Method and system for monitoring steam chamber expansion during steam flooding of heavy oil reservoirs based on satellite remote sensing technology

Abstract

The present invention provides a method and system for monitoring the expansion of steam chambers in steam-driven heavy oil reservoirs based on satellite remote sensing technology. The method includes: determining the night surface temperature data of the study area based on thermal infrared remote sensing images, and then determining the surface temperature gradient grid data of the study area; using the surface temperature gradient grid data, classifying according to steam zone, steam zone + hot oil water zone, steam zone + hot oil water zone + cold oil zone, and cold oil zone, and determining the horizontal distribution data of the steam chamber; based on the surface temperature gradient grid data combined with the observation well temperature curve data, determining the vertical depth of the steam zone, hot oil water zone, and cold oil zone, and determining the vertical distribution data of the steam chamber; based on the horizontal and vertical distribution data of the steam chamber, performing reservoir temperature fitting interpolation processing, and determining the three-dimensional distribution data of the steam chamber; based on the horizontal, vertical and three-dimensional distribution data of the steam chamber in different periods, predicting the spatiotemporal evolution process of the steam chamber.

Description

Technical Field

[0001] The present invention belongs to the technical field of oilfield reservoir engineering, and in particular relates to a method and system for monitoring steam chamber expansion during steam flooding of heavy oil reservoirs based on satellite remote sensing technology. Background Art

[0002] Steam flooding is the main technology for the development of heavy oil reservoirs. The study of key parameters such as the shape and expansion rate of the steam chamber is of great significance for predicting the steam chamber boundary, evaluating the development effect and guiding production.

[0003] The steam chamber of steam drive expands unevenly, the oil production of a single well is low, and the oil and gas production is relatively low; how to further control the steam chamber is the focus of the next research, but the key to studying these is to accurately understand the boundary of the underground high-temperature steam chamber displacement front, laying the foundation for the next step of precise control.

[0004] Currently, the main methods for studying steam chamber expansion include numerical simulation, micro-4D seismic, observation well temperature analysis, and the steam chamber edge method. Thermal recovery numerical simulation methods are limited by the number of grid cells in the computational model. The calculation of the SAGD FlexWell numerical model with hundreds of thousands of nodes is often time-consuming. While 4D seismic interpretation generally has low vertical accuracy, it provides relatively accurate interpretation of the steam chamber's planar expansion morphology. However, the monitoring interval exceeds one year, which limits the widespread application of this method. Thermocouples embedded in observation wells provide real-time information on steam chamber expansion and serve as the primary source of information for field personnel to assess steam chamber development. However, the limited number of observation wells employed does not fully reflect the overall steam chamber expansion. While the steam chamber edge method is relatively accurate, its model is based on multiple assumptions and is affected by reservoir heterogeneity and operational factors. Furthermore, the calculated value is the average of two adjacent time periods. In general, both the observation well temperature method and the steam chamber edge method rely on computational models to predict the steam chamber expansion rate during steam flooding, resulting in inaccurate results. Summary of the Invention

[0005] The present invention aims to provide a method for monitoring steam chamber expansion during steam flooding of heavy oil reservoirs, capable of accurately monitoring the expansion of the steam chamber during steam flooding of heavy oil reservoirs in real time and calculating the steam chamber expansion rate in real time. This method facilitates a detailed understanding of the distribution of underground reservoir temperature and crude oil saturation, providing a comprehensive understanding of the entire reservoir, facilitating the discovery of new drilling targets, the implementation of new production enhancement measures, and the precise calculation of oilfield recovery factors, providing technical support for steam flooding operations at different production stages.

[0006] To achieve the above object, the present invention provides a method for monitoring steam chamber expansion during steam flooding of heavy oil reservoirs based on satellite remote sensing technology, wherein the method comprises:

[0007] Real-time acquisition of satellite thermal infrared remote sensing images and observation well temperature curve data (such as observation well temperature measured data and observation well temperature instrument logging curves) of the study area at night;

[0008] Determine the nighttime surface temperature data of the study area based on the thermal infrared remote sensing image;

[0009] Based on the night surface temperature data of the study area, the surface temperature gradient raster data of the study area is determined;

[0010] Using the surface temperature gradient grid data of the study area, the steam chamber states are classified into four categories: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone, so as to determine the horizontal distribution data of the steam chamber in steam flooding.

[0011] Based on the surface temperature gradient grid data of the study area and the temperature curve data of the observation wells in the study area, the vertical depths of the steam zone, hot oil and water zone, and cold oil zone are determined, thereby determining the vertical distribution data of the steam chamber in the steam drive;

[0012] Based on the horizontal and vertical distribution data of the steam flooding steam chamber, the reservoir temperature is fitted and interpolated to determine the three-dimensional distribution data of the steam flooding steam chamber;

[0013] Based on the horizontal distribution data of the steam chamber in steam drive at different periods, the vertical distribution data of the steam chamber in steam drive and the three-dimensional distribution data of the steam chamber in steam drive, the spatiotemporal evolution process of the steam chamber is predicted.

[0014] In the above-mentioned method for monitoring steam chamber expansion in steam flooding of heavy oil reservoirs based on satellite remote sensing technology, preferably, determining the surface temperature of the study area based on acquired satellite thermal infrared remote sensing images of the study area at night includes:

[0015] The obtained nighttime satellite thermal infrared remote sensing images of the study area were pre-processed by radiometric calibration, atmospheric correction, geometric correction, etc. to obtain remote sensing surface reflectance data;

[0016] The thermal infrared band of remote sensing surface reflectance data is inverted and calculated, and important parameters such as brightness temperature, surface emissivity, atmospheric mean temperature, and atmospheric water vapor content are estimated to obtain the nighttime thermal infrared remote sensing inversion surface temperature data, i.e., the nighttime surface temperature data of the study area.

[0017] More preferably, the inversion calculation is performed using a single-window algorithm, a single-channel algorithm, or a radiation transfer equation method.

[0018] In the above-mentioned method for monitoring steam chamber expansion in steam flooding of heavy oil reservoirs based on satellite remote sensing technology, preferably, the method further comprises:

[0019] Based on the measured temperature data of the observation wells in the study area, the night surface temperature data of the study area determined based on the obtained night satellite thermal infrared remote sensing images of the study area are calibrated and corrected to obtain the calibrated and corrected night surface temperature data as the final night surface temperature data of the study area.

[0020] In the above-mentioned method for monitoring steam chamber expansion in steam flooding of heavy oil reservoirs based on satellite remote sensing technology, preferably, based on the nighttime surface temperature data of the study area, determining the surface temperature gradient raster data of the study area includes:

[0021] Using the nighttime surface temperature data of the study area, the surface temperature gradient calculation model is used to calculate the horizontal surface temperature gradient data. The temperature gradient classification and data post-processing are then performed to obtain the surface temperature gradient raster data of the study area.

[0022] More preferably, based on the nighttime surface temperature data of the study area, determining the surface temperature gradient grid data of the study area includes:

[0023] The surface temperature gradient data of the study area was calculated using the night surface temperature data of the study area.

[0024] Using the surface temperature gradient calculation model, calculate the temperature gradient value of each grid and generate the temperature gradient grid data map;

[0025] In one embodiment, the surface temperature gradient calculation model is:

[0026]

[0027] Where:

[0028] is the partial derivative in the x direction; is the partial derivative in the y direction; ΔT is the surface temperature gradient.

[0029] In the above-mentioned method for monitoring steam chamber expansion in steam flooding of heavy oil reservoirs based on satellite remote sensing technology, preferably, the surface temperature gradient grid data of the study area is used to classify the steam chamber states into four categories: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone, so as to determine the horizontal distribution data of the steam chamber in steam flooding by the following method:

[0030] Using the surface temperature gradient grid data of the study area, the steam chamber states were classified into four categories: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone. The classified data were vectorized and post-processed to determine the horizontal distribution data of the steam chamber in steam flooding.

[0031] More preferably, the surface temperature gradient grid data of the study area is used to classify the steam chamber states into four types: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone, so as to determine the horizontal distribution data of the steam chamber in the steam flooding process, including:

[0032] 1) Classify steam chamber production areas into four types: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone. Determine the surface temperature gradient classification thresholds for the four types of zones based on the impact of different zone types on the change in surface temperature gradient.

[0033] 2) Based on the surface temperature gradient grid data and classification thresholds in the study area, the grid data of four types of steam chamber regional states in the study area were obtained: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone;

[0034] 3) The raster data of the four types of steam chamber regional states in the study area were spatially vectorized to obtain the horizontal distribution data of the steam chambers;

[0035] Further preferably, in the process of spatial vectorization of the raster data of the four types of steam chamber area states in the study area, patch integration, boundary smoothing, topology reconstruction, and data cropping are performed to obtain the edge distribution data of the four types of steam chamber area states in the horizontal direction of the steam-driven steam chamber in the study area, thereby completing the spatial vectorization processing of the raster data of the edges of the four types of steam chamber area states in the study area.

[0036] In the above-mentioned method for monitoring steam chamber expansion in steam flooding of heavy oil reservoirs based on satellite remote sensing technology, preferably, based on the surface temperature gradient grid data of the study area and in combination with the temperature curve data of the observation wells in the study area, the vertical depths of the steam zone, the hot oil and water zone, and the cold oil zone are determined, thereby determining the vertical distribution data of the steam chamber in the steam flooding process by the following method:

[0037] Based on the surface temperature gradient grid data of the study area, focusing on the effects of heat conduction and heat convection during steam flooding production, combined with reservoir engineering and the principle of energy conservation, and combined with the temperature curve data of observation wells in the study area, a heat transfer model for the vertical temperature of the steam flooding steam chamber was established and solved. The vertical depths of the steam zone, hot oil and water zone, and cold oil zone were determined, thereby determining the vertical distribution data of the steam flooding steam chamber.

[0038] More preferably, based on the surface temperature gradient grid data of the study area and in combination with the temperature curve data of the observation wells in the study area, the vertical depths of the steam zone, the hot oil-water zone, and the cold oil zone are determined, thereby determining the vertical distribution data of the steam chamber of the steam flooding, including:

[0039] 1) Determine the depth of the constant temperature layer based on the observation well temperature curve data;

[0040] 2) Determine the number of reservoir temperature systems based on the observed well temperature curve data;

[0041] 3) Based on the depth of the isothermal layer, the number of reservoir temperature systems, and the observation well temperature curve data, determine the reservoir temperature values ​​at different depths in the study area;

[0042] 4) Based on the heat conduction and convection effects during steam flooding production, and using surface temperature gradient grid data and reservoir temperature values ​​at different depths in the study area, a heat transfer model for the vertical temperature of the steam flooding steam chamber was established and solved. The vertical depths of the steam zone, hot oil-water zone, and cold oil zone were determined, thereby determining the vertical distribution data of the steam flooding steam chamber.

[0043] The vertical heat transfer process in the steam chamber includes the heat convection process of steam rising and condensing, and the heat conduction process in the formation. During the evolution of the steam chamber, the range of the steam zone, hot oil and water zone, and cold oil zone are constantly changing, and there is a convective heat transfer process in the adjacent areas.

[0044] Further preferably, determining the depth of the isothermal layer according to the observation well temperature curve data includes:

[0045] Based on the well temperature instrument logging curve, a linear regression model of the isothermal layer temperature and the surface temperature is established; at the same time, the linear regression model of the isothermal layer temperature and the surface temperature is corrected using the observed well temperature data;

[0046] Further preferably, the number of broken line segments of the reservoir temperature system curve is determined based on the well temperature instrument logging curve; wherein, in the reservoir temperature system curve, it is assumed that the thermal conductivity λ is uniformly layered, that is:

[0047] D0≤z≤D1, λ=λ1; D1≤z≤D2, λ=λ2;......D n-1 ≤z≤D n ,λ=λ n ;

[0048] Among them, D i (i=0, 1, 2…n) is the burial depth of the stratum, where D0 is the burial depth of the isothermal layer;

[0049] Further preferably, the buried depth and slope of each broken line segment in the reservoir temperature system curve are determined based on the well temperature instrument logging curve; and then the reservoir temperature system curve is determined in combination with the night surface temperature data of the study area;

[0050] In one embodiment, the isothermal layer depth is determined based on well temperature meter data; the well temperature meter data is clearly displayed, large in quantity, and easy to collect and is very suitable for determining the isothermal layer depth.

[0051] In the above-mentioned method for monitoring steam chamber expansion in steam flooding of heavy oil reservoirs based on satellite remote sensing technology, preferably, the vertical depths of the steam zone, the hot oil and water zone, and the cold oil zone are determined according to Fourier's law and Newton's law of cooling in combination with reservoir engineering and the principle of energy conservation;

[0052] More preferably, the vertical depths of the steam zone, hot oil water zone, and cold oil zone are determined according to the following formula:

[0053] dQ=αdA(TT W )

[0054]

[0055] Where: dQ is the micro-element convective heat transfer rate, dA is the micro-element heat transfer area, α is the local convective heat transfer coefficient, T is the temperature of the high temperature zone, T W is the temperature of the low temperature zone, λ is the thermal conductivity, and z is the reservoir depth.

[0056] In the above-mentioned method for monitoring steam chamber expansion in steam flooding of heavy oil reservoirs based on satellite remote sensing technology, preferably, reservoir temperature fitting and interpolation processing is performed based on horizontal distribution data of the steam flooding steam chamber and vertical distribution data of the steam flooding steam chamber to determine the three-dimensional distribution data of the steam flooding steam chamber, including:

[0057] Predicting the historical temperature values ​​of the time and space points in the steam flooding steam chamber based on the horizontal distribution data and vertical distribution data of the steam flooding steam chamber;

[0058] Based on the historical temperature values ​​of the time and space points in the steam chamber of the steam flooding, a time and space empirical variation function is established;

[0059] According to the spatiotemporal empirical variogram, a spatiotemporal theoretical variogram model is established;

[0060] Through geostatistical methods and using the space-time theoretical variation function model, the horizontal and vertical distribution data of steam drive steam chambers are subjected to reservoir temperature fitting and interpolation processing to obtain the three-dimensional distribution data of steam drive steam chambers.

[0061] More preferably, the prediction of the historical values ​​of the space-time point temperature in the steam flooding steam chamber is performed based on the following formula:

[0062] and

[0063] Where: z0 is the historical value of the temperature at the time and space point in the steam chamber, z i (i=1, ..., n) is the temperature value of the known time and space point in the i-th steam drive steam chamber, λ i is the weight coefficient;

[0064] More preferably, establishing the spatiotemporal empirical variation function is performed based on the following formula:

[0065]

[0066] Where: h s , h T are spatial interval variables and time interval variables respectively, N(h S , h T ) is the number of point pairs that conform to the defined spatial and temporal separations;

[0067] More preferably, the establishment of the spatiotemporal theoretical variogram model is based on the following formula:

[0068] γ(h s , h T )=γ S (h s )+γ T (h T )+γ ST (h ST )

[0069] Where, γ S (h S ),γ T (h T ),γ ST (h ST ) are spatial variogram, temporal variogram and spatiotemporal variogram respectively;

[0070] Further preferably, the spatial variation function uses a spatial theoretical model such as a Gaussian model, a hemispherical model, an exponential model, a linear model, etc.;

[0071] Further preferably, the time variation function is a spatial theoretical model such as a Gaussian model, a hemispherical model, an exponential model, a linear model, etc.;

[0072] Further preferably, the spatiotemporal variation function uses a spatial theoretical model such as a Gaussian model, a hemispherical model, an exponential model, a linear model, etc.

[0073] More preferably, the reservoir temperature fitting interpolation processing is performed according to a spatiotemporal Kriging interpolation algorithm.

[0074] In the above-mentioned method for monitoring steam chamber expansion in steam flooding heavy oil reservoirs based on satellite remote sensing technology, the spatiotemporal evolution of the steam chamber is predicted based on the horizontal distribution data of the steam chamber in steam flooding at different periods, the vertical distribution data of the steam chamber in steam flooding, and the three-dimensional distribution data of the steam chamber in steam flooding. The following method is used:

[0075] Based on the horizontal distribution data of steam chambers during different periods, the vertical distribution data of steam chambers during steam flooding, and the three-dimensional distribution data of steam chambers during steam flooding, the spatiotemporal evolution velocity of the steam chamber front is determined by fitting regression methods. Then, a spatiotemporal pan-kriging prediction model is established to predict the change process of reservoir temperature and thus obtain the spatiotemporal evolution process of the steam chamber.

[0076] More preferably, the spatiotemporal evolution speed of the steam chamber front is determined according to the following formula:

[0077] Z(x)={Z(s,t)|s∈S,t∈T}

[0078] Where S represents the spatial domain of the steam chamber front edge, S∈R 2 ; T represents the time domain of the steam chamber front, T∈R; Z(x) can be decomposed into the following form: Z(s, t) = M(s, t) + R(s, t), where M(s, t) is the mathematical expectation of the temperature at the spacetime point (s, t) of the steam chamber front, and represents the drift at the spacetime point (s, t); R(s, t) is the remaining part of the temperature at the steam chamber front after removing the trend, and represents the random error of Z(s, t) under the preset scale of fluctuation around the trend M(s, t);

[0079] More preferably, the spatiotemporal pan-Kriging prediction model is established based on the following formula:

[0080]

[0081] where Z * (s, t) is the temperature change value, which is used to represent the Kriging prediction value of the spatiotemporal trend of Z(s, t); is the expected value of the spatiotemporal trend M(s, t) of the steam chamber front edge temperature at the spatiotemporal point (s, t); is the spatiotemporal ordinary kriging prediction value of the residual R(s, t) at the spatiotemporal point (s, t).

[0082] The present invention also provides a heavy oil reservoir steam flooding steam chamber expansion monitoring system based on satellite remote sensing technology, wherein the system comprises:

[0083] Data acquisition module: used to obtain satellite thermal infrared remote sensing images of the study area at night;

[0084] Surface temperature determination module: used to determine the night surface temperature data of the study area based on the acquired satellite thermal infrared remote sensing images of the study area at night;

[0085] Surface temperature gradient determination module: used to determine the surface temperature gradient raster data of the study area based on the night surface temperature data of the study area;

[0086] Horizontal distribution determination module: It is used to use the surface temperature gradient grid data of the study area to classify the steam chamber states into four categories: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone, so as to determine the horizontal distribution data of the steam chamber in steam flooding;

[0087] Vertical distribution determination module: used to determine the vertical depths of the steam zone, hot oil and water zone, and cold oil zone based on the surface temperature gradient grid data of the study area and the temperature curve data of the observation wells in the study area, thereby determining the vertical distribution data of the steam chamber of the steam flooding;

[0088] Three-dimensional determination module: used to perform reservoir temperature fitting and interpolation processing based on the horizontal distribution data of the steam drive steam chamber and the vertical distribution data of the steam drive steam chamber, and determine the three-dimensional distribution data of the steam drive steam chamber;

[0089] Steam chamber evolution process determination module: used to predict the spatiotemporal evolution process of the steam chamber based on the horizontal distribution data of the steam chamber in the steam drive, the vertical distribution data of the steam chamber in the steam drive, and the three-dimensional distribution data of the steam chamber in the steam drive at different periods.

[0090] In the above-mentioned heavy oil reservoir steam flooding steam chamber expansion monitoring system based on satellite remote sensing technology, preferably, the surface temperature determination module includes:

[0091] Surface reflectance determination submodule: used to perform radiometric calibration, atmospheric correction, and geometric correction on the acquired satellite thermal infrared remote sensing images of the study area at night to obtain remote sensing surface reflectance data;

[0092] Surface temperature determination submodule; used to invert and calculate the thermal infrared band of remote sensing surface reflectance data, estimate important parameters such as brightness temperature, surface emissivity, atmospheric average temperature, and atmospheric water vapor content, and obtain the night thermal infrared remote sensing inversion surface temperature data, that is, the night surface temperature data of the study area.

[0093] In the above-mentioned heavy oil reservoir steam flooding steam chamber expansion monitoring system based on satellite remote sensing technology, preferably, the surface temperature determination module further includes:

[0094] Surface temperature calibration submodule: It is used to calibrate the night surface temperature data of the study area obtained based on the obtained satellite thermal infrared remote sensing images of the study area at night based on the measured temperature data of the observation wells in the study area. The calibrated and corrected night surface temperature data is used as the final night surface temperature data of the study area.

[0095] In the above-mentioned heavy oil reservoir steam flooding steam chamber expansion monitoring system based on satellite remote sensing technology, preferably, the surface temperature gradient determination module includes:

[0096] The first processing submodule is used to calculate the surface temperature gradient data of the study area using the surface temperature data of the study area at night;

[0097] The second processing submodule is used to calculate the temperature gradient value of each grid using the surface temperature gradient calculation model and generate a temperature gradient grid data map.

[0098] In the above-mentioned heavy oil reservoir steam flooding steam chamber expansion monitoring system based on satellite remote sensing technology, preferably, the horizontal distribution determination module includes:

[0099] The classification threshold determination submodule is used to classify steam chamber production areas into four types: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone. Based on the impact of different zone types on the change of surface temperature gradient, the surface temperature gradient classification thresholds for the four types of zone states are determined.

[0100] Raster data classification submodule: used to obtain raster data of four types of steam chamber regional states in the study area, namely steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone, based on the surface temperature gradient raster data and classification threshold of the study area;

[0101] Horizontal distribution determination submodule: It is used to spatially vectorize the raster data of the four types of steam chamber regional states in the study area, so as to obtain the horizontal distribution data of the steam drive steam chamber.

[0102] In the above-mentioned heavy oil reservoir steam flooding steam chamber expansion monitoring system based on satellite remote sensing technology, preferably, the vertical distribution determination module includes:

[0103] Isothermal layer depth determination submodule: used to determine the isothermal layer depth based on the observation well temperature curve data;

[0104] Temperature system curve qualitative analysis submodule: used to determine the number of reservoir temperature systems based on the observed well temperature curve data;

[0105] Curve data determination submodule: used to determine the reservoir temperature values ​​at different depths in the study area based on the depth of the isothermal layer, the number of reservoir temperature systems, and the observation well temperature curve data;

[0106] Surface temperature heat transfer determination submodule: It is used to establish and solve the heat transfer model of the vertical temperature of the steam drive steam chamber based on the heat conduction and heat convection effects in the steam drive production process, according to the surface temperature gradient grid data, and combined with the temperature values ​​of oil reservoirs at different depths in the study area, to determine the vertical depths of the steam zone, hot oil and water zone, and cold oil zone, thereby determining the vertical distribution data of the steam drive steam chamber.

[0107] In the above-mentioned heavy oil reservoir steam flooding steam chamber expansion monitoring system based on satellite remote sensing technology, preferably, the three-dimensional determination module includes:

[0108] The submodule for determining the historical value of the temperature at a time-space point in the steam drive steam chamber is used to predict the historical value of the temperature at a time-space point in the steam drive steam chamber based on the horizontal distribution data and the vertical distribution data of the steam drive steam chamber;

[0109] The spatiotemporal empirical variation function determination submodule is used to establish the spatiotemporal empirical variation function based on the historical temperature values ​​of the spatiotemporal points in the steam chamber of the steam drive;

[0110] Spatiotemporal theoretical variation function model determination submodule: used to establish a spatiotemporal theoretical variation function model based on the spatiotemporal empirical variation function;

[0111] 3D distribution determination submodule: This module is used to perform reservoir temperature fitting and interpolation processing on the horizontal and vertical distribution data of steam drive steam chambers using geostatistical methods and a time-space theory variation function model to obtain 3D distribution data of steam drive steam chambers.

[0112] More preferably, the time-space point temperature history value determination submodule predicts the time-space point temperature history value in the steam flooding steam chamber based on the following formula:

[0113] Head

[0114] Where: z0 is the historical value of the temperature at the time and space point in the steam chamber, z i (i=1, ..., n) is the temperature value of the known time and space point in the i-th steam flooding steam chamber, and λi is the weight coefficient;

[0115] More preferably, the spatiotemporal empirical variation function determination submodule establishes the spatiotemporal empirical variation function based on the following formula:

[0116]

[0117] Where: h S , h T are spatial interval variables and time interval variables respectively, N(h S , h T ) is the number of point pairs that conform to the defined spatial and temporal separations;

[0118] More preferably, the spatiotemporal theoretical variogram model determination submodule establishes the spatiotemporal theoretical variogram model based on the following formula:

[0119] γ(h S , h T )=γ S (h s)+γ T (h T )+γ ST (h ST )

[0120] Where, γ S (h s ),γ T (h T ),γ ST (h ST ) are spatial variogram, temporal variogram and spatiotemporal variogram respectively.

[0121] In the above-mentioned heavy oil reservoir steam flooding steam chamber expansion monitoring system based on satellite remote sensing technology, preferably, the steam chamber evolution process determination module includes:

[0122] The steam chamber front spatiotemporal evolution velocity determination submodule is used to determine the spatiotemporal evolution velocity of the steam chamber front through a fitting regression method based on the horizontal distribution data of the steam chamber, the vertical distribution data of the steam chamber, and the three-dimensional distribution data of the steam chamber during different periods of steam drive.

[0123] The spatiotemporal evolution process determination submodule is used to establish a spatiotemporal universal Kriging prediction model based on the spatiotemporal evolution velocity of the steam chamber front, predict the change process of the reservoir temperature, and thus obtain the spatiotemporal evolution process of the steam chamber;

[0124] More preferably, the steam chamber front spatiotemporal evolution speed determining submodule determines the steam chamber front spatiotemporal evolution speed based on the following formula:

[0125] Z(x)={Z(s,t)|s∈S,t∈T}

[0126] Where S represents the spatial domain of the steam chamber front edge, S∈R 2 ; T represents the time domain of the steam chamber front, T∈R; Z(x) can be decomposed into the following form: Z(s, t) = M(s, t) + R(s, t), where M(s, t) is the mathematical expectation of the temperature at the spacetime point (s, t) of the steam chamber front, and represents the drift at the spacetime point (s, t); R(s, t) is the remaining part of the temperature at the steam chamber front after removing the trend, and represents the random error of Z(s, t) under the preset scale of fluctuation around the trend M(s, t);

[0127] More preferably, the spatiotemporal evolution process determination submodule establishes a spatiotemporal pan-Kriging prediction model based on the following formula:

[0128]

[0129] where Z *(s, t) is the temperature change value, which is used to represent the Kriging prediction value of the spatiotemporal trend of Z(s, t); is the expected value of the spatiotemporal trend M(s, t) of the steam chamber front edge temperature at the spatiotemporal point (s, t); is the spatiotemporal ordinary kriging prediction value of the residual R(s, t) at the spatiotemporal point (s, t).

[0130] The present invention also provides a device for monitoring steam chamber expansion in steam flooding of heavy oil reservoirs based on satellite remote sensing technology, comprising a processor and a memory; wherein,

[0131] Memory for storing computer programs;

[0132] The processor is configured to implement the steps of the above-mentioned method for monitoring steam chamber expansion in steam flooding of heavy oil reservoirs based on satellite remote sensing technology when executing the program stored in the memory.

[0133] The present invention also provides a computer-readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned method for monitoring steam chamber expansion in steam flooding of heavy oil reservoirs based on satellite remote sensing technology.

[0134] The satellite remote sensing-based method for monitoring steam chamber expansion in heavy oil reservoirs provides a method for using satellite remote sensing imagery to achieve simple, automated, and real-time monitoring of steam chamber expansion in heavy oil reservoirs. This method, combined with observation well data, allows for timely acquisition of highly accurate three-dimensional spatial features of the steam chamber. In the preferred technical solution, verification and calibration are also performed using ground monitoring and observation well data. The technical solution provided by the method provides the following beneficial effects:

[0135] (1) The technical solution provided by the present invention can realize real-time and accurate monitoring of the expansion of the steam chamber of steam drive in heavy oil reservoirs, and can calculate the steam chamber expansion speed in real time, providing technical support for operations in different production stages of steam drive; it is helpful to discover new drilling targets and implement new production increase measures, and accurately calculate the recovery rate of the oil field; it is helpful to deeply understand the details of the underground oil reservoir temperature and crude oil saturation distribution, and have a comprehensive grasp of the entire oil reservoir.

[0136] (2) The technical solution provided by the present invention has a wide monitoring range, is not affected by reservoir heterogeneity, has no blind spots, integrates the sky and the earth, improves quality and efficiency, takes less time, and has high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0137] Figure 1 The present invention provides a flowchart of a method for monitoring steam chamber expansion in heavy oil reservoirs using satellite remote sensing technology according to an embodiment of the present invention.

[0138] Figure 2 A schematic structural diagram of a heavy oil reservoir steam flooding steam chamber expansion monitoring system based on satellite remote sensing technology provided by one embodiment of the present invention.

[0139] Figure 3 A schematic structural diagram of a device for monitoring steam chamber expansion in steam flooding of heavy oil reservoirs based on satellite remote sensing technology is provided in one embodiment of the present invention.

[0140] Figure 4 This is a surface temperature gradient graded grid data diagram in one embodiment.

[0141] Figure 5 Schematic diagram of a temperature system curve in one embodiment.

[0142] Figure 6 FIG. 1 is a schematic diagram of three-dimensional data in an embodiment. DETAILED DESCRIPTION

[0143] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0144] The principles and spirit of the present invention are described in detail below with reference to several representative embodiments of the present invention.

[0145] See also Figure 1 In order to achieve the above-mentioned object, the present invention provides a method for monitoring steam chamber expansion in steam flooding of heavy oil reservoirs based on satellite remote sensing technology, wherein the method comprises:

[0146] Step S1: Real-time acquisition of satellite thermal infrared remote sensing images and observation well temperature curve data (such as observation well temperature measured data and observation well temperature instrument logging curves) of the study area at night;

[0147] Step S2: determining the nighttime surface temperature data of the study area based on the thermal infrared remote sensing image;

[0148] Step S3: Based on the night surface temperature data of the study area, determine the surface temperature gradient raster data of the study area;

[0149] Step S4: Using the surface temperature gradient grid data of the study area, the steam chamber states are classified into four categories: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone (e.g. Figure 4 As shown), the horizontal distribution data of the steam chamber can be determined;

[0150] Step S5: Based on the surface temperature gradient grid data of the study area and the temperature curve data of the observation wells in the study area, the vertical depths of the steam zone, the hot oil-water zone, and the cold oil zone are determined, thereby determining the vertical distribution data of the steam chamber of the steam flooding;

[0151] Step S6: Based on the horizontal distribution data of the steam drive steam chamber and the vertical distribution data of the steam drive steam chamber, perform reservoir temperature fitting interpolation processing to determine the three-dimensional distribution data of the steam drive steam chamber (such as Figure 6 shown);

[0152] Step S7: predicting the spatiotemporal evolution of the steam chamber based on the horizontal distribution data of the steam chamber, the vertical distribution data of the steam chamber, and the three-dimensional distribution data of the steam chamber during different periods of steam drive.

[0153] In one embodiment, step S2 includes:

[0154] The obtained nighttime satellite thermal infrared remote sensing images of the study area were pre-processed by radiometric calibration, atmospheric correction, geometric correction, etc. to obtain remote sensing surface reflectance data;

[0155] The thermal infrared band of remote sensing surface reflectance data is inverted and calculated, and important parameters such as brightness temperature, surface emissivity, atmospheric mean temperature, and atmospheric water vapor content are estimated to obtain the nighttime thermal infrared remote sensing inversion surface temperature data, i.e., the nighttime surface temperature data of the study area.

[0156] Furthermore, the inversion calculation is performed using a single-window algorithm, a single-channel algorithm, or a radiation transfer equation method.

[0157] In one embodiment, step S2 further includes:

[0158] Based on the measured temperature data of the observation wells in the study area, the night surface temperature data of the study area determined based on the obtained night satellite thermal infrared remote sensing images of the study area are calibrated and corrected to obtain the calibrated and corrected night surface temperature data as the final night surface temperature data of the study area.

[0159] In one embodiment, step S3 is implemented as follows:

[0160] Using the nighttime surface temperature data of the study area, the surface temperature gradient calculation model is used to calculate the horizontal surface temperature gradient data. The temperature gradient classification and data post-processing are then performed to obtain the surface temperature gradient raster data of the study area.

[0161] Further, step S3 includes:

[0162] The surface temperature gradient data of the study area was calculated using the night surface temperature data of the study area.

[0163] Using the surface temperature gradient calculation model, calculate the temperature gradient value of each grid and generate the temperature gradient grid data map

[0164] Among them, the surface temperature gradient calculation model is preferably:

[0165]

[0166] Where:

[0167] is the partial derivative in the x direction; is the partial derivative in the y direction; ΔT is the surface temperature gradient.

[0168] In one embodiment, step S4 is implemented as follows:

[0169] Using the surface temperature gradient grid data of the study area, the steam chamber states were classified into four categories: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone. The classified data were vectorized and post-processed to determine the horizontal distribution data of the steam chamber in steam flooding.

[0170] Further, step S4 includes:

[0171] 1) Classify steam chamber production areas into four types: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone. Determine the surface temperature gradient classification thresholds for the four types of zones based on the impact of different zone types on the change in surface temperature gradient.

[0172] 2) Based on the surface temperature gradient grid data and classification thresholds in the study area, the grid data of four types of steam chamber regional states in the study area were obtained: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone;

[0173] 3) The raster data of the four types of steam chamber regional states in the study area were spatially vectorized to obtain the horizontal distribution data of the steam chambers;

[0174] Furthermore, in the process of spatial vectorization of the raster data of the four types of steam chamber regional states in the study area, patch integration, boundary smoothing, topology reconstruction, and data clipping are performed to obtain the edge distribution data of the four types of steam chamber regional states in the horizontal direction of the steam drive steam chamber in the study area, thereby completing the spatial vectorization processing of the raster data of the edges of the four types of steam chamber regional states in the study area.

[0175] In one embodiment, step S5 is implemented as follows:

[0176] Based on the surface temperature gradient grid data of the study area, focusing on the effects of heat conduction and heat convection during steam flooding production, combined with reservoir engineering and the principle of energy conservation, and combined with the temperature curve data of observation wells in the study area, a heat transfer model for the vertical temperature of the steam flooding steam chamber was established and solved. The vertical depths of the steam zone, hot oil and water zone, and cold oil zone were determined, thereby determining the vertical distribution data of the steam flooding steam chamber.

[0177] Further, step S5 includes:

[0178] 1) Determine the depth of the constant temperature layer based on the observation well temperature curve data;

[0179] 2) Determine the number of reservoir temperature systems based on the observed well temperature curve data;

[0180] 3) Based on the depth of the isothermal layer, the number of reservoir temperature systems, and the observation well temperature curve data, determine the reservoir temperature values ​​at different depths in the study area;

[0181] 4) Based on the heat conduction and convection effects during steam flooding production, and using surface temperature gradient grid data and reservoir temperature values ​​at different depths in the study area, a heat transfer model for the vertical temperature of the steam flooding steam chamber was established and solved. The vertical depths of the steam zone, hot oil-water zone, and cold oil zone were determined, thereby determining the vertical distribution data of the steam flooding steam chamber.

[0182] The vertical heat transfer process in the steam chamber includes the heat convection process of steam rising and condensing, and the heat conduction process in the formation. During the evolution of the steam chamber, the range of the steam zone, hot oil and water zone, and cold oil zone are constantly changing, and there is a convective heat transfer process in the adjacent areas.

[0183] Furthermore, according to the observation well temperature curve data, the depth of the isothermal layer is determined including:

[0184] Based on the well temperature instrument logging curve, a linear regression model of the isothermal layer temperature and the surface temperature is established; at the same time, the linear regression model of the isothermal layer temperature and the surface temperature is corrected using the observed well temperature data;

[0185] Furthermore, the number of broken line segments in the reservoir temperature system curve is determined based on the well temperature logging curve. In the reservoir temperature system curve, it is assumed that the thermal conductivity λ is uniformly layered, that is:

[0186] D0≤z≤D1, λ=λ1; D1≤z≤D2, λ=λ2;......D n-1 ≤z≤D n ,λ=λ n ;

[0187] Among them, D i (i=0, 1, 2…n) is the burial depth of the stratum, where D0 is the burial depth of the isothermal layer;

[0188] Furthermore, according to the well temperature instrument logging curve, the burial depth and slope of each broken line segment in the reservoir temperature system curve are determined; then, combined with the night surface temperature data of the study area, the reservoir temperature system curve (such as Figure 5 shown);

[0189] Among them, the data from well temperature meters are obvious, large in quantity and easy to collect, making them very suitable for determining the depth of the isothermal layer.

[0190] Furthermore, combining reservoir engineering with the principle of energy conservation, and based on Fourier's law and Newton's law of cooling, the vertical depths of the steam zone, hot oil-water zone, and cold oil zone are determined;

[0191] Furthermore, the vertical depths of the steam zone, hot oil and water zone, and cold oil zone are determined according to the following formula:

[0192] dQ=αdA(TT W )

[0193]

[0194] Where: dQ is the micro-element convective heat transfer rate, dA is the micro-element heat transfer area, α is the local convective heat transfer coefficient, T is the temperature of the high temperature zone, T W is the temperature of the low temperature zone, λ is the thermal conductivity, and z is the reservoir depth.

[0195] In one embodiment, step S6 includes:

[0196] Predicting the historical temperature values ​​of the time and space points in the steam flooding steam chamber based on the horizontal distribution data and vertical distribution data of the steam flooding steam chamber;

[0197] Based on the historical temperature values ​​of the time and space points in the steam chamber of the steam flooding, a time and space empirical variation function is established;

[0198] According to the spatiotemporal empirical variogram, a spatiotemporal theoretical variogram model is established;

[0199] Through geostatistical methods and using the space-time theoretical variation function model, the horizontal and vertical distribution data of steam drive steam chambers are subjected to reservoir temperature fitting and interpolation processing to obtain the three-dimensional distribution data of steam drive steam chambers.

[0200] Furthermore, the prediction of the historical values ​​of the space-time point temperature in the steam chamber of the steam flooding is based on the following formula:

[0201] and

[0202] Where: z0 is the historical value of the temperature at the time and space point in the steam chamber, z i(i=1, ..., n) is the temperature value of the known time and space point in the i-th steam drive steam chamber, λ i is the weight coefficient;

[0203] Furthermore, the spatiotemporal empirical variation function is established based on the following formula:

[0204]

[0205] Where: h S , h T are spatial interval variables and time interval variables respectively, N(h S , h T ) is the number of point pairs that conform to the defined spatial and temporal separations;

[0206] Furthermore, the spatiotemporal theoretical variation function model is established based on the following formula:

[0207] γ(h s , h T )=γ S (h s )+γ T (h T )+γ ST (h ST )

[0208] Where, γ S (h S ),γ T (h T ),γ ST (h ST ) are spatial variogram, temporal variogram and spatiotemporal variogram respectively;

[0209] Furthermore, the spatial variogram uses spatial theoretical models such as Gaussian model, hemispherical model, exponential model, and linear model;

[0210] Furthermore, the time variogram uses spatial theoretical models such as Gaussian model, hemispherical model, exponential model, and linear model;

[0211] Furthermore, the space-time variation function uses spatial theoretical models such as Gaussian model, hemispherical model, exponential model, and linear model.

[0212] Furthermore, the reservoir temperature fitting interpolation processing is performed according to the spatiotemporal Kriging interpolation algorithm.

[0213] Among them, based on the horizontal distribution data of steam-driven steam chambers in different periods, the vertical distribution data of steam-driven steam chambers and the three-dimensional distribution data of steam-driven steam chambers, in the process of predicting the spatiotemporal evolution of the steam chambers, the night surface temperature data of the study area determined based on the night satellite thermal infrared remote sensing images, the steam drive temperature system simulation and calculation, and the observation well temperature measured data are continuously iteratively fitted and optimized.

[0214] In one embodiment, the steps for establishing a spatiotemporal theoretical variogram model may be as follows:

[0215] 1. Define the non-stationary space-time variable Z(x) = {Z(s, t)|s∈S, t∈T}, where S represents the spatial domain and S∈R 2 ; T represents the spatial domain, T∈R.

[0216] 2. Using the horizontal distribution model of the steam drive steam chamber and the vertical distribution model of the steam drive steam chamber, predict the temperature history value of the unmeasured time and space point (s0, t0) in the current steam chamber, that is:

[0217] and

[0218] Where: z0 is the temperature history value of the unmeasured time and space point (s0, t0) in the current steam chamber, z i (i=1, ..., n) is the temperature value of the current spacetime point (s0, t0) in the steam chamber, λ i is the weight coefficient.

[0219] Under the condition of the intrinsic hypothesis, the spatiotemporal empirical variation function is defined to calculate the spatiotemporal semivariance value.

[0220]

[0221] Where: h S , h T are spatial interval variables and time interval variables respectively, N(h S , h T ) is the number of point pairs that meet the defined spatial and temporal intervals.

[0222] 3. Based on the Bilonick space-time separation model, define the space-time theoretical variation function model:

[0223] γ(h s , h T )=γ S (h s )+γ T (h T )+γ ST (h ST )

[0224] Where, γ S (h S ),γ T (h T ),γ ST (h ST ) are space, time and spatiotemporal variation functions respectively, and their forms can follow the spatial theoretical models such as Gaussian model, hemispherical model, exponential model, linear model, etc.

[0225] 4. Based on the acquired theoretical spatiotemporal variation model of the three-dimensional distribution of steam chambers, the observation well temperature data, the horizontal distribution of steam chambers during steam drive, and the vertical distribution of steam chambers during steam drive are used to interpolate according to the spatiotemporal Kriging interpolation algorithm to obtain the three-dimensional spatiotemporal distribution of each spatiotemporal point in the steam chamber.

[0226] In one embodiment, step S7 is performed as follows:

[0227] Based on the horizontal distribution data of steam chambers during different periods, the vertical distribution data of steam chambers during steam flooding, and the three-dimensional distribution data of steam chambers during steam flooding, the spatiotemporal evolution velocity of the steam chamber front is determined by fitting regression methods. Then, a spatiotemporal pan-kriging prediction model is established to predict the change process of reservoir temperature and thus obtain the spatiotemporal evolution process of the steam chamber.

[0228] Furthermore, the temporal and spatial evolution speed of the steam chamber front is determined according to the following formula:

[0229] Z(x)={Z(s,t)|s∈S,t∈T}

[0230] Where S represents the spatial domain of the steam chamber front edge, S∈R 2 ; T represents the time domain of the steam chamber front, T∈R; Z(x) can be decomposed into the following form: Z(s, t) = M(s, t) + R(s, t), where M(s, t) is the mathematical expectation of the temperature at the spacetime point (s, t) of the steam chamber front, and represents the drift at the spacetime point (s, t); R(s, t) is the remaining part of the temperature at the steam chamber front after removing the trend, and represents the random error of Z(s, t) under the preset scale of fluctuation around the trend M(s, t);

[0231] Furthermore, the spatiotemporal pan-Kriging prediction model is established based on the following formula:

[0232]

[0233] where Z * (s, t) is the temperature change value, which is used to represent the Kriging prediction value of the spatiotemporal trend of Z(s, t); is the expected value of the spatiotemporal trend M(s, t) of the steam chamber leading edge temperature at the spatiotemporal point (s, t); is the spatiotemporal ordinary kriging prediction value of the residual R(s, t) at the spatiotemporal point (s, t).

[0234] In one embodiment, iterative fitting is continuously performed on the three-dimensional distribution data of the steam flooding steam chamber to obtain a steam chamber front evolution model. Based on the monitoring results of the horizontal distribution, vertical distribution, and three-dimensional distribution of the steam chamber at different periods, the spatiotemporal temperature of the steam chamber front is obtained by fitting regression method, and then a spatiotemporal pan-kriging prediction model is established to predict the change process of the reservoir temperature, thereby obtaining the spatiotemporal evolution process of the steam chamber. The steps may include:

[0235] 1) Calculate the leading edge temperature of the steam chamber: Z(x) = {Z(s, t) | s∈S, t∈T}, where S represents the spatial domain of the leading edge temperature of the steam chamber, and S∈R 2 ; T represents the time domain of the steam chamber front edge temperature, T∈R; Z(x) can be decomposed into the following form: Z(s, t) = M(s, t) + R(s, t)

[0236] Where M(s, t) is the mathematical expectation of the temperature at the spacetime point (s, t) at the leading edge of the steam chamber, representing the drift at point (s, t), that is, the trend part; R(s, t) is the remaining part of the temperature at the leading edge of the steam chamber after removing the trend, representing the random error of Z(s, t) at a smaller scale around the trend M(s, t), that is, the residual part.

[0237] 2) Based on the front edge temperature of the steam chamber, the change in oil layer temperature is predicted according to the following formula:

[0238] Assume that the spatiotemporal trend kriging prediction value of Z(s, t) is Z * (s, t), ensure Z * (s, t) is the optimal unbiased estimate of Z(s, t):

[0239]

[0240] Where, is the expected value of the spatiotemporal trend M(s, t) of the steam chamber leading edge temperature at the spatiotemporal point (s, t); is the spatiotemporal ordinary kriging prediction value of the residual R(s, t) at the spatiotemporal point (s, t).

[0241] Among them, after continuously iteratively fitting the three-dimensional distribution data of the steam-driven steam chamber to obtain the steam chamber front edge evolution model, the expansion speed, expansion shape and expansion size of the steam chamber are calculated in real time according to the steam chamber front edge evolution model.

[0242] In the embodiment, based on the horizontal, vertical, and three-dimensional monitoring results of the steam chamber for multiple periods, iterative fitting and model optimization can be continuously performed through satellite remote sensing surface temperature inversion, steam drive temperature system simulation and estimation, and observation well temperature measured data, to analyze the steam chamber front edge evolution speed, obtain the steam chamber front edge evolution model, and predict the steam chamber evolution process.

[0243] An embodiment of the present invention further provides a system for monitoring steam chamber expansion in steam flooding of heavy oil reservoirs based on satellite remote sensing technology. Preferably, the system is used to implement the above method embodiment.

[0244] Figure 2 is a structural block diagram of a steam chamber expansion monitoring system for steam flooding of heavy oil reservoirs based on satellite remote sensing technology according to an embodiment of the present invention. Figure 2 As shown, the system includes:

[0245] Data acquisition module 21: used to obtain satellite thermal infrared remote sensing images of the study area at night;

[0246] The surface temperature determination module 22 is used to determine the surface temperature data of the study area at night based on the acquired satellite thermal infrared remote sensing image of the study area at night;

[0247] The surface temperature gradient determination module 23 is used to determine the surface temperature gradient grid data of the study area based on the night surface temperature data of the study area;

[0248] Horizontal distribution determination module 24: for determining horizontal distribution data of steam chambers in steam flooding by using the surface temperature gradient grid data of the study area and classifying the steam chamber states into four categories: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone;

[0249] Vertical distribution determination module 25: used to determine the vertical depths of the steam zone, hot oil-water zone, and cold oil zone based on the surface temperature gradient grid data of the study area and the temperature curve data of the observation wells in the study area, thereby determining the vertical distribution data of the steam chamber of the steam flooding;

[0250] A three-dimensional determination module 26 is configured to perform reservoir temperature fitting and interpolation processing based on the horizontal distribution data of the steam drive steam chamber and the vertical distribution data of the steam drive steam chamber to determine the three-dimensional distribution data of the steam drive steam chamber;

[0251] The steam chamber evolution process determination module 27 is used to predict the spatiotemporal evolution process of the steam chamber based on the horizontal distribution data of the steam chamber, the vertical distribution data of the steam chamber and the three-dimensional distribution data of the steam chamber during different periods of steam drive.

[0252] In one embodiment, the ground surface temperature determination module 22 includes:

[0253] Surface reflectance determination submodule: used to perform radiometric calibration, atmospheric correction, and geometric correction on the acquired satellite thermal infrared remote sensing images of the study area at night to obtain remote sensing surface reflectance data;

[0254] Surface temperature determination submodule; used to invert and calculate the thermal infrared band of remote sensing surface reflectance data, estimate important parameters such as brightness temperature, surface emissivity, atmospheric average temperature, and atmospheric water vapor content, and obtain the night thermal infrared remote sensing inversion surface temperature data, that is, the night surface temperature data of the study area.

[0255] In one embodiment, the ground surface temperature determination module 22 further includes:

[0256] Surface temperature calibration submodule: It is used to calibrate the night surface temperature data of the study area obtained based on the obtained satellite thermal infrared remote sensing images of the study area at night based on the measured temperature data of the observation wells in the study area. The calibrated and corrected night surface temperature data is used as the final night surface temperature data of the study area.

[0257] In one embodiment, the surface temperature gradient determination module 23 includes:

[0258] The first processing submodule is used to calculate the surface temperature gradient data of the study area using the surface temperature data of the study area at night;

[0259] The second processing submodule is used to calculate the temperature gradient value of each grid using the surface temperature gradient calculation model and generate a temperature gradient grid data map.

[0260] In one embodiment, the horizontal distribution determination module 24 includes:

[0261] The classification threshold determination submodule is used to classify steam chamber production areas into four types: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone. Based on the impact of different zone types on the change of surface temperature gradient, the surface temperature gradient classification thresholds for the four types of zone states are determined.

[0262] Raster data classification submodule: used to obtain raster data of four types of steam chamber regional states in the study area, namely steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone, based on the surface temperature gradient raster data and classification threshold of the study area;

[0263] Horizontal distribution determination submodule: It is used to spatially vectorize the raster data of the four types of steam chamber regional states in the study area, so as to obtain the horizontal distribution data of the steam drive steam chamber.

[0264] In one embodiment, the vertical distribution determination module 25 includes:

[0265] Isothermal layer depth determination submodule: used to determine the isothermal layer depth based on the observation well temperature curve data;

[0266] Temperature system curve qualitative analysis submodule: used to determine the number of reservoir temperature systems based on the observed well temperature curve data;

[0267] Curve data determination submodule: used to determine the reservoir temperature values ​​at different depths in the study area based on the depth of the isothermal layer, the number of reservoir temperature systems, and the observation well temperature curve data;

[0268] Surface temperature heat transfer determination submodule: It is used to establish and solve the heat transfer model of the vertical temperature of the steam drive steam chamber based on the heat conduction and heat convection effects in the steam drive production process, according to the surface temperature gradient grid data, and combined with the temperature values ​​of oil reservoirs at different depths in the study area, to determine the vertical depths of the steam zone, hot oil and water zone, and cold oil zone, thereby determining the vertical distribution data of the steam drive steam chamber.

[0269] In one embodiment, the 3D condition determination module 26 includes:

[0270] The submodule for determining the historical value of the temperature at a time-space point in the steam drive steam chamber is used to predict the historical value of the temperature at a time-space point in the steam drive steam chamber based on the horizontal distribution data and the vertical distribution data of the steam drive steam chamber;

[0271] The spatiotemporal empirical variation function determination submodule is used to establish the spatiotemporal empirical variation function based on the historical temperature values ​​of the spatiotemporal points in the steam chamber of the steam drive;

[0272] Spatiotemporal theoretical variation function model determination submodule: used to establish a spatiotemporal theoretical variation function model based on the spatiotemporal empirical variation function;

[0273] 3D distribution determination submodule: This module is used to perform reservoir temperature fitting and interpolation processing on the horizontal and vertical distribution data of steam drive steam chambers using geostatistical methods and a time-space theory variation function model to obtain 3D distribution data of steam drive steam chambers.

[0274] Furthermore, the time-space point temperature history value determination submodule predicts the time-space point temperature history value in the steam flooding steam chamber based on the following formula:

[0275] Head

[0276] Where: z0 is the historical value of the temperature at the time and space point in the steam chamber, z i (i=1, ..., n) is the temperature value of the known time and space point in the i-th steam drive steam chamber, λ i is the weight coefficient;

[0277] Furthermore, the spatiotemporal empirical variation function determination submodule establishes the spatiotemporal empirical variation function based on the following formula:

[0278]

[0279] Where: h s , h T are spatial interval variables and time interval variables respectively, N(h s , h T ) is the number of point pairs that conform to the defined spatial and temporal separations;

[0280] Furthermore, the spatiotemporal theoretical variation function model determination submodule establishes the spatiotemporal theoretical variation function model based on the following formula:

[0281] γ(h s , h T )=γ S (h S )+γ T (h T )+γ ST (h ST )

[0282] Where, γ S (h S ),γ T (h T ),γ ST (h ST ) are spatial variogram, temporal variogram and spatiotemporal variogram respectively.

[0283] In one embodiment, the steam chamber evolution process determination module 27 includes:

[0284] The steam chamber front spatiotemporal evolution velocity determination submodule is used to determine the spatiotemporal evolution velocity of the steam chamber front through a fitting regression method based on the horizontal distribution data of the steam chamber in different periods, the vertical distribution data of the steam chamber in different periods, and the monitoring results of the three-dimensional status of the steam chamber;

[0285] The spatiotemporal evolution process determination submodule is used to establish a spatiotemporal universal Kriging prediction model based on the spatiotemporal evolution velocity of the steam chamber front, predict the change process of the reservoir temperature, and thus obtain the spatiotemporal evolution process of the steam chamber;

[0286] Furthermore, the steam chamber front spatiotemporal evolution speed determination submodule determines the steam chamber front spatiotemporal evolution speed based on the following formula:

[0287] Z(x)={Z(s,t)|s∈S,t∈T}

[0288] Among them, S represents the spatial domain of the front edge of the steam chamber, S ∈ R 2 ; T represents the time domain of the front edge of the steam chamber, T ∈ R; Z(x) can be decomposed into the following form: Z(s, t) = M(s, t) + R(s, t), where M(s, t) is the mathematical expectation of the temperature at the space-time point (s, t) of the front edge of the steam chamber, representing the drift at the space-time point (s, t); R(s, t) is the remaining part of the temperature of the front edge of the steam chamber after removing the trend, representing the random error under the preset scale of the fluctuation of Z(s, t) around the trend M(s, t);

[0289] Furthermore, the space-time evolution process determination sub-module establishes a space-time universal Kriging prediction model based on the following formula:

[0290]

[0291] where Z * (s, t) is the change value of the temperature, used to represent the Kriging prediction value of the trend of the space-time point of Z(s, t); is the expected value of the space-time trend M(s, t) of the temperature of the front edge of the steam chamber at the space-time point (s, t); is the space-time ordinary Kriging prediction value of the residual R(s, t) at the space-time point (s, t).

[0292] Figure 3 is a schematic diagram of the steam chamber expansion monitoring device for heavy oil reservoirs by steam flooding based on satellite remote sensing technology in an embodiment of the present invention. Figure 3 The steam chamber expansion monitoring device for heavy oil reservoirs by steam flooding based on satellite remote sensing technology shown is a general data processing device, which includes a general computer hardware structure, and at least includes a processor 1000 and a memory 1111; the processor 1000 is used to execute the steam chamber expansion monitoring program for heavy oil reservoirs by steam flooding based on satellite remote sensing technology stored in the memory, so as to implement the steam chamber expansion monitoring method for heavy oil reservoirs by steam flooding based on satellite remote sensing technology in each method embodiment (for the specific method, please refer to the description of the above method embodiment, which will not be repeated here).

[0293] An embodiment of the present invention also provides a computer-readable storage medium, and the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steam chamber expansion monitoring method for heavy oil reservoirs by steam flooding based on satellite remote sensing technology in each method embodiment (for the specific method, please refer to the description of the above method embodiment, which will not be repeated here).

[0294] Preferred embodiments of the present invention have been described above with reference to the accompanying drawings. Many features and advantages of these embodiments are apparent from this detailed description, and thus the appended claims are intended to cover all such features and advantages of these embodiments that fall within their true spirit and scope. Furthermore, since numerous modifications and changes will readily occur to those skilled in the art, the embodiments of the present invention are not intended to be limited to the precise construction and operation illustrated and described, but are intended to cover all suitable modifications and equivalents that fall within the scope thereof.

[0295] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0296] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0297] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0298] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0299] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A method for monitoring steam chamber expansion in heavy oil reservoirs based on satellite remote sensing technology, including: The method includes: Real-time acquisition of satellite thermal infrared remote sensing images and observation well temperature curve data of the study area at night; Determine the night surface temperature data of the study area based on the thermal infrared remote sensing image; Based on the night surface temperature data of the study area, the surface temperature gradient grid data of the study area is determined; Using the surface temperature gradient grid data of the study area, the steam chamber states are classified into four categories: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone, so as to determine the horizontal distribution data of the steam chamber in steam drive. Based on the surface temperature gradient grid data of the study area and the well temperature curve data of the observation wells in the study area, the vertical depths of the steam zone, hot oil and water zone, and cold oil zone are determined, thereby determining the vertical distribution data of the steam chamber of the steam drive; Based on the horizontal distribution data of the steam chamber of steam drive and the vertical distribution data of the steam chamber of steam drive, the reservoir temperature is fitted and interpolated to determine the three-dimensional distribution data of the steam chamber of steam drive; Based on the horizontal distribution data of the steam chamber during steam drive at different periods, the vertical distribution data of the steam chamber during steam drive, and the three-dimensional distribution data of the steam chamber during steam drive, the spatiotemporal evolution of the steam chamber is predicted; The surface temperature gradient grid data of the study area is used to classify the steam chamber states into four types: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone, so as to determine the horizontal distribution data of the steam chamber in the steam drive, including: 1) The steam chamber production area is divided into four types of steam chamber regional states: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone; and based on the impact of different regional types on the change of surface temperature gradient, the surface temperature gradient classification thresholds of the four types of regional states are determined; 2) Based on the surface temperature gradient grid data and classification thresholds in the study area, the grid data of the four types of steam chamber regional states in the study area, namely, steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone, were obtained; 3) The raster data of the four types of steam chamber regional states in the study area are spatially vectorized to obtain the horizontal distribution data of the steam chamber of steam drive; The vertical depths of the steam zone, hot oil and water zone, and cold oil zone are determined based on the surface temperature gradient grid data of the study area and combined with the temperature curve data of the observation wells in the study area, so as to determine the vertical distribution data of the steam chamber of the steam drive by the following method: Based on the surface temperature gradient grid data of the study area, aiming at the heat conduction and heat convection in the steam drive production process, combined with reservoir engineering and energy conservation principles, combined with the well temperature curve data of the observation wells in the study area, a heat transfer model of the vertical temperature of the steam drive steam chamber is established and solved, and the vertical depths of the steam zone, hot oil and water zone, and cold oil zone are determined, thereby determining the vertical distribution data of the steam drive steam chamber; Among them, based on the horizontal distribution data of the steam chamber of steam drive and the vertical distribution data of the steam chamber of steam drive, the reservoir temperature fitting interpolation processing is performed to determine the three-dimensional distribution data of the steam chamber of steam drive, including: Predict the historical temperature values ​​of the time and space points in the steam drive steam chamber according to the horizontal distribution data of the steam drive steam chamber and the vertical distribution data of the steam drive steam chamber; According to the historical values ​​of the temperature of the time and space points in the steam chamber of the steam drive, the time and space empirical variation function is established; According to the space-time empirical variogram, a space-time theoretical variogram model is established; Through geostatistical methods and using the space-time theoretical variation function model, the horizontal distribution data of the steam chamber of steam drive and the vertical distribution data of the steam chamber of steam drive are subjected to reservoir temperature fitting interpolation processing to obtain the three-dimensional distribution data of the steam chamber of steam drive. The prediction of the spatiotemporal evolution of the steam chamber based on the horizontal distribution data of the steam chamber during steam drive, the vertical distribution data of the steam chamber during steam drive and the three-dimensional distribution data of the steam chamber during steam drive is performed in the following manner: Based on the horizontal distribution data of steam chambers in steam drive at different periods, the vertical distribution data of steam chambers in steam drive and the three-dimensional distribution data of steam chambers in steam drive, the spatiotemporal evolution speed of the steam chamber front is determined by fitting regression method, and then a spatiotemporal pan-Kriging prediction model is established to predict the change process of reservoir temperature and obtain the spatiotemporal evolution process of the steam chamber.

2. The monitoring method according to claim 1, wherein: Determining the surface temperature of the study area based on the thermal infrared remote sensing image includes: The obtained nighttime satellite thermal infrared remote sensing images of the study area are calibrated by radiation, atmospheric corrected, and geometrically corrected to obtain remote sensing surface reflectance data. The thermal infrared band of the remote sensing surface reflectance data is inverted and calculated, and the important parameters of brightness temperature, surface emissivity, atmospheric average temperature, and atmospheric water vapor content are estimated to obtain the night thermal infrared remote sensing inversion surface temperature data, that is, the night surface temperature data of the study area.

3. The monitoring method according to claim 1, wherein: The method further comprises: Based on the measured temperature data of the observation wells in the study area, the night surface temperature data of the study area determined based on the obtained night satellite thermal infrared remote sensing images of the study area are calibrated and corrected to obtain the calibrated and corrected night surface temperature data as the final night surface temperature data of the study area.

4. The monitoring method according to claim 1, wherein: Determining the surface temperature gradient grid data of the study area based on the night surface temperature data of the study area includes: Using the night surface temperature data of the study area, the surface temperature gradient data of the study area is calculated; The surface temperature gradient calculation model is used to calculate the temperature gradient value of each grid and generate a temperature gradient grid data map.

5. The monitoring method according to claim 4, wherein: The surface temperature gradient calculation model is: Where: is the partial derivative in the x direction; is the partial derivative in the y direction; ΔT is the surface temperature gradient.

6. The monitoring method according to claim 1, wherein: In the process of spatial vectorization of the raster data of the four types of steam chamber regional states in the study area, patch integration, boundary smoothing, topology reconstruction, and data clipping are performed to obtain the edge distribution data of the four types of steam chamber regional states in the horizontal direction of the steam drive steam chamber in the study area, thereby completing the spatial vectorization of the raster data of the edges of the four types of steam chamber regional states in the study area.

7. The monitoring method according to claim 1, wherein: The vertical depths of the steam zone, hot oil and water zone, and cold oil zone are determined based on the surface temperature gradient grid data of the study area and combined with the well temperature curve data of the observation wells in the study area, so as to determine the vertical distribution data of the steam chamber of the steam drive, including: 1) Determine the depth of the isothermal layer based on the observation well temperature curve data; 2) Determine the number of reservoir temperature systems based on the observed well temperature curve data; 3) Based on the depth of the isothermal layer, the number of reservoir temperature systems, and the observation well temperature curve data, determine the reservoir temperature values ​​at different depths in the study area; 4) Based on the heat conduction and convection in the steam drive production process, according to the surface temperature gradient grid data and combined with the temperature values ​​of reservoirs at different depths in the study area, a heat transfer model for the vertical temperature of the steam chamber of steam drive is established and solved to determine the vertical depths of the steam zone, hot oil and water zone, and cold oil zone, thereby determining the vertical distribution data of the steam chamber of steam drive.

8. The monitoring method according to claim 7, wherein: Determining the depth of the isothermal layer according to the observation well temperature curve data includes: A linear regression model of the isothermal layer temperature and the surface temperature is established according to the well temperature instrument logging curve; at the same time, the linear regression model of the isothermal layer temperature and the surface temperature is corrected using the observed well temperature measured data.

9. The monitoring method according to claim 7, wherein: According to the well logging curve of the well temperature instrument, the number of broken line segments of the reservoir temperature system curve is determined; in the reservoir temperature system curve, it is assumed that the thermal conductivity λ is uniformly layered, that is: D0≤ z ≤D1,λ=λ1;D1≤z≤D2,λ=λ2;......D n-1 ≤z≤D n ,λ=λ n ; Among them, D i (i=0,1,2…n) is the burial depth of the stratum, among which D0 is the burial depth of the isothermal layer.

10. The monitoring method according to claim 7, wherein: According to the well logging curve of the well temperature instrument, the burial depth and slope of each broken line segment in the reservoir temperature system curve are determined; then, combined with the night surface temperature data of the study area, the reservoir temperature system curve is determined.

11. The monitoring method according to claim 7, wherein: Combining reservoir engineering with the principle of energy conservation, the vertical depths of the steam zone, hot oil-water zone, and cold oil zone are determined based on Fourier's law and Newton's law of cooling.

12. The monitoring method according to claim 11, wherein: The vertical depths of the steam zone, hot oil water zone, and cold oil zone are determined according to the following formula: dQ=αdA(T-T W ) Where: dQ is the micro-element convective heat transfer rate, dA is the micro-element heat transfer area, α is the local convective heat transfer coefficient, T is the temperature of the high temperature zone, T W is the temperature of the low temperature zone, λ is the thermal conductivity, and z is the reservoir depth.

13. The monitoring method according to claim 1, wherein: The prediction of the historical values ​​of the time and space point temperature in the steam chamber of the steam drive is based on the following formula: Where: z0 is the temperature history value of the steam chamber in the steam drive at a certain time and space, z i (i=1,...,n) is the temperature value of the known time and space point in the i-th steam drive steam chamber, λ i is the weight coefficient.

14. The monitoring method according to claim 1, wherein: The establishment of spatiotemporal empirical variation function is based on the following formula: Where: h S ,h T are spatial interval variables and time interval variables respectively, N(h S ,h T ) is the number of point pairs that meet the defined spatial and temporal intervals.

15. The monitoring method according to claim 1, wherein: The establishment of the spatiotemporal theoretical variation function model is based on the following formula: c(h S ,h T )=γ S (h S )+c T (h T )+c ST (h ST ) In the formula, γ S (h S ), γ T (h T ), γ ST (h ST ) are spatial variogram, temporal variogram and spatiotemporal variogram respectively.

16. The monitoring method according to claim 15, wherein: The spatial variation function uses a Gaussian model, a hemispherical model, an exponential model or a linear model; The time variation function uses a Gaussian model, a hemispherical model, an exponential model or a linear model; The spatiotemporal variation function uses a Gaussian model, a hemispherical model, an exponential model or a linear model.

17. The monitoring method according to claim 1, wherein: The reservoir temperature fitting interpolation processing is performed according to the spatiotemporal Kriging interpolation algorithm.

18. The monitoring method according to claim 1, wherein: The temporal and spatial evolution speed of the steam chamber front is determined by the following formula: Z(x)={Z(s,t)|s∈S,t∈T} Where S represents the spatial domain of the steam chamber front edge, s∈R 2 ; T represents the time domain of the steam chamber front edge, T∈R; Z(x) can be decomposed into the following form: Z(s,t)=M(s,t)+R(s,t), where M(s,t) is the mathematical expectation of the temperature at the spacetime point (s,t) of the steam chamber front edge, and represents the drift at the spacetime point (s,t); R(s,t) is the remaining part of the temperature at the steam chamber front edge after removing the trend, and represents the random error of Z(s,t) under a preset scale of fluctuation around the trend M(s,t).

19. The monitoring method according to claim 18, wherein: The establishment of spatiotemporal pan-Kriging prediction model is based on the following formula: Where Z * (s, t) is the change value of temperature, which is used to represent the Kriging prediction value of the spatial-temporal point trend Z(s, t); is the expected value of the spatiotemporal trend M(s, t) of the temperature at the front edge of the steam chamber at the spatiotemporal point (s, t); is the spatiotemporal ordinary kriging prediction value of the residual R(s, t) at the spatiotemporal point (s, t).

20. A steam chamber expansion monitoring system for steam flooding of heavy oil reservoirs based on satellite remote sensing technology, wherein: The system includes: Data acquisition module: used to obtain satellite thermal infrared remote sensing images of the study area at night; Surface temperature determination module: used to determine the night surface temperature data of the study area based on the acquired satellite thermal infrared remote sensing images of the study area at night; Surface temperature gradient determination module: used to determine the surface temperature gradient grid data of the study area based on the night surface temperature data of the study area; Horizontal distribution determination module: It is used to use the surface temperature gradient grid data of the study area to classify the steam chamber states into four types: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone, so as to determine the horizontal distribution data of the steam chamber in steam drive; Vertical distribution determination module: used to determine the vertical depths of the steam zone, hot oil and water zone, and cold oil zone based on the surface temperature gradient grid data of the study area and the observation well temperature curve data of the study area, so as to determine the vertical distribution data of the steam chamber of the steam drive; Three-dimensional determination module: used to perform reservoir temperature fitting interpolation processing based on the horizontal distribution data of the steam chamber for steam drive and the vertical distribution data of the steam chamber for steam drive, and determine the three-dimensional distribution data of the steam chamber for steam drive; Steam chamber evolution process determination module: used to predict the spatiotemporal evolution process of the steam chamber based on the horizontal distribution data of the steam chamber, the vertical distribution data of the steam chamber and the three-dimensional distribution data of the steam chamber during different periods of steam drive; Wherein, the horizontal distribution determination module includes: Grading threshold determination submodule: used to classify the steam chamber production area into four types of steam chamber regional states: steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone; and based on the impact of different regional types on the change of surface temperature gradient, the surface temperature gradient classification thresholds of the four types of regional states are determined; Raster data classification submodule: used to obtain the raster data of the four types of steam chamber regional states in the study area, namely, steam zone, steam zone + hot oil and water zone, steam zone + hot oil and water zone + cold oil zone, and cold oil zone, based on the surface temperature gradient raster data and classification threshold of the study area; Horizontal distribution determination submodule: used to spatially vectorize the raster data of the four types of steam chamber regional states in the study area, so as to obtain the horizontal distribution data of the steam chamber of steam drive; The vertical depths of the steam zone, hot oil and water zone, and cold oil zone are determined based on the surface temperature gradient grid data of the study area and combined with the temperature curve data of the observation wells in the study area, so as to determine the vertical distribution data of the steam chamber of the steam drive by the following method: Based on the surface temperature gradient grid data of the study area, aiming at the heat conduction and heat convection in the steam drive production process, combined with reservoir engineering and energy conservation principles, combined with the well temperature curve data of the observation wells in the study area, a heat transfer model of the vertical temperature of the steam drive steam chamber is established and solved, and the vertical depths of the steam zone, hot oil and water zone, and cold oil zone are determined, thereby determining the vertical distribution data of the steam drive steam chamber; The three-dimensional determination module includes: The time-space point temperature history value determination submodule is used to predict the time-space point temperature history value in the steam drive steam chamber according to the horizontal distribution data of the steam drive steam chamber and the vertical distribution data of the steam drive steam chamber; The submodule for determining the temporal and spatial empirical variation function is used to establish the temporal and spatial empirical variation function according to the historical values ​​of the temporal and spatial point temperatures in the steam chamber of the steam drive; The submodule for determining the spatiotemporal theoretical variation function model is used to establish the spatiotemporal theoretical variation function model based on the spatiotemporal empirical variation function; Three-dimensional distribution determination submodule: It is used to perform reservoir temperature fitting and interpolation processing on the horizontal distribution data of the steam chamber of steam drive and the vertical distribution data of the steam chamber of steam drive by using geostatistical methods and the time-space theory variation function model, so as to obtain the three-dimensional distribution data of the steam chamber of steam drive; Among them, the steam chamber evolution process determination module includes: The submodule for determining the spatiotemporal evolution speed of the steam chamber front edge is used to determine the spatiotemporal evolution speed of the steam chamber front edge through a fitting regression method based on the horizontal distribution data of the steam chamber in different periods, the vertical distribution data of the steam chamber in different periods, and the three-dimensional distribution data of the steam chamber in different periods; The submodule for determining the spatiotemporal evolution process is used to establish a spatiotemporal pan-Kriging prediction model based on the spatiotemporal evolution speed of the steam chamber front, predict the change process of the reservoir temperature, and then obtain the spatiotemporal evolution process of the steam chamber.

21. The monitoring system according to claim 20, wherein: The surface temperature determination module includes: The surface reflectivity determination submodule is used to perform radiation calibration, atmospheric correction, and geometric correction on the obtained satellite thermal infrared remote sensing images of the study area at night to obtain remote sensing surface reflectivity data; The surface temperature determination submodule is used to invert and calculate the thermal infrared band of the remote sensing surface reflectance data, estimate the important parameters of brightness temperature, surface emissivity, atmospheric average temperature, and atmospheric water vapor content, and obtain the night thermal infrared remote sensing inversion surface temperature data, that is, the night surface temperature data of the study area.

22. The monitoring system of claim 20, wherein: The surface temperature determination module further includes: Surface temperature calibration submodule: It is used to calibrate the night surface temperature data of the study area determined based on the acquired night satellite thermal infrared remote sensing images of the study area based on the measured temperature data of the observation wells in the study area, and the calibrated and corrected night surface temperature data is used as the final night surface temperature data of the study area.

23. The monitoring system of claim 20, wherein: The surface temperature gradient determination module includes: The first processing submodule is used to calculate the surface temperature gradient data of the study area using the surface temperature data of the study area at night; The second processing submodule is used to calculate the temperature gradient value of each grid by using the surface temperature gradient calculation model, and generate a temperature gradient grid data map.

24. The monitoring system of claim 20, wherein: The vertical distribution determination module includes: The isothermal layer depth determination submodule is used to determine the isothermal layer depth based on the observation well temperature curve data; Temperature system curve qualitative analysis submodule: used to determine the number of reservoir temperature systems based on the observed well temperature curve data; Curve data determination submodule: used to determine the reservoir temperature values ​​at different depths in the study area based on the depth of the isothermal layer, the number of reservoir temperature systems, and the observation well temperature curve data; Surface temperature heat transfer determination submodule: It is used to establish and solve the heat transfer model of the vertical temperature of the steam chamber of steam drive based on the heat conduction and heat convection in the steam drive production process, according to the surface temperature gradient grid data, combined with the temperature values ​​of reservoirs at different depths in the study area, to determine the vertical depths of the steam zone, hot oil and water zone, and cold oil zone, thereby determining the vertical distribution data of the steam chamber of steam drive.

25. The monitoring system of claim 20, wherein: The time-space point temperature history value determination submodule predicts the time-space point temperature history value in the steam chamber of the steam drive based on the following formula: Where: z0 is the historical value of the temperature at a certain point in time and space in the steam chamber of the steam drive, z i (i=1,...,n) is the temperature value of the known time and space point in the i-th steam drive steam chamber, λ i is the weight coefficient.

26. The monitoring system of claim 20, wherein: The spatiotemporal empirical variogram determination submodule establishes the spatiotemporal empirical variogram based on the following formula: Where: h S ,h T are spatial interval variables and time interval variables respectively, N(h S ,h T ) is the number of point pairs that fit within the defined spatial and temporal intervals.

27. The monitoring system of claim 20, wherein: The space-time theoretical variation function model determination submodule establishes the space-time theoretical variation function model based on the following formula: c(h S ,h T )=γ S (h S )+c T (h T )+c ST (h ST ) In the formula, γ S (h S ), γ T (h T ), γ ST (h ST ) are spatial variogram, temporal variogram and spatiotemporal variogram respectively.

28. The monitoring system of claim 20, wherein: The steam chamber front spatiotemporal evolution speed determination submodule determines the steam chamber front spatiotemporal evolution speed based on the following formula: Z(x)={Z(s,t)|s∈S,t∈T} Where S represents the spatial domain of the steam chamber front edge, s∈R 2 ; T represents the time domain of the steam chamber front edge, T∈R; Z(x) can be decomposed into the following form: Z(s,t)=M(s,t)+R(s,t), where M(s,t) is the mathematical expectation of the temperature at the spacetime point (s,t) of the steam chamber front edge, and represents the drift at the spacetime point (s,t); R(s,t) is the remaining part of the temperature at the steam chamber front edge after removing the trend, and represents the random error of Z(s,t) under a preset scale of fluctuation around the trend M(s,t).

29. The monitoring system of claim 20, wherein: The spatiotemporal evolution process determination submodule establishes a spatiotemporal pan-kriging prediction model based on the following formula: Where z*(s, t) is the change in temperature, which is used to represent the Kriging prediction value of the spatial-temporal trend of Z(s, t); is the expected value of the spatiotemporal trend M(s, t) of the temperature at the front edge of the steam chamber at the spatiotemporal point (s, t); is the spatiotemporal ordinary kriging prediction value of the residual R(s, t) at the spatiotemporal point (s, t).

30. A steam chamber expansion monitoring device for steam flooding of heavy oil reservoirs based on satellite remote sensing technology, comprising a processor and a memory; wherein: Memory, used to store computer programs; The processor is used to implement the steps of the method for monitoring steam chamber expansion in steam flooding of heavy oil reservoirs based on satellite remote sensing technology as described in any one of claims 1 to 19 when executing the program stored in the memory.

31. A computer-readable storage medium storing one or more programs, wherein the one or more programs can be executed by one or more processors to implement the steps of the method for monitoring steam chamber expansion in steam flooding of heavy oil reservoirs based on satellite remote sensing technology as described in any one of claims 1 to 19.

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