data volume is composed of a series of asc files, each data volume should contain at least 5 columns of data, representing image coordinates (x, y), geological coordinates (x, y) and porosity (corresponding to image gray scale), respectively. Well location coordinate data. Xlsx or. Prn file contains at least four columns of data, representing geological coordinates and pixel coordinate locations, respectively. The porosity pixel coordinates and geographic coordinates are as follows:
The downhole geographic coordinates are as follows:
s2, interpolation between data;
the seismic surface slice data in S1 can cause partial information loss due to the problem of seismic resolution, and a piece of picture data is inserted between two adjacent slice data by adopting a linear interpolation method widely used at present so as to improve the image recognition precision. As shown in fig. 7a to 7 c.
S3, binarizing the image;
As shown in fig. 8a and 8b, the image needs to be binarized before being analyzed. The binarization method needs to search a threshold value, and adopts a binarization method based on a histogram by combining an image gray level histogram and an oil reservoir pore classification theory to count all pores into the histogram, so as to search a bimodal valley bottom as the threshold value. The specific steps of image binarization include:
Counting pore pixels of each picture;
Drawing a pixel histogram;
moving average smoothed histogram data;
the valley bottom value is found as the binarization threshold.
S4, extracting a flow center axis of the image features;
The refinement of the binarized image in S3, also called flow axis extraction, aims to preserve the topological features of the pore channels while describing the flow path. The central axis of the flow path in S3 is extracted by using a medial axis transformation, also called a grass-fire technique. As shown in fig. 9a to 9 b.
S5, establishing weights;
the darcy law determines the seepage law under linear flow, and the continuous darcy law is as follows:
Wherein Q is flow, k is permeability, A is seepage area, Δp is seepage pressure difference, μ is fluid viscosity, Δx is core length, core length is the whole before dispersion, and edge length is the result after dispersion. Core length is the same meaning as core length.
Let the seepage resistance be R, the above formula is rewritable:
Wherein,
Since the fluid viscosity mu is a fixed value in the flowing process, the size of the seepage resistance is not influenced. As can be seen from the basic model, the basic model obtained in the above manner is a discrete seepage model determined by nodes and edges, and the solution area is a discrete pore pixel point.
Therefore, the darcy law needs to be discretized, and in the micro seepage unit, the fluid seepage resistance can be expressed as follows:
Where Δx i represents the i-1 th aperture and the spatial distance (length of the edge) of the i-th aperture on the first flow path, k i represents the permeability of the i-th aperture (KC formula solution), and a i represents the flow area of the i-th aperture (two-dimensional space represents the flow width, determined by the maximum sphere algorithm).
The resistance of a single flow channel in a single direction considering a single width is a linear superposition (series mechanism), namely:
R＝R1+R2+...＝∑Ri (7)，
I.e. the flow resistance is calculated in a series pattern between every two connected nodes (every two flowable pore nodes). The flow resistance from the injection well to the production well is a linear superposition of the area resistance between every two communicating pore points on the flow path.
S4, after the flow center axis is extracted, calculating the weight of the flow channel in the form of discrete Darcy' S law. The weight of the flow channel is actually the sum of the weights of the base units formed by two vertices plus one edge. For the cell weights, the length of the edge represents Δx, and the overflow area Ai and the permeability ki are both vertex attributes weighted as follows:
A i represents the flow area, and in the two-dimensional flow mode, represents the radius of the vertical line of the flow channel, and is calculated by using the maximum sphere method. The algorithm is as follows:
As can be seen from the implementation process of the conventional maximum sphere algorithm, each target aperture performs distance calculation with all the non-flowable pixels, the resolution of each horizon of the data is 649×609, and the flowable porosity is 10% according to the binary threshold subdivision, so that the number of pixels of the flowable apertures is 3.6 ten thousand, and each flowable aperture performs distance calculation with all the non-flowable apertures, so that the calculation amount is huge and the time consumption is long. Therefore, the present invention provides a speed-up maximum sphere algorithm, which can effectively reduce the calculated amount and save the calculation time, and as shown in fig. 10a and 10b, the calculation flow is as follows:
splitting the flow pores and the non-flow pores according to a threshold;
selecting a group of 5 multiplied by 5 neighborhood templates, searching whether a non-flowable pore exists in a neighborhood space, and if so, taking the distance of the non-flowable pore closest to a central target pore as the radius of a maximum sphere;
If not, increasing the radius of the neighborhood template, and returning to the step (2) for carrying out again;
After finding the maximum sphere radius, using the neighborhood template again with the maximum sphere radius as the neighborhood template radius, finding all the flowable pores with the distances smaller than or equal to the maximum sphere radius in the template, and taking the flowable pores as the flow coverage area of the central flowable pore for recording.
Ki denotes the permeability of the flow block, and the solution of ki is calculated by using the K-C formula.
S6, searching an optimal path.
And according to the weight value determined in the step S4, searching an optimal path which is an advantageous channel by adopting a Dijkstra algorithm, and reconnecting the breakpoint in the step S4 image by adopting a corrosion algorithm in consideration of the influence of factors such as seismic resolution, noise and the like. As shown in fig. 11a and 11 b.
The invention discloses a rapid and intelligent searching and identifying technical process for forming a communication path between fracture-cavity bodies by utilizing a path analysis algorithm. Establishing a flow path weight by using a discrete Darcy law and searching an optimal path by combining a Dijkstra shortest path algorithm; after the refined image is filtered, the breakpoint reconnection is carried out on the area which is not communicated originally due to the factors of seismic resolution, noise and the like through a corrosion algorithm, so that the intelligent recognition precision is greatly improved.
Based on the result of the porosity Kriging interpolation data of the reservoir geology, the interpolation result is restored by a vectorization matrix recombination mode of python, the effective value of the porosity fluidity is determined based on a porosity distribution relation, and then the dominant flow channel is determined by adopting a mode of axis transformation to refine and extract the flow path and combining the Dijkstra algorithm and the discrete Darcy law. The invention can find and determine the geographic and weighted dominant flow channels and provide reference for the optimization of the later oil reservoir production and injection or exploitation scheme.
Embodiment III:
an embodiment of the invention provides an electronic device comprising a memory and a processor,
A memory storing executable instructions;
And the processor runs executable instructions in the memory to realize the intelligent identification method of the inter-well communication path.
The memory is for storing non-transitory computer readable instructions. In particular, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform the desired functions. In one embodiment of the invention, the processor is configured to execute the computer readable instructions stored in the memory.
It should be understood by those skilled in the art that, in order to solve the technical problem of how to obtain a good user experience effect, the present embodiment may also include well-known structures such as a communication bus, an interface, and the like, and these well-known structures are also included in the protection scope of the present invention.
The detailed description of the present embodiment may refer to the corresponding description in the foregoing embodiments, and will not be repeated herein.
The embodiment of the invention provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize an intelligent identification method for a communication path between wells.
A computer-readable storage medium according to an embodiment of the present invention has stored thereon non-transitory computer-readable instructions. When executed by a processor, perform all or part of the steps of the methods of embodiments of the invention described above.
The computer-readable storage medium described above includes, but is not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or removable hard disk), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described.
Claims (5)
1. An intelligent identification method for an inter-well communication path is characterized by comprising the following steps:
acquiring image data to be identified, wherein the image data comprises inter-well communication paths;
Performing inter-data interpolation on the acquired image data, wherein the inter-data interpolation is to insert corresponding image data between two adjacent slice image data;
performing binarization processing on the image in the image data to obtain a binarized image;
Extracting the central axis of the flow path based on the binarized image to obtain a central axis transformation image;
calculating the weight of the flow path;
Searching for a dominant path in the medial axis transformation image based on the weight, and reconnecting a breakpoint in the medial axis transformation image, including:
Searching a dominant path by adopting a Dijkstra algorithm;
Performing breakpoint reconnection on the areas which are not communicated in the middle axis transformation image by adopting a corrosion algorithm;
The binarizing processing of the image in the image data comprises the following steps:
counting pore pixels of each picture in the image data;
drawing a pixel histogram based on the aperture pixels;
moving averages smooth the histogram data;
Searching for a valley bottom value based on the histogram data after moving average smoothing, and taking the valley bottom value as a binarization threshold;
the extracting a flow path central axis based on the binarized image includes:
Extracting a flow center axis of the binarized image to obtain a refined path image, and describing a flow path while retaining the topological characteristics of a pore channel;
Extracting the central axis of the flow path from the thinned path image by adopting a central axis transformation technology;
The calculating the weight of the flow path includes:
the weight of the flow path is the weight accumulation of a basic unit consisting of two vertexes and one edge, and the attribute of the vertexes comprises an overflow area Ai and a permeability ki;
the weighting mode of the overflow area Ai and the permeability ki is as follows:
Where ki denotes the permeability of the ith pore and Ai denotes the flow area of the ith pore.
2. The method for intelligently identifying an inter-well communication path according to claim 1, wherein the image data comprises: seismic interval slice porosity data and well position coordinate data;
The seismic surface slice porosity data at least comprises 5 columns of data which are respectively used for representing image coordinates, geological coordinates and porosity;
The well location coordinate data at least comprises 4 columns of data which are respectively used for representing geological coordinates and pixel coordinate positions.
3. The method of intelligent identification of an inter-well communication path according to claim 1, wherein calculating the overflow area Ai comprises:
Splitting a flow pore and a non-flow pore in the medial axis transformation image according to a set threshold;
selecting a neighborhood template, searching whether a non-flowable pore exists in a neighborhood space, and if so, taking the distance of the non-flowable pore closest to a central target pore as the radius of a maximum sphere;
if not, increasing the radius of the neighborhood template, and continuously searching whether a non-flowable pore exists in the neighborhood space;
And using the maximum sphere radius as a neighborhood template radius to reuse the neighborhood template, searching all the flowable pores with the distances smaller than or equal to the maximum sphere radius in the neighborhood template, and taking the flowable pores as the flow coverage area of the flowable pores of the central target pore, and obtaining the overflow area based on the coverage area.
4. The method for intelligently identifying the communication path between wells according to claim 1, wherein the formula for solving the permeability K is specifically:
Φ represents porosity, K represents permeability, c represents calculation constant, and D represents gravel diameter.
5. An electronic device, the electronic device comprising:
A memory storing executable instructions;
a processor executing the executable instructions in the memory to implement the method of intelligent identification of an inter-well communication path of any of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010555476.7A CN113807359B (en) | 2020-06-17 | 2020-06-17 | Intelligent identification method for inter-well communication path and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010555476.7A CN113807359B (en) | 2020-06-17 | 2020-06-17 | Intelligent identification method for inter-well communication path and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113807359A CN113807359A (en) | 2021-12-17 |
| CN113807359B true CN113807359B (en) | 2024-05-10 |
Family
ID=78943554
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010555476.7A Active CN113807359B (en) | 2020-06-17 | 2020-06-17 | Intelligent identification method for inter-well communication path and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113807359B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102930277A (en) * | 2012-09-19 | 2013-02-13 | 上海珍岛信息技术有限公司 | Character picture verification code identifying method based on identification feedback |
| CN106097375A (en) * | 2016-06-27 | 2016-11-09 | 湖南大学 | The folding line detection method of a kind of scanogram and device |
| CN106323836A (en) * | 2016-08-11 | 2017-01-11 | 中国石油天然气股份有限公司 | Calculating method and device for well-wall permeability |
| CN107632319A (en) * | 2017-07-31 | 2018-01-26 | 成都理工大学 | Solution cavity identifies scaling method in carbonate rock heterogeneous reservoir based on GST |
| CN108802073A (en) * | 2017-05-05 | 2018-11-13 | 中国石油化工股份有限公司 | A kind of rock electrical parameters acquisition methods and device based on digital cores |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130110485A1 (en) * | 2011-10-26 | 2013-05-02 | Weichang Li | Determining Interwell Communication |
-
2020
- 2020-06-17 CN CN202010555476.7A patent/CN113807359B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102930277A (en) * | 2012-09-19 | 2013-02-13 | 上海珍岛信息技术有限公司 | Character picture verification code identifying method based on identification feedback |
| CN106097375A (en) * | 2016-06-27 | 2016-11-09 | 湖南大学 | The folding line detection method of a kind of scanogram and device |
| CN106323836A (en) * | 2016-08-11 | 2017-01-11 | 中国石油天然气股份有限公司 | Calculating method and device for well-wall permeability |
| CN108802073A (en) * | 2017-05-05 | 2018-11-13 | 中国石油化工股份有限公司 | A kind of rock electrical parameters acquisition methods and device based on digital cores |
| CN107632319A (en) * | 2017-07-31 | 2018-01-26 | 成都理工大学 | Solution cavity identifies scaling method in carbonate rock heterogeneous reservoir based on GST |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113807359A (en) | 2021-12-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8862450B2 (en) | Method of developing a petroleum reservoir from history matching of production data and seismic data | |
| CN106981093B (en) | Three-dimensional stratum parallel modeling method based on partition constraint coupling | |
| US10997327B2 (en) | Direct numerical simulation of petrophysical properties of rocks with two or more immicible phases | |
| Tillier et al. | Appropriate formulation of the objective function for the history matching of seismic attributes | |
| CN104950334A (en) | Method and device for predicting reservoir distribution | |
| US20230332495A1 (en) | Borehole image interpretation and analysis | |
| Delhomme | A quantitative characterization of formation heterogeneities based on borehole image analysis | |
| CN112861890B (en) | Reservoir evaluation model construction method and reservoir identification method | |
| CA2710809C (en) | Method for determining distinct alternative paths between two object sets in 2-d and 3-d heterogeneous data | |
| CN112800590B (en) | Grid coarsening method for machine learning-assisted two-phase flow oil reservoir random modeling | |
| CN113807359B (en) | Intelligent identification method for inter-well communication path and electronic equipment | |
| CN112903555A (en) | Porous medium permeability calculation method and device considering pore anisotropy | |
| CN116721227A (en) | Automatic modeling method for three-dimensional geologic model of complex geologic body | |
| US11802989B2 (en) | Systems and methods for generating vertical and lateral heterogeneity indices of reservoirs | |
| CN113409463B (en) | Three-dimensional geological model construction method and device including pinch-out treatment | |
| CN114898057A (en) | Geological profile three-dimensional space information extraction method and device and storage medium | |
| Zhang et al. | An innovative image-based workflow for quantitative pore structure characterization in reservoir rocks | |
| Wu et al. | Quantification of Connectivity in Images | |
| CN115775277B (en) | Fracturing stage selection method and system based on big data | |
| CN113255155B (en) | Evaluation method for seepage resistance grade of reservoir discontinuous limit | |
| CN116226623B (en) | Mark layer division method and device based on SegNet segmentation model and computer equipment | |
| US20230313649A1 (en) | System and Method to Develop Naturally Fractured Hydrocarbon Reservoirs Using A Fracture Density Index | |
| CN112302638B (en) | Streamline field characterization method and device for oil reservoir | |
| CN115588103A (en) | Main flow line azimuth angle prediction device and method for narrow and small river channel type reservoir | |
| CN116607931A (en) | Method, device and medium for calculating permeability of tight reservoir based on big data analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |