CN112014841A - Analysis method for monitoring deformation of surface of oil field area based on DS-InSAR technology - Google Patents
Analysis method for monitoring deformation of surface of oil field area based on DS-InSAR technology Download PDFInfo
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Abstract
The invention discloses an analysis method for monitoring surface deformation of an oil field area based on a DS-InSAR technology, belonging to the field of surface deformation monitoring of InSAR and comprising the following steps: preprocessing a radar remote sensing image; identifying a homogeneous set of pixels; extracting potential distributed targets of the oil field; optimizing phase estimation, and selecting a point with higher quality as a distributed target point (DSC); obtaining permanent scatterer Points (PSC) through a conventional time sequence InSAR technology, and combining the PSC and the DSC points to perform time sequence processing to realize surface deformation monitoring; and (4) cooperatively analyzing the ground surface deformation result and the oil field production data. The method can greatly improve the number of the measuring points and the spatial distribution density of the oil field area, improve the phase unwrapping precision and obtain a more comprehensive and reliable deformation result. Meanwhile, the collaborative analysis of the production data of the oil field is beneficial to timely examining high-risk areas such as casing loss and the like, and powerful technical support is provided for adjusting production planning.
Description
Technical Field
The invention relates to the technical field of geological disaster deformation monitoring, in particular to an analysis method for monitoring surface deformation of an oil field area based on a DS-InSAR technology.
Background
In the actual oil exploitation process, due to the long-time oil exploitation activity, the pressure of an oil layer can be continuously reduced, the reduction of the pressure of the oil layer can increase the viscosity of underground crude oil, the crude oil is difficult to extract, and the yield of the oil can be greatly reduced. In order to increase the oil recovery rate, a water injection well is generally used to inject waste liquid or the like into an oil reservoir to increase the pressure of the reservoir depletion, thereby achieving stable production of an oil field. However, flooding and recovery operations in oil fields cause changes in subsurface pore pressures and stresses, often inducing surface deformation in areas where the formation is unstable. Surface deformation of an oil field may cause problems with seismic activity, surface subsidence, casing damage, oil well shut-down, etc., which adversely affect local population and economics. Therefore, the problem of monitoring the deformation of the surface of the oil field has become a problem which needs attention in the future. The method can be used for monitoring the deformation of the earth surface in the oil field area, can provide a basis for scientific decision-making for the production activities of the oil field, and has very important significance for effectively improving the yield, preventing the damage of facilities and ensuring the economic stability.
The traditional geological disaster deformation monitoring method comprises leveling measurement and GPS measurement, but points need to be arranged in advance, and the problems of time and labor waste, high cost, long period, difficulty in comprehensively covering a research area and the like exist. The InSAR technology has the characteristics of high resolution, large coverage area, low cost, continuous observation and the like, and has incomparable superiority with the conventional deformation monitoring means. Although the conventional time sequence InSAR technology is used for monitoring an oil field area, the measurement points extracted by the conventional time sequence InSAR technology are Permanent Scatterer (PS) points with stable scattering characteristics and strong echo signals, so that the method is more suitable for urban areas with stable ground object targets. The oil field area has no area of typical ground objects and artificial buildings, is mostly in natural areas such as cultivated land, grassland and foam, and the problems of sparse observation point distribution, low space sampling rate, large atmospheric correction error and the like exist when monitoring is carried out by utilizing the conventional time sequence InSAR technology, so that the scope and the strength of the surface deformation of the oil field are difficult to clearly and completely reflect, and the surface deformation information cannot be accurately obtained.
Disclosure of Invention
The invention aims to overcome the defects of monitoring an oil field area by a conventional time sequence InSAR, and provides an analysis method for monitoring the deformation of the earth surface of the oil field area based on a DS-InSAR technology.
In order to achieve the purpose, the invention is realized by the following technical scheme: a method for monitoring surface deformation of an oil field area based on a DS-InSAR technology is characterized in that distributed target alternative points (DSC) are integrated in a conventional time sequence InSAR technology to remarkably improve the number and spatial distribution density of measuring points and improve the accuracy of phase unwrapping, and meanwhile, a surface deformation result is combined with production data of the oil field area to carry out collaborative analysis, so that a high-risk casing damage area can be identified and eliminated in time, and technical support is provided for planning and adjusting production operation of the oil field. The method comprises the following specific steps:
(1) preprocessing an SAR image covering a research area, wherein the preprocessing comprises image registration, intensity matching, common diversity registration, SLC resampling and generation of an initial differential interferogram;
(2) performing similarity inspection on SAR image pixels of an oil field area based on intensity information according to different scattering characteristics shown by responses of different types of surface feature targets to electromagnetic wave signals, and identifying a homogeneous pixel set;
(3) calculating a sample covariance/coherence matrix containing the adaptive multi-view interference phase values between all interference pairs; the sample is a complex observation vector formed by a certain pixel on the N registered SAR images;
(4) estimating a phase value meeting the phase consistency as an optimized phase of the distributed target;
(5) calculating the phase optimization quality, and selecting a distributed target alternative point (DSC) during fusion;
(6) fusing the selected distributed target alternate points (DSC) with a permanent scatterer alternate Point (PSC) set obtained by utilizing a traditional time sequence PS-InSAR technology to obtain a target set;
(7) performing conventional time sequence InSAR technical treatment on the target set obtained after fusion, wherein the conventional time sequence InSAR technical treatment comprises terrain error correction, 3D phase unwrapping, residual terrain error correction and atmospheric phase delay correction to obtain a surface deformation monitoring result of the oil field area;
(8) and obtaining an annual average sedimentation rate value and a time sequence sedimentation value according to the surface deformation monitoring result of the oil field area, identifying the deformation area, and performing linkage analysis by combining the production data of liquid production and injection of the wellhead of the oil field area and the pipeline casing loss.
Further, in the step (2), homogeneous pixels in the oil field area are identified by adopting a parameter hypothesis testing method, so that a homogeneous pixel set with the same or similar backscattering characteristics is obtained.
Further, in the step (4), a phase value meeting the phase consistency is estimated as an optimized phase of the distributed target by using a coherent matrix eigenvalue decomposition method;
the phase optimization function model is:
wherein,
for a group of optimal fitting phase estimation values solved from the multi-view interference phase, N represents N SAR images; b is an unknown parameter representing the interference phase of the single main image to be estimated
o is the main image, i is 1,2, …, Ν -1; wweightRespective weights set for coherence in accordance with the interference phases; exp (je) is a residual vector used to describe the degree to which a certain distributed target phase is affected by the decorrelated noise.
Further, in step (5), in order to make the distributed target have statistical properties for subsequent phase optimization and preserve PS stable phase information, the number of (SHPs) satisfying a certain threshold condition is generally set>20) The homogeneous pixels are used as distributed target alternative points, and the SHPs are the quantity of the homogeneous pixels with the same or similar backscattering characteristics. By choosing the temporal coherence yDSIs measured by the threshold ofAnd measuring the influence degree of the distributed targets by the time coherence, and selecting distributed target alternative points (DSC) during fusion.
Compared with the prior art, the invention has the following beneficial effects:
the invention discloses an analysis method for monitoring oil field surface deformation based on a DS-InSAR technology. The DS-InSAR technology is used for monitoring the deformation of the earth surface of the oil field area, the density and the spatial distribution of monitoring points in the oil field area can be improved, the reliable data volume is increased, and the problem that the number of the monitoring points in the oil field area is sparse due to the fact that high-coherence stable points are few in the traditional time sequence InSAR technology is greatly solved. On the basis of obtaining a comprehensive surface deformation result, production data such as liquid extraction and liquid injection in an oil field area are combined for analysis, and the influence condition of production activities on surface deformation can be obtained, so that potential hazards possibly existing in the oil field area can be identified and judged in time, a high-risk area of casing damage and the like can be subjected to key investigation in a guiding mode, necessary methods and technical support are provided for planning adjustment of corresponding production operation conditions, and continuous and stable development of the production activities in the oil field area is guaranteed.
Drawings
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a comparison of surface deformation results obtained by the conventional timing technique and the DS-InSAR technique according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the relationship between the injection production activity of a well and the deformation of the earth's surface according to an embodiment of the present invention.
Detailed Description
The invention will be further elucidated and described with reference to the embodiments of the invention described hereinafter.
As shown in fig. 1, an analysis method for monitoring surface deformation of an oil field area based on DS-InSAR technology includes the following steps:
And 2, according to the response of different types of surface feature targets to electromagnetic wave signals, showing different scattering characteristics, carrying out similarity detection on SAR image pixels in the oil field area based on intensity information, and identifying a homogeneous pixel set which has the same or similar backscattering characteristics with each distributed pixel by adopting a parameter hypothesis detection method.
In the steps, a hypothesis testing method is adopted, homogeneous pixels are determined by judging the similarity of the distribution characteristics among the pixel samples, and a homogeneous pixel set having the same or similar characteristics with each distributed pixel is identified. Hypothesis testing methods include nonparametric hypothesis methods and parametric hypothesis testing methods. The parameter hypothesis testing method is established under the condition that a sample distribution function is known, and the homogeneous pixels are identified by judging whether the overall distribution statistics of the sample data has significance difference, so that the calculation efficiency is higher.
Step 3, calculating a sample covariance/coherence matrix containing self-adaptive multi-view interference phase values among all interference pairs according to the homogeneous pixel set, and describing the statistical properties of the distributed target in the homogeneous pixel set; the sample is a complex observation vector formed by a certain pixel on the N registered SAR images.
Wherein the sample covariance matrix contains the adaptive multi-view interference phase information for all interference pairs. The sample coherent matrix not only contains the self-adaptive multi-view interference phase information of all interference pairs, but also contains coherence estimation for measuring the quality of the interference phase, so that the difference caused by backward scattering energy imbalance among multi-temporal SAR images can be effectively compensated.
And 4, constructing a group of single main image optimized phase values by adopting a certain phase optimization algorithm under the condition of meeting the phase consistency, and acquiring a group of best fit phases subjected to self-adaptive multi-view noise reduction treatment so as to weaken the influence of the distributed target decoherence phenomenon. The phase optimization method aiming at the single main image comprises a maximum likelihood estimation Method (ML), covariance matrix eigenvalue decomposition (C-EVD) and coherence matrix eigenvalue decomposition (T-EVD). The coherent matrix eigenvalue decomposition-based method is higher in calculation efficiency, and therefore the phase value meeting the phase consistency is estimated by the coherent matrix eigenvalue decomposition method to serve as the optimized phase of the distributed target. The function model in the phase-optimized complex form is:
wherein,
a group of optimal fitting phase estimation values are solved from N (N-1)/2 multi-view interference phases by adopting a certain optimization method, wherein N represents N SAR images; b is an unknown parameter representing the interference phase of the single main image to be estimated
o is the main image, i is 1,2, …, Ν -1; wweightRespective weights set for coherence in accordance with the interference phases; exp (je) is a residual vector used to describe the degree to which a certain distributed target phase is affected by the decorrelated noise.
Solving the maximum eigenvalue λ in general1Corresponding eigenvector u1As an optimized phase estimate. Solving for the maximum eigenvalue λ1Corresponding feature vector u1Can be expressed as:
wherein,
a set of best-fit phase estimation values solved from N (N-1)/2 multi-view interference phases by a coherent matrix eigenvalue decomposition method (T-EVD) phase optimization method;
representing a single main image interference phase to be estimated; coherence matrix
Can be expressed as the sum of the superposition of coherence matrices obtained under the interaction of different types of scattering mechanisms.
wherein gamma isDSThe temporal coherence of the distributed object is represented,
and
to optimize the phase of the distributed object under a single master image,
for adaptive multi-view interference phase, m, n, o are sequential SAR image sequence numbers.
In order to enable the distributed targets to have statistical characteristics so as to facilitate subsequent phase optimization and retain PS stable phase information, homogeneous pixels (SHPs >20) with the number meeting a certain threshold value condition are used as distributed target candidate points, and the SHPs are the number of the homogeneous pixels with the same or similar backscattering characteristics. And replacing the original interference phase observed quantity with a high-quality phase observed quantity with good observed quantity coherence and high signal-to-noise ratio, and taking the distributed target corresponding to the high-quality phase observed quantity as a distributed target alternative point (DSC) during fusion.
And 6, fusing the selected distributed target alternate points (DSC) with a permanent scatterer alternate Point (PSC) set obtained by utilizing the traditional time sequence PS-InSAR technology to obtain a target set.
And 7, carrying out conventional time sequence InSAR technical treatment on the target set obtained after fusion, wherein the conventional time sequence InSAR technical treatment comprises terrain error correction, 3D phase unwrapping, residual terrain error correction and atmospheric phase delay correction, and obtaining the surface deformation monitoring result of the DS-InSAR technology of the oil field area. As can be seen from comparison of the monitoring results of the DS-InSAR technology shown in FIG. 2 and the traditional time sequence InSAR technology, the DS-InSAR technology remarkably increases the number and density of observation points, and more clearly reflects the surface deformation distribution condition of the oil field area.
Step 8, obtaining an annual average sedimentation rate value and a time sequence sedimentation value according to the surface deformation monitoring result of the oil field area, and identifying a deformation area; and the linkage analysis is carried out by combining the production data of liquid extraction and liquid injection of the wellhead of the oil field area and the pipeline casing damage, the linkage relation between the surface deformation and the production activity of the oil field area is obtained, and the high-risk casing damage area is identified in time. As can be seen from fig. 3, the flooding causes the surface to swell, and the swelling amount of the surface is reduced after the flooding amount is reduced, so that the wellhead casing pipe should be prevented from being damaged due to the ground lifting caused by the continuous increase of the wellhead flooding amount.
The embodiments of the present invention have been described above, but the present invention is not limited to the embodiments. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit of the invention, and these modifications and improvements are considered to be within the scope of the invention.
Claims (4)
1. An analysis method for monitoring surface deformation of an oil field area based on a DS-InSAR technology is characterized by comprising the following steps: the method comprises the following steps:
(1) preprocessing an SAR image covering a research area, wherein the preprocessing comprises image registration, intensity matching, common diversity registration, SLC resampling and generation of an initial differential interferogram;
(2) performing similarity inspection on SAR image pixels of an oil field area based on intensity information according to different scattering characteristics shown by responses of different types of surface feature targets to electromagnetic wave signals, and identifying a homogeneous pixel set;
(3) calculating a sample covariance/coherence matrix containing the adaptive multi-view interference phase values between all interference pairs; the sample is a complex observation vector formed by a certain pixel on the N registered SAR images;
(4) estimating a phase value meeting the phase consistency as an optimized phase of the distributed target;
(5) calculating the phase optimization quality, and selecting a distributed target alternative point (DSC) during fusion;
(6) fusing the selected distributed target alternate points (DSC) with a permanent scatterer alternate Point (PSC) set obtained by utilizing a traditional time sequence PS-InSAR technology to obtain a target set;
(7) performing conventional time sequence InSAR technical treatment on the target set obtained after fusion, wherein the conventional time sequence InSAR technical treatment comprises terrain error correction, 3D phase unwrapping, residual terrain error correction and atmospheric phase delay correction to obtain a surface deformation monitoring result of the oil field area;
(8) and obtaining an annual average sedimentation rate value and a time sequence sedimentation value according to the surface deformation monitoring result of the oil field area, identifying the deformation area, and performing linkage analysis by combining the production data of liquid production and injection of the wellhead of the oil field area and the pipeline casing loss.
2. The method for analyzing the deformation of the surface of the monitored oilfield region based on the DS-InSAR technology as claimed in claim 1, wherein: and (2) identifying homogeneous pixels in the oil field area by adopting a parameter hypothesis testing method to obtain a homogeneous pixel set with the same or similar backscattering characteristics.
3. The method for analyzing the deformation of the surface of the monitored oilfield region based on the DS-InSAR technology as claimed in claim 1, wherein: in the step (4), a coherent matrix eigenvalue decomposition method is used for estimating phase values meeting the phase consistency as the optimized phases of the distributed targets; the phase optimization function model is:
wherein,
for a group of optimal fitting phase estimation values solved from the multi-view interference phase, N represents N SAR images; b is an unknown parameter representing the interference phase of the single main image to be estimated
o is the main image, i is 1,2, …, Ν -1; wweightRespective weights set for coherence in accordance with the interference phases; exp (je) is a residual vector used to describe the degree to which a certain distributed target phase is affected by the decorrelated noise.
4. The method for analyzing the surface deformation of the monitored oilfield region based on the DS-InSAR technology as claimed in any one of claims 1 to 3, wherein: and (5) taking the homogeneous pixels with the quantity meeting a certain threshold value condition as distributed target alternate points, and selecting time coherence gammaDSThe influence degree of the distributed targets by the time coherence is measured by the threshold value, and the distributed target alternative points during fusion are selected.
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| CN113204023A (en) * | 2021-05-10 | 2021-08-03 | 中国地质大学（武汉） | Dual-polarization phase optimization earth surface deformation monitoring method combining PS target and DS target |
| CN113447927A (en) * | 2021-06-29 | 2021-09-28 | 中国矿业大学 | Time sequence InSAR (interferometric synthetic Aperture Radar) surface subsidence monitoring method based on point target hierarchical analysis |
| CN113447927B (en) * | 2021-06-29 | 2023-09-05 | 中国矿业大学 | Time sequence InSAR earth surface subsidence monitoring method based on point target layering analysis |
| CN113253271A (en) * | 2021-07-12 | 2021-08-13 | 中国测绘科学研究院 | Deformation rate estimation method and system |
| CN113687353B (en) * | 2021-08-09 | 2023-08-29 | 中国矿业大学 | DS target phase optimization method based on homogeneous pixel time sequence phase matrix decomposition |
| CN113687353A (en) * | 2021-08-09 | 2021-11-23 | 中国矿业大学 | DS target phase optimization method based on homogeneous pixel time sequence phase matrix decomposition |
| CN114545407A (en) * | 2021-09-24 | 2022-05-27 | 中国科学院精密测量科学与技术创新研究院 | Satellite-borne differential tomography SAR imaging method based on distributed compressed sensing |
| CN115586527A (en) * | 2022-09-08 | 2023-01-10 | 杭州余杭大数据经营有限公司 | Cloud road deformation early warning system based on DS-InSAR technology |
| CN115586527B (en) * | 2022-09-08 | 2024-01-12 | 杭州余杭大数据经营有限公司 | Cloud road deformation early warning system based on DS-InSAR technology |
| CN116047519A (en) * | 2023-03-30 | 2023-05-02 | 山东建筑大学 | Point selection method based on synthetic aperture radar interferometry technology |
| CN116994132A (en) * | 2023-07-21 | 2023-11-03 | 云南大学 | DS-InSAR technology homogeneous pixel selection method and system |
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