Method and system for monitoring combustion front of fire flooding in heavy oil reservoirs based on satellite remote sensing technology

Abstract

The present invention provides a method and system for monitoring the fire drive combustion front in heavy oil reservoirs based on satellite remote sensing technology. The method comprises: determining the nighttime surface temperature data of a study area based on thermal infrared remote sensing images, and then determining the surface temperature gradient raster data of the study area; using the surface temperature gradient raster data to determine the planar distribution of the burned area, fire wall, coking zone, oil wall, and residual oil area, and thus determining the horizontal distribution data of the fire drive combustion front; determining the vertical depth of the burned area, fire wall, coking zone, oil wall, and residual oil area based on the surface temperature gradient raster data combined with observation well temperature curve data, and thus determining the vertical distribution data of the fire drive combustion front; performing reservoir temperature fitting interpolation based on the horizontal and vertical distribution data of the fire drive combustion front to obtain three-dimensional distribution data of the fire drive combustion front; and predicting the evolution process of the fire drive combustion front based on the horizontal, vertical, and three-dimensional distribution data of the fire drive combustion front at different times.

Description

Technical Field

[0001] The present invention belongs to the technical field of oilfield and reservoir engineering, and in particular relates to a method and system for monitoring the combustion front of a fire drive in a heavy oil reservoir based on satellite remote sensing technology. Background Art

[0002] Fire flooding, an emerging development technology for heavy oil reservoirs, has been widely applied in China, Romania, and India. The mechanisms of fire flooding are complex and challenging to understand. Research into the full oxidation process is crucial for improving fire flooding effectiveness. Fire front prediction and control technologies have been developed. However, precise monitoring and control of the fire front is a key focus for future research.

[0003] Satellite remote sensing monitoring technology can be used to accurately monitor the expansion of the combustion front of fire flooding in heavy oil reservoirs in real time, calculate the expansion speed of the combustion front in real time, and provide technical support for operations in different production stages of fire flooding.

[0004] It can also provide in-depth understanding of the details of underground reservoir temperature and crude oil saturation distribution, and have an overall grasp of the entire reservoir, which helps to discover new drilling targets and implement new production-increasing measures, and accurately calculate the recovery rate of the oil field. Summary of the Invention

[0005] The present invention aims to provide a method for monitoring the fire front in heavy oil reservoirs, which can accurately monitor the expansion of the fire front in heavy oil reservoirs in real time and calculate the expansion rate of the fire front 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. It helps identify new drilling targets, implement new production enhancement measures, and accurately calculate the oil field recovery factor, providing technical support for fire flooding operations at different production stages.

[0006] To achieve the above object, the present invention provides a method for monitoring the combustion front of a fire flooding in a heavy oil reservoir based on satellite remote sensing technology, wherein the method comprises:

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

[0008] Surface temperature determination step: determining the nighttime surface temperature data of the study area based on the thermal infrared remote sensing image;

[0009] Surface temperature gradient determination steps: Based on the night surface temperature data of the study area, determine the surface temperature gradient raster data of the study area;

[0010] Steps for determining horizontal distribution: Using the surface temperature gradient grid data of the study area, classify them into five states: burned area, fire wall, coking zone, oil wall, and residual oil area, and determine the plane distribution of burned area, fire wall, coking zone, oil wall, and residual oil area, thereby determining the horizontal distribution data of the fire flooding combustion front;

[0011] Steps for determining vertical distribution: 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 burned area, fire wall, coking zone, oil wall, and residual oil area are determined, thereby determining the vertical distribution data of the fire flooding combustion front;

[0012] Three-dimensional distribution determination step: performing reservoir temperature fitting interpolation based on horizontal distribution data and vertical distribution data of the fire drive combustion front to obtain three-dimensional distribution data of the fire drive combustion front;

[0013] The steps for determining the evolution process of the fire drive combustion front are as follows: based on the horizontal distribution data of the fire drive combustion front at different periods, the vertical distribution data of the fire drive combustion front and the three-dimensional distribution data of the fire drive combustion front, the evolution process of the fire drive combustion front is predicted.

[0014] In the above-mentioned method for monitoring the combustion front of a fire flooding in a heavy oil reservoir based on satellite remote sensing technology, preferably, determining the nighttime surface temperature data of the study area based on the thermal infrared remote sensing image comprises:

[0015] The obtained thermal infrared remote sensing images of the study area at night were processed with radiation 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 nighttime thermal infrared remote sensing surface temperature data.

[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 the combustion front of fire flooding in heavy oil reservoirs based on satellite remote sensing technology, preferably, the method further comprises the following steps of determining the surface temperature:

[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 the combustion front of a fire flooding in a heavy oil reservoir based on satellite remote sensing technology, preferably, the surface temperature gradient determining step is based on the nighttime surface temperature data of the study area, and the surface temperature gradient raster data of the study area is determined by:

[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 temperature gradient.

[0029] In the above-mentioned method for monitoring the fire flooding combustion front in heavy oil reservoirs based on satellite remote sensing technology, preferably, the step of determining the horizontal distribution data utilizes the surface temperature gradient grid data of the study area to classify the state into five categories: burned area, fire wall, coking zone, oil wall, and residual oil area, and determines the plane distribution of the burned area, fire wall, coking zone, oil wall, and residual oil area, thereby determining the horizontal distribution data of the fire flooding combustion front by the following method:

[0030] The surface temperature gradient grid data of the study area was used to classify the state into five categories: burned area, fire wall, coking zone, oil wall, and residual oil area. The classified data were vectorized and post-processed to determine the horizontal distribution data of the fire drive combustion front.

[0031] More preferably, the surface temperature gradient grid data of the study area is used to classify the burnt area, fire wall, coking zone, oil wall, and residual oil area into five categories, and the plane distribution of the burnt area, fire wall, coking zone, oil wall, and residual oil area is determined, thereby determining the horizontal distribution data of the fire flooding combustion front, including:

[0032] 1) Fire flooding production areas are classified into five types: burned area, fire wall, coking zone, oil wall, and residual oil area. Based on the impact of different regional states on the change of surface temperature gradient, the surface temperature gradient classification thresholds for the five types of regional states are determined;

[0033] 2) Based on the surface temperature gradient grid data and classification thresholds in the study area, raster data of five regional states in the study area, namely, burned area, fire wall, coking zone, oil wall, and remaining oil area, were obtained;

[0034] 3) The raster data of the five types of regional states in the study area were spatially vectorized to determine the planar distribution of the burned area, fire wall, coking zone, oil wall, and residual oil area, thereby obtaining the horizontal distribution data of the fire drive combustion front.

[0035] Further preferably, in the process of spatial vectorization of the raster data of the five types of 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 five types of regional states in the horizontal direction of fire drive in the study area, thereby completing the spatial vectorization of the raster data of the five types of regional states in the study area.

[0036] In the above-mentioned method for monitoring the fire flooding combustion front in heavy oil reservoirs based on satellite remote sensing technology, preferably, the vertical depths of the burned area, fire wall, coking zone, oil wall, and residual oil area are determined based on the surface temperature gradient grid data of the study area in combination with the temperature curve data of the observation wells in the study area, thereby determining the vertical distribution data of the fire flooding combustion front in the following manner:

[0037] Based on the surface temperature gradient grid data of the study area, focusing on the effects of heat conduction and convection during fire flooding production, combined with reservoir engineering and energy conservation principles, and combined with the temperature curve data of observation wells in the study area, a heat transfer model for the vertical temperature of the fire flooding combustion front was established and solved. The vertical depths of the burned zone, fire wall, coking zone, oil wall, and residual oil zone were determined, thereby determining the vertical distribution of the fire flooding combustion front.

[0038] Further 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 burned area, the fire wall, the coking zone, the oil wall, and the remaining oil area are determined, thereby determining the vertical distribution data of the fire flooding combustion front, 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 the fire flooding production process, 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 fire flooding combustion front was established and solved. The vertical depths of the burned zone, fire wall, coking zone, oil wall, and residual oil zone were determined, thereby determining the vertical distribution of the fire flooding combustion front.

[0043] Further preferably, determining the depth of the isothermal layer according to the observation well temperature curve data includes:

[0044] 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;

[0045] 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:

[0046] D0≤z≤D1, λ=λ1; D1≤z≤D2, λ=λ2;…D n-1 ≤z≤D n ,λ=λ n ;

[0047] 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;

[0048] 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;

[0049] In one embodiment, the isothermal layer depth is determined based on well temperature instrument data; the well temperature instrument data is clearly displayed, has a large amount, and is easy to collect, and is very suitable for determining the isothermal layer depth;

[0050] In the above-mentioned method for monitoring the expansion of the combustion front in a fire flooding of a heavy oil reservoir based on satellite remote sensing technology, preferably, the vertical depths of the burned area, the fire wall, the coking zone, the oil wall, and the residual oil zone are determined according to Fourier's law and Newton's law of cooling in combination with reservoir engineering and the principle of conservation of energy;

[0051] More preferably, the vertical depths of the burned zone, fire wall, coking zone, oil wall, and residual oil zone are determined according to the following formula:

[0052] dQ=αdA(TT W )

[0053]

[0054] 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.

[0055] In the above-mentioned method for monitoring the fire flooding combustion front in a heavy oil reservoir based on satellite remote sensing technology, preferably, performing reservoir temperature fitting interpolation based on the horizontal distribution data and the vertical distribution data of the fire flooding combustion front to obtain three-dimensional distribution data of the fire flooding combustion front includes:

[0056] According to the horizontal distribution data and vertical distribution data of the fire drive combustion front, the temperature history values ​​of the time and space points in the fire drive combustion front are predicted;

[0057] Based on the temperature history values ​​of the time and space points in the fire drive combustion front, a time and space empirical variation function is established;

[0058] According to the spatiotemporal empirical variogram, a spatiotemporal theoretical variogram model is established;

[0059] Through geostatistical methods and the use of the space-time theoretical variation function model, the horizontal and vertical distribution data of the fire drive combustion front are subjected to reservoir temperature fitting and interpolation processing to obtain the three-dimensional distribution data of the fire drive combustion front.

[0060] More preferably, the prediction of the historical values ​​of the space-time point temperature in the fire drive combustion front is performed based on the following formula:

[0061] and

[0062] Where: z0 is the temperature history value of the space-time point in the fire drive combustion front, z i (i=1, ..., n) is the temperature value of the known time and space point in the i-th fire drive combustion front, λ i is the weight coefficient;

[0063] More preferably, establishing the spatiotemporal empirical variation function is performed based on the following formula:

[0064]

[0065] 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 intervals;

[0066] More preferably, the establishment of the spatiotemporal theoretical variogram model is based on the following formula:

[0067] γ(h S , h T )=γ S (h S )+γ T (h T )+γ ST (h ST )

[0068] Where, γ S (h S ),γ T (h T ),γ ST (h ST ) are spatial variogram, temporal variogram and spatiotemporal variogram respectively;

[0069] 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.;

[0070] 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.;

[0071] 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.

[0072] More preferably, the reservoir temperature fitting interpolation processing is performed according to a spatiotemporal Kriging interpolation algorithm.

[0073] In the above-mentioned method for monitoring the fire flooding combustion front in a heavy oil reservoir based on satellite remote sensing technology, preferably, based on the horizontal distribution data of the fire flooding combustion front, the vertical distribution data of the fire flooding combustion front, and the three-dimensional distribution data of the fire flooding combustion front at different periods, the evolution process of the fire flooding combustion front is predicted by the following method:

[0074] Based on the horizontal distribution data of the fire flooding combustion front at different periods, the vertical distribution data of the fire flooding combustion front, and the three-dimensional distribution data of the fire flooding combustion front, the spatiotemporal evolution speed of the fire flooding combustion front is determined by fitting regression method, and then a spatiotemporal pan-kriging prediction model is established to predict the change process of the reservoir temperature, and thus the spatiotemporal evolution process of the fire flooding is obtained.

[0075] More preferably, the spatiotemporal evolution speed of the fire drive combustion front is determined according to the following formula:

[0076] Z(x)={Z(s,t)|s∈S,t∈T}

[0077] Where S represents the spatial domain of the fire drive combustion front, S∈R 2; T represents the time domain of the fire-driven combustion 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 time and space point (s, t) of the fire-driven combustion front, and represents the drift at the time and space point (s, t); R(s, t) is the remaining part of the fire-driven combustion front temperature 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);

[0078] More preferably, the spatiotemporal pan-Kriging prediction model is established based on the following formula:

[0079]

[0080] 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 fire drive combustion front 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).

[0081] In the above-mentioned method for monitoring the fire flooding combustion front in heavy oil reservoirs based on satellite remote sensing technology, in the process of monitoring the horizontal distribution data, vertical distribution data, and three-dimensional distribution data of the fire flooding combustion front at different periods in the data acquisition step and the three-dimensional distribution data determination step, iterative fitting and model optimization are continuously performed through surface temperature data of the study area determined based on thermal infrared remote sensing images, fire flooding temperature system simulation and measurement data of observation well temperatures, so as to realize monitoring of the horizontal distribution data, vertical distribution data, and three-dimensional distribution data of the fire flooding combustion front at different periods.

[0082] The present invention also provides a heavy oil reservoir fire flooding combustion front 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 state into five categories: burned area, fire wall, coking zone, oil wall, and residual oil area, and determine the plane distribution of burned area, fire wall, coking zone, oil wall, and residual oil area, so as to determine the horizontal distribution data of the fire drive combustion front;

[0087] Vertical distribution determination module: This module is used to determine the vertical depths of the burned area, fire wall, coking zone, oil wall, and residual 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 fire flooding combustion front;

[0088] Three-dimensional determination module: used to perform reservoir temperature fitting interpolation based on the horizontal distribution data and vertical distribution data of the fire drive combustion front to obtain the three-dimensional distribution data of the fire drive combustion front;

[0089] Fire drive combustion front evolution process determination module: used to predict the evolution process of the fire drive combustion front based on the horizontal distribution data of the fire drive combustion front, the vertical distribution data of the fire drive combustion front and the three-dimensional distribution data of the fire drive combustion front at different periods.

[0090] In the above-mentioned heavy oil reservoir fire flooding combustion front monitoring system based on satellite remote sensing technology, the surface temperature determination module preferably 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 fire flooding combustion front 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 fire flooding combustion front 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 fire flooding combustion front 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 fire flooding production areas into five types: burned area, fire wall, coking zone, oil wall, and residual oil area. Based on the impact of different regional states on the change of surface temperature gradient, the surface temperature gradient classification thresholds of the five regional states are determined;

[0100] Raster data classification submodule: used to obtain raster data of the five regional states of the study area, namely, burned area, fire wall, coking zone, oil wall, and remaining oil area, 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 five types of regional states in the study area, determine the plane distribution of the burned area, fire wall, coking zone, oil wall, and residual oil area, and thus obtain the horizontal distribution data of the fire drive combustion front.

[0102] In the above-mentioned heavy oil reservoir fire flooding combustion front 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: Based on the heat conduction and convection effects in the fire flooding production process, according to the surface temperature gradient grid data and the temperature values ​​of reservoirs at different depths in the study area, it establishes and solves the heat transfer model of the vertical temperature of the fire flooding combustion front, determines the vertical depths of the burned area, fire wall, coking zone, oil wall, and residual oil area, and thus determines the vertical distribution of the fire flooding combustion front.

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

[0108] The submodule for determining the historical temperature value of a time-space point is used to predict the historical temperature value of a time-space point in the fire drive combustion front according to the horizontal distribution data and the vertical distribution data of the fire drive combustion front;

[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 fire drive combustion front;

[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] The three-dimensional distribution determination submodule is used to perform reservoir temperature fitting and interpolation processing on the horizontal distribution data and the vertical distribution data of the fire drive combustion front through geostatistical methods and the space-time theoretical variation function model to obtain the three-dimensional distribution data of the fire drive combustion front;

[0112] More preferably, the spatiotemporal point temperature history value determination submodule predicts the spatiotemporal point temperature history value in the fire drive combustion front based on the following formula:

[0113] and

[0114] Where: z0 is the temperature history value of the space-time point in the fire drive combustion front, z i (i=1, ..., n) is the temperature value of the known time and space point in the i-th fire drive combustion front, λ i 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 intervals;

[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 (hS )+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 fire flooding combustion front monitoring system based on satellite remote sensing technology, preferably, the fire flooding combustion front evolution process determination module includes:

[0122] The fire drive combustion front spatiotemporal evolution speed determination submodule is used to determine the spatiotemporal evolution speed of the fire drive combustion front through a fitting regression method based on the horizontal distribution data of the fire drive combustion front at different periods, the vertical distribution data of the fire drive combustion front, and the three-dimensional distribution data of the fire drive combustion front;

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

[0124] More preferably, the fire drive combustion front spatiotemporal evolution speed determination submodule determines the fire drive combustion 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 fire drive combustion front, S∈R 2 ; T represents the time domain of the fire-driven combustion 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 time and space point (s, t) of the fire-driven combustion front, and represents the drift at the time and space point (s, t); R(s, t) is the remaining part of the fire-driven combustion front temperature 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 fire drive combustion front 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 heavy oil reservoir fire flooding combustion front monitoring device 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 the combustion front of fire flooding in 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 storing one or more programs, which can be executed by one or more processors to implement the above-mentioned method for monitoring the combustion front of fire flooding in heavy oil reservoirs based on satellite remote sensing technology.

[0134] The satellite remote sensing-based method for monitoring the fire front in heavy oil reservoirs provides a method for using satellite remote sensing technology to achieve simple, automated, and real-time monitoring of the expansion of the fire front in heavy oil reservoirs. This method, combined with observation well data, enables timely acquisition of highly accurate three-dimensional spatial features of the fire front. In the preferred technical solution, verification and calibration are also provided by combining ground monitoring and observation well data. The technical solution provided by the method of the present invention has 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 fire-driven combustion front in heavy oil reservoirs, and can calculate the expansion speed of the fire-driven combustion front in real time, providing technical support for the operation of fire-driven combustion in different production stages; it is helpful to discover new drilling targets and implement new production-increasing 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 holistic grasp of the entire oil reservoir; it is helpful to comprehensively consider factors such as reservoir heterogeneity, process technology level, and production status, and formulate control strategies and methods to improve the development of the combustion front, which is suitable for on-site application in oil fields and lays the foundation for the large-scale application of fire-driven combustion in the future.

[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 the combustion front of a heavy oil reservoir fire flooding based on satellite remote sensing technology according to an embodiment of the present invention.

[0138] Figure 2 This is a structural diagram of a heavy oil reservoir fire flooding combustion front 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 the combustion front of a heavy oil reservoir fire flooding based on satellite remote sensing technology is provided in one embodiment of the present invention.

[0140] Figure 4 This is a schematic diagram of the characteristics of the fire drive burned area, fire wall, coking zone, oil wall, and residual oil area.

[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 the combustion front of fire flooding in 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, classify the areas into five categories: burned area, fire wall, coking zone, oil wall, and residual oil area, and determine the plane distribution of burned area, fire wall, coking zone, oil wall, and residual oil area (characteristics of burned area, fire wall, coking zone, oil wall, and residual oil area are as follows: Figure 4 As shown), the horizontal distribution data of the fire drive combustion front is 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 burned area, the fire wall, the coking zone, the oil wall, and the remaining oil area are determined, thereby determining the vertical distribution data of the fire flooding combustion front;

[0151] Step S6: Based on the horizontal distribution data of the fire drive combustion front and the vertical distribution data of the fire drive combustion front, the reservoir temperature is fitted and interpolated to obtain the three-dimensional distribution data of the fire drive combustion front (such as Figure 6 shown);

[0152] Step S7: predicting the evolution of the fire drive combustion front based on the horizontal distribution data of the fire drive combustion front, the vertical distribution data of the fire drive combustion front, and the three-dimensional distribution data of the fire drive combustion front at different periods.

[0153] In one embodiment, step S2 includes:

[0154] The nighttime surface temperature data of the study area determined based on the thermal infrared remote sensing image include:

[0155] The obtained thermal infrared remote sensing images of the study area at night were processed with radiation calibration, atmospheric correction, geometric correction, etc. to obtain remote sensing surface reflectance data;

[0156] 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 nighttime thermal infrared remote sensing surface temperature data.

[0157] Furthermore, the inversion calculation is performed using a single-window algorithm, a single-channel algorithm, or a radiation transfer equation method.

[0158] In one embodiment, step S2 further includes:

[0159] 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.

[0160] In one embodiment, step S3 is implemented as follows:

[0161] 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.

[0162] Further, step S3 includes:

[0163] The surface temperature gradient data of the study area was calculated using the night surface temperature data of the study area.

[0164] Using the surface temperature gradient calculation model, calculate the temperature gradient value of each grid and generate the temperature gradient grid data map;

[0165] Among them, the surface temperature gradient calculation model is preferably:

[0166]

[0167] Where:

[0168] is the partial derivative in the x direction; is the partial derivative in the y direction; ΔT is the temperature gradient.

[0169] In one embodiment, step S4 is implemented as follows:

[0170] The surface temperature gradient grid data of the study area was used to classify the state into five categories: burned area, fire wall, coking zone, oil wall, and residual oil area. The classified data were vectorized and post-processed to determine the horizontal distribution data of the fire drive combustion front.

[0171] Further, step S4 includes:

[0172] 1) Fire flooding production areas are classified into five types: burned area, fire wall, coking zone, oil wall, and residual oil area. Based on the impact of different regional states on the change of surface temperature gradient, the surface temperature gradient classification thresholds for the five types of regional states are determined;

[0173] 2) Based on the surface temperature gradient grid data and classification thresholds in the study area, raster data of five regional states in the study area, namely, burned area, fire wall, coking zone, oil wall, and remaining oil area, were obtained;

[0174] 3) The raster data of the five types of regional states in the study area were spatially vectorized to determine the plane distribution of the burned area, fire wall, coking zone, oil wall, and residual oil area, thereby obtaining the horizontal distribution data of the fire flooding combustion front;

[0175] Furthermore, in the process of spatial vectorization of the raster data of the five types of regional states in the study area, patch integration, boundary smoothing, topology reconstruction, and data clipping were performed to obtain the edge distribution data of the five types of regional states in the horizontal direction of fire drive in the study area, thereby completing the spatial vectorization of the raster data of the five types of regional states in the study area.

[0176] In one embodiment, step S5 is implemented as follows:

[0177] Based on the surface temperature gradient grid data of the study area, focusing on the effects of heat conduction and convection during fire flooding production, combined with reservoir engineering and energy conservation principles, and combined with the temperature curve data of observation wells in the study area, a heat transfer model for the vertical temperature of the fire flooding combustion front was established and solved. The vertical depths of the burned zone, fire wall, coking zone, oil wall, and residual oil zone were determined, thereby determining the vertical distribution of the fire flooding combustion front.

[0178] Further, step S5 includes:

[0179] 1) Determine the depth of the constant temperature layer based on the observation well temperature curve data;

[0180] 2) Determine the number of reservoir temperature systems based on the observed well temperature curve data;

[0181] 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;

[0182] 4) Based on the heat conduction and convection effects during the fire flooding production process, 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 fire flooding combustion front was established and solved. The vertical depths of the burned zone, fire wall, coking zone, oil wall, and residual oil zone were determined, thereby determining the vertical distribution of the fire flooding combustion front.

[0183] The vertical heat transfer process at the combustion front includes convection of rising steam condensation heat and conduction in the formation. As the combustion front evolves, the extent of the burned zone, fire wall, coking zone, oil wall, and residual oil zone constantly changes, and convection heat transfer occurs in adjacent areas. Combining reservoir engineering with the principle of energy conservation, Fourier's law, and Newton's law of cooling, the depths of the burned zone, fire wall, coking zone, oil wall, and residual oil zone are determined.

[0184] Further optimization, based on the observation well temperature curve data, the depth of the isothermal layer is determined including:

[0185] 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;

[0186] 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:

[0187] D0≤z≤D1, λ=λ1; D1≤z≤D2, λ=λ2;…D n-1 ≤z≤D n ,λ=λ n ;

[0188] 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;

[0189] 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);

[0190] 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.

[0191] 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 burned zone, fire wall, coking zone, oil wall, and remaining oil zone are determined;

[0192] Furthermore, the vertical depths of the burned area, fire wall, coking zone, oil wall, and residual oil area are determined according to the following formula:

[0193] dQ=αdA(TT W )

[0194]

[0195] 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.

[0196] In one embodiment, step S6 includes:

[0197] According to the horizontal distribution data and vertical distribution data of the fire drive combustion front, the temperature history values ​​of the time and space points in the fire drive combustion front are predicted;

[0198] Based on the temperature history values ​​of the time and space points in the fire drive combustion front, a time and space empirical variation function is established;

[0199] According to the empirical function of space-time theory, a space-time theory variation function model is established;

[0200] Through geostatistical methods and the use of a spatiotemporal theoretical variation function model, the horizontal and vertical distribution data of the fire drive combustion front are subjected to reservoir temperature fitting and interpolation processing to obtain the three-dimensional distribution data of the fire drive combustion front.

[0201] Furthermore, the prediction of the historical temperature values ​​of the time and space points in the fire drive combustion front is based on the following formula:

[0202] and

[0203] Where: z0 is the temperature history value of the space-time point in the fire drive combustion front, z i (i=1, ..., n) is the temperature value of the known time and space point in the i-th fire drive combustion front, λ i is the weight coefficient;

[0204] Furthermore, the spatiotemporal empirical variation function is established based on the following formula:

[0205]

[0206] 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 intervals;

[0207] Furthermore, the spatiotemporal theoretical variation function model is established based on the following formula:

[0208] γ(h S , h T )=γ S (h S )+γ T (h T )+γ ST (h ST )

[0209] Where, γ S (h S ),γ T (h T ),γ ST (h ST ) are spatial variogram, temporal variogram and spatiotemporal variogram respectively;

[0210] Furthermore, the spatial variation function uses a spatial theoretical model such as a Gaussian model, a hemispherical model, an exponential model, a linear model, etc.;

[0211] Furthermore, the time variation function uses a spatial theoretical model such as a Gaussian model, a hemispherical model, an exponential model, a linear model, etc.;

[0212] Furthermore, the spatiotemporal variation function uses a spatial theoretical model such as a Gaussian model, a hemispherical model, an exponential model, a linear model, etc.;

[0213] Furthermore, the reservoir temperature fitting interpolation processing is performed according to the spatiotemporal Kriging interpolation algorithm.

[0214] Among them, based on the horizontal distribution data of the fire drive combustion front at different periods, the vertical distribution data of the fire drive combustion front and the three-dimensional distribution data of the fire drive combustion front, the surface temperature data of the study area determined based on thermal infrared remote sensing images, the simulation and calculation of the fire drive temperature system, the actual temperature data of the observation wells, etc., iterative fitting and model optimization are continuously performed to realize the monitoring of the horizontal distribution data of the fire drive combustion front at different periods, the vertical distribution data of the fire drive combustion front and the three-dimensional distribution data of the fire drive combustion front.

[0215] In one embodiment, the steps for establishing a spatiotemporal theoretical variogram model may be as follows:

[0216] 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.

[0217] 2. Using the horizontal distribution model of fire drive combustion and the vertical distribution model of fire drive combustion, predict the temperature history value of the unmeasured time and space point (s0, t0) in the current fire drive combustion, that is:

[0218] and

[0219] Where: z0 is the temperature history value of the unmeasured time and space point (s0, t0) in the current fire drive combustion, z i (i=1, ..., n) is the temperature value of the measured time and space point (s0, t0) in the current fire drive combustion, λ i is the weight coefficient.

[0220] Under the condition of the intrinsic hypothesis, the spatiotemporal empirical variation function is defined to calculate the spatiotemporal semivariance value.

[0221]

[0222] Where: h S ,h T are spatial and time interval variables, respectively, N(h S , h T ) is the number of point pairs in the sample that conform to the defined spatial and temporal intervals.

[0223] 3. Based on the Bilonick space-time separation model, define the space-time theoretical variation function model:

[0224] γ(h S , h T )=γ S (h S )+γ T (h T )+γ ST (h ST )

[0225] 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.

[0226] 4. Based on the acquired theoretical spatiotemporal variation model of the three-dimensional distribution of fire drive combustion, using the observation well temperature data, the horizontal distribution of fire drive combustion, and the vertical distribution of fire drive combustion, the three-dimensional spatiotemporal distribution of each spatiotemporal point of fire drive combustion is obtained by interpolation according to the spatiotemporal kriging interpolation algorithm.

[0227] In one embodiment, step S7 is performed as follows:

[0228] Based on the horizontal distribution data of the fire flooding combustion front at different periods, the vertical distribution data of the fire flooding combustion front, and the three-dimensional distribution data of the fire flooding combustion front, the spatiotemporal evolution speed of the fire flooding combustion front is determined by fitting regression method, and then a spatiotemporal pan-kriging prediction model is established to predict the change process of the reservoir temperature, and thus the spatiotemporal evolution process of the fire flooding is obtained.

[0229] Furthermore, the spatiotemporal evolution speed of the fire drive combustion front is determined according to the following formula:

[0230] Z(x)={Z(s,t)|s∈S,t∈T}

[0231] Where S represents the spatial domain of the fire drive combustion front, S∈R 2 ; T represents the time domain of the fire-driven combustion 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 time and space point (s, t) of the fire-driven combustion front, and represents the drift at the time and space point (s, t); R(s, t) is the remaining part of the fire-driven combustion front temperature 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);

[0232] Furthermore, the spatiotemporal pan-Kriging prediction model is established based on the following formula:

[0233]

[0234] 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 fire drive combustion front 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).

[0235] In one embodiment, iterative fitting is continuously performed on the three-dimensional distribution data of the fire drive combustion front to obtain a fire drive combustion front evolution model. Based on the monitoring results of the horizontal distribution, vertical distribution, and three-dimensional distribution of the fire drive combustion front at different periods, the spatiotemporal temperature of the fire drive combustion front is obtained by fitting regression methods. 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 fire drive combustion front. The steps may include:

[0236] 1) Calculate the fire drive combustion front temperature: Z(x) = {Z(s, t) | s∈S, t∈T}, where S represents the spatial domain of the fire drive combustion front temperature, S∈R 2 ; T represents the time domain of the fire drive combustion front temperature, T∈R; Z(x) can be decomposed into the following form: Z(s, t)=M(s, t)+R(s, t)

[0237] Where M(s, t) is the mathematical expectation of the temperature at the spatiotemporal point M(s, t) on the fire drive combustion front, representing the drift at point (s, t), that is, the trend part; R(s, t) is the remaining part of the fire drive combustion front temperature 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.

[0238] 2) Based on the fire drive combustion front temperature, the evolution process of the fire drive combustion front is predicted according to the following formula:

[0239] 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):

[0240]

[0241] Where, is the expected value of the spatiotemporal trend M(s, t) of the temperature during the evolution of the fire drive combustion front 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).

[0242] Among them, after continuously iteratively fitting the three-dimensional distribution data of the fire-driven combustion front to obtain the fire-driven combustion front evolution model, the expansion speed, expansion shape and expansion size of the fire-driven combustion front are calculated in real time according to the fire-driven combustion front evolution model.

[0243] In the embodiment, based on the multi-period horizontal, vertical, and three-dimensional monitoring results of the combustion front, iterative fitting and model optimization can be continuously performed through satellite remote sensing surface temperature inversion, fire drive temperature system simulation and estimation, and observation well temperature measured data, to analyze the combustion front evolution speed, obtain a fire drive evolution model, and predict the evolution process of the combustion front.

[0244] An embodiment of the present invention further provides a heavy oil reservoir fire flooding combustion front monitoring system based on satellite remote sensing technology. Preferably, the system is used to implement the above method embodiment.

[0245] Figure 2 FIG. 1 is a structural block diagram of a heavy oil reservoir fire flooding combustion front monitoring system based on satellite remote sensing technology according to an embodiment of the present invention. Figure 2 As shown, the system includes:

[0246] Data acquisition module 21: used to obtain satellite thermal infrared remote sensing images of the study area at night;

[0247] 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;

[0248] 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;

[0249] Horizontal distribution determination module 24: for using the surface temperature gradient grid data of the study area to classify the state into five categories: burned area, fire wall, coking zone, oil wall, and residual oil area, and determine the plane distribution of the burned area, fire wall, coking zone, oil wall, and residual oil area, thereby determining the horizontal distribution data of the fire flooding combustion front;

[0250] Vertical distribution determination module 25: used to determine the vertical depths of the burned area, fire wall, coking zone, oil wall, and remaining 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 fire flooding combustion front;

[0251] The three-dimensional determination module 26 is configured to perform reservoir temperature fitting interpolation based on the horizontal distribution data and the vertical distribution data of the fire drive combustion front to obtain the three-dimensional distribution data of the fire drive combustion front;

[0252] The fire drive combustion front evolution process determination module 27 is used to predict the evolution process of the fire drive combustion front based on the horizontal distribution data of the fire drive combustion front, the vertical distribution data of the fire drive combustion front and the three-dimensional distribution data of the fire drive combustion front at different periods.

[0253] In one embodiment, the ground surface temperature determination module 22 includes:

[0254] 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;

[0255] 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.

[0256] In one embodiment, the ground surface temperature determination module 22 further includes:

[0257] 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.

[0258] In one embodiment, the surface temperature gradient determination module 23 includes:

[0259] 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;

[0260] 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.

[0261] In one embodiment, the horizontal distribution determination module 24 includes:

[0262] The classification threshold determination submodule is used to classify fire flooding production areas into five types: burned area, fire wall, coking zone, oil wall, and residual oil area. Based on the impact of different regional states on the change of surface temperature gradient, the surface temperature gradient classification thresholds of the five regional states are determined;

[0263] Raster data classification submodule: used to obtain raster data of the five regional states of the study area, namely, burned area, fire wall, coking zone, oil wall, and remaining oil area, based on the surface temperature gradient raster data and classification threshold of the study area;

[0264] Horizontal distribution determination submodule: It is used to spatially vectorize the raster data of the five types of regional states in the study area, determine the plane distribution of the burned area, fire wall, coking zone, oil wall, and residual oil area, and thus obtain the horizontal distribution data of the fire drive combustion front.

[0265] In one embodiment, the vertical distribution determination module 25 includes:

[0266] Isothermal layer depth determination submodule: used to determine the isothermal layer depth based on the observation well temperature curve data;

[0267] Temperature system curve qualitative analysis submodule: used to determine the number of reservoir temperature systems based on the observed well temperature curve data;

[0268] 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;

[0269] Surface temperature heat transfer determination submodule: Based on the heat conduction and convection effects in the fire flooding production process, according to the surface temperature gradient grid data and the temperature values ​​of reservoirs at different depths in the study area, it establishes and solves the heat transfer model of the vertical temperature of the fire flooding combustion front, determines the vertical depths of the burned area, fire wall, coking zone, oil wall, and residual oil area, and thus determines the vertical distribution of the fire flooding combustion front.

[0270] In one embodiment, the 3D determination module 26 includes:

[0271] The submodule for determining the historical temperature value of a time-space point is used to predict the historical temperature value of a time-space point in the fire drive combustion front according to the horizontal distribution data and the vertical distribution data of the fire drive combustion front;

[0272] 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 fire drive combustion front;

[0273] Spatiotemporal theoretical variation function model determination submodule: used to establish a spatiotemporal theoretical variation function model based on the spatiotemporal empirical variation function;

[0274] The three-dimensional distribution determination submodule is used to perform reservoir temperature fitting and interpolation processing on the horizontal distribution data and the vertical distribution data of the fire drive combustion front through geostatistical methods and the space-time theoretical variation function model to obtain the three-dimensional distribution data of the fire drive combustion front;

[0275] Furthermore, the spatiotemporal point temperature history value determination submodule predicts the spatiotemporal point temperature history values ​​in the fire drive combustion front based on the following formula:

[0276] and

[0277] Where: z0 is the temperature history value of the space-time point in the fire drive combustion front, z i (i=1, ..., n) is the temperature value of the known time and space point in the i-th fire drive combustion front, λ i is the weight coefficient;

[0278] Furthermore, the spatiotemporal empirical variation function determination submodule establishes the spatiotemporal empirical variation function based on the following formula:

[0279]

[0280] 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 intervals;

[0281] Furthermore, the spatiotemporal theoretical variation function model determination submodule establishes the spatiotemporal theoretical variation function model based on the following formula:

[0282] γ(h S , h T )=γ S (h S )+γ T (h T )+γ ST (h ST )

[0283] Where, γ S (h S ),γ T (h T ),γ ST (h ST ) are spatial variogram, temporal variogram and spatiotemporal variogram respectively.

[0284] In one embodiment, the fire driving combustion front evolution process determination module 27 includes:

[0285] The fire drive combustion front spatiotemporal evolution speed determination submodule is used to determine the spatiotemporal evolution speed of the fire drive combustion front through a fitting regression method based on the horizontal distribution data of the fire drive combustion front at different periods, the vertical distribution data of the fire drive combustion front, and the three-dimensional distribution data of the fire drive combustion front;

[0286] The spatiotemporal evolution process determination submodule is used to establish a spatiotemporal universal Kriging prediction model based on the spatiotemporal evolution speed of the fire flooding combustion front, predict the change process of the reservoir temperature, and thus obtain the spatiotemporal evolution process of the fire flooding;

[0287] Furthermore, the fire drive combustion front spatiotemporal evolution speed determination submodule determines the fire drive combustion front spatiotemporal evolution speed based on the following formula:

[0288] Z(x)={Z(s,t)|s∈S,t∈T}

[0289] Where S represents the spatial domain of the fire drive combustion front, S∈R 2 ; T represents the time domain of the fire-driven combustion 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 time and space point (s, t) of the fire-driven combustion front, and represents the drift at the time and space point (s, t); R(s, t) is the remaining part of the fire-driven combustion front temperature 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);

[0290] Furthermore, the spatiotemporal evolution process determination submodule establishes a spatiotemporal pan-Kriging prediction model based on the following formula:

[0291]

[0292] 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 fire drive combustion front 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).

[0293] Figure 3 Schematic diagram of a device for monitoring the combustion front of a heavy oil reservoir fire flooding based on satellite remote sensing technology according to an embodiment of the present invention. Figure 3 The illustrated device for monitoring the combustion front of a heavy oil reservoir fire flooding based on satellite remote sensing technology is a general-purpose data processing device comprising a general-purpose computer hardware structure, including at least a processor 1000 and a memory 1111. The processor 1000 is configured to execute a heavy oil reservoir fire flooding combustion front monitoring program based on satellite remote sensing technology stored in the memory to implement the heavy oil reservoir fire flooding combustion front monitoring method based on satellite remote sensing technology of each method embodiment (for specific methods, refer to the description of the above method embodiments and will not be repeated here).

[0294] An embodiment of the present invention further provides a computer-readable storage medium storing one or more programs, which can be executed by one or more processors to implement the method for monitoring the combustion front of heavy oil reservoir fire flooding based on satellite remote sensing technology according to each method embodiment (for specific methods, please refer to the description of the above method embodiments and will not be repeated here).

[0295] 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.

[0296] 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.

[0297] 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.

[0298] 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.

[0299] 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.

[0300] 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 the combustion front of fire flooding 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 nighttime 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 raster data of the study area is determined; Using the surface temperature gradient grid data of the study area, the five states of burnt area, fire wall, coking zone, oil wall and residual oil area were classified to determine the plane distribution of burnt area, fire wall, coking zone, oil wall and residual oil area, thus determining the horizontal distribution data of the fire flooding combustion front. 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 burned area, fire wall, coking zone, oil wall and residual oil area are determined, thereby determining the vertical distribution data of the fire flooding combustion front; Based on the horizontal and vertical distribution data of the fire drive combustion front, reservoir temperature fitting and interpolation are performed to obtain the three-dimensional distribution data of the fire drive combustion front. The evolution of the fire drive combustion front is predicted based on the horizontal distribution data, vertical distribution data and three-dimensional distribution data of the fire drive combustion front at different periods.

2. The monitoring method according to claim 1, wherein: The nighttime surface temperature data of the study area determined based on the thermal infrared remote sensing image include: The obtained thermal infrared remote sensing images of the study area at night were processed with radiation calibration, atmospheric correction, geometric correction, etc. to obtain remote sensing surface reflectance data; 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 nighttime thermal infrared remote sensing inversion surface temperature data.

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: The surface temperature gradient data of the study area was calculated using the night surface temperature data of the study area. 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 temperature gradient.

6. The monitoring method according to claim 1, wherein: Using the surface temperature gradient grid data of the study area, the five states of burnt area, fire wall, coking zone, oil wall, and residual oil area were classified to determine the plane distribution of burnt area, fire wall, coking zone, oil wall, and residual oil area. The horizontal distribution data of the fire drive combustion front were determined, including: 1) Fire flooding production areas are classified into five types: burned area, fire wall, coking zone, oil wall, and residual oil area. Based on the impact of different regional states on the change of surface temperature gradient, the surface temperature gradient classification thresholds for the five types of regional states are determined; 2) Based on the surface temperature gradient grid data and classification thresholds in the study area, raster data of five regional states in the study area, namely, burned area, fire wall, coking zone, oil wall, and remaining oil area, were obtained; 3) The raster data of the five types of regional states in the study area were spatially vectorized to determine the planar distribution of the burned area, fire wall, coking zone, oil wall, and residual oil area, thereby obtaining the horizontal distribution data of the fire drive combustion front.

7. The monitoring method according to claim 6, wherein: In the process of spatial vectorization of the raster data of the five types of 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 five types of regional states in the horizontal direction of fire drive in the study area, thereby completing the spatial vectorization of the raster data of the five types of regional states in the study area.

8. The monitoring method according to claim 1, wherein: The vertical depths of the burned area, fire wall, coking zone, oil wall, and residual oil area 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, thereby determining the vertical distribution data of the fire flooding combustion front. This is achieved by the following method: Based on the surface temperature gradient grid data of the study area, targeting the effects of heat conduction and convection in the fire flooding production process, 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 fire flooding combustion front was established and solved. The vertical depths of the burned area, fire wall, coking zone, oil wall, and residual oil zone were determined, thereby determining the vertical distribution of the fire flooding combustion front.

9. The monitoring method according to claim 8, wherein: The vertical depths of the burned area, fire wall, coking zone, oil wall, and residual oil area 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, thereby determining the vertical distribution data of the fire flooding combustion front. 1) Determine the depth of the constant temperature 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 effects during the fire flooding production process, according to the surface temperature gradient grid data and combined with the reservoir temperature values ​​at different depths in the study area, a heat transfer model for the vertical temperature of the fire flooding combustion front was established and solved. The vertical depths of the burned area, fire wall, coking zone, oil wall, and residual oil zone were determined, thereby determining the vertical distribution of the fire flooding combustion front.

10. The monitoring method according to claim 9, wherein: Determining the depth of the isothermal layer based on the observation well temperature curve data includes: A linear regression model of the isothermal layer temperature and the surface temperature is established based on the well temperature meter 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 data.

11. The monitoring method according to claim 9, wherein: In step 2), the number of broken line segments of the reservoir temperature system curve is determined based on the well temperature instrument logging curve; wherein, the thermal conductivity λ in the reservoir temperature system curve is assumed to be 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, where D0 is the burial depth of the isothermal layer.

12. The monitoring method according to claim 9, wherein: In step 3), 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 by combining the night surface temperature data of the study area.

13. The monitoring method according to claim 9, wherein: 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 burned zone, fire wall, coking zone, oil wall, and residual oil zone are determined.

14. The monitoring method according to claim 13, wherein the vertical depths of the burned area, fire wall, coking zone, oil wall, and residual oil area 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.

15. The monitoring method according to claim 1, wherein: Based on the horizontal and vertical distribution data of the fire flooding combustion front, reservoir temperature fitting and interpolation are performed to obtain the three-dimensional distribution data of the fire flooding combustion front, including: According to the horizontal distribution data and vertical distribution data of the fire drive combustion front, the temperature history values ​​of the time and space points in the fire drive combustion front are predicted; Based on the temperature history values ​​of the time and space points in the fire drive combustion front, a time and space empirical variation function is established; According to the spatiotemporal empirical variogram, a spatiotemporal theoretical variogram model is established; Through geostatistical methods and the use of the space-time theoretical variation function model, the horizontal and vertical distribution data of the fire drive combustion front are subjected to reservoir temperature fitting and interpolation processing to obtain the three-dimensional distribution data of the fire drive combustion front.

16. The monitoring method according to claim 15, wherein: The prediction of the historical temperature values ​​of the time and space points in the fire drive combustion front is based on the following formula: and Where: z0 is the temperature history value of the space-time point in the fire drive combustion front, z i (i=1, ..., n) is the temperature value of the known time and space point in the i-th fire drive combustion front, λ i is the weight coefficient.

17. The monitoring method according to claim 15, 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.

18. The monitoring method according to claim 15, 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 ) Where, γ S (h S ),γ T (h T ),γ ST (h ST ) are spatial variogram, temporal variogram and spatiotemporal variogram respectively.

19. The monitoring method according to claim 18, 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.

20. The monitoring method according to claim 15, wherein: The reservoir temperature fitting interpolation processing is performed according to the spatiotemporal Kriging interpolation algorithm.

21. The monitoring method according to claim 1, wherein: The evolution process of the fire drive combustion front is predicted based on the horizontal distribution data of the fire drive combustion front, the vertical distribution data of the fire drive combustion front and the three-dimensional distribution data of the fire drive combustion front at different periods in the following manner: Based on the horizontal, vertical, and three-dimensional distribution data of the fire flooding combustion front at different periods, the spatiotemporal evolution rate of the fire flooding combustion front was determined by fitting regression method. Then, a spatiotemporal pan-Kriging prediction model was established to predict the variation of reservoir temperature and thus the spatiotemporal evolution process of fire flooding.

22. The monitoring method according to claim 21, wherein: The temporal and spatial evolution speed of the fire drive combustion front is determined by the following formula: Z(x)={Z(s,t)|s∈S,t∈T} Where S represents the spatial domain of the fire drive combustion front, S∈R 2 ; T represents the time domain of the fire-driven combustion 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 fire-driven combustion front, and represents the drift at the spacetime point (s, t); R(s, t) is the remaining part of the fire-driven combustion front temperature 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).

23. The monitoring method according to claim 22, wherein: The establishment of spatiotemporal universal Kriging prediction model is based on the following formula: 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 fire drive combustion front 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).

24. A heavy oil reservoir fire flooding combustion front monitoring system 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 raster 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 state into five categories: burned area, fire wall, coking zone, oil wall, and residual oil area, and determine the plane distribution of burned area, fire wall, coking zone, oil wall, and residual oil area, so as to determine the horizontal distribution data of the fire drive combustion front; Vertical distribution determination module: This module is used to determine the vertical depths of the burned area, fire wall, coking zone, oil wall, and residual 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 fire flooding combustion front; Three-dimensional determination module: used to perform reservoir temperature fitting interpolation based on the horizontal distribution data and vertical distribution data of the fire drive combustion front to obtain the three-dimensional distribution data of the fire drive combustion front; Fire drive combustion front evolution process determination module: used to predict the evolution process of the fire drive combustion front based on the horizontal distribution data of the fire drive combustion front, the vertical distribution data of the fire drive combustion front and the three-dimensional distribution data of the fire drive combustion front at different periods.

25. The monitoring system of claim 24, wherein: The surface temperature determination module includes: 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; 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.

26. The monitoring system of claim 24, 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 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.

27. The monitoring system of claim 24, 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 using the surface temperature gradient calculation model and generate a temperature gradient grid data map.

28. The monitoring system of claim 24, wherein: The horizontal distribution determination module includes: The classification threshold determination submodule is used to classify fire flooding production areas into five types: burned area, fire wall, coking zone, oil wall, and residual oil area. Based on the impact of different regional states on the change of surface temperature gradient, the surface temperature gradient classification thresholds of the five regional states are determined; Raster data classification submodule: used to obtain raster data of the five regional states of the study area, namely, burned area, fire wall, coking zone, oil wall, and remaining oil area, based on the surface temperature gradient raster data and classification threshold of the study area; Horizontal distribution determination submodule: It is used to spatially vectorize the raster data of the five types of regional states in the study area, determine the plane distribution of the burned area, fire wall, coking zone, oil wall, and residual oil area, and thus obtain the horizontal distribution data of the fire drive combustion front.

29. The monitoring system of claim 24, wherein: The vertical distribution determination module includes: Isothermal layer depth determination submodule: 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: Based on the heat conduction and convection effects in the fire flooding production process, according to the surface temperature gradient grid data and the temperature values ​​of reservoirs at different depths in the study area, it establishes and solves the heat transfer model of the vertical temperature of the fire flooding combustion front, determines the vertical depths of the burned area, fire wall, coking zone, oil wall, and residual oil area, and thus determines the vertical distribution of the fire flooding combustion front.

30. The monitoring system of claim 24, wherein: The 3D stereo determination module includes: The submodule for determining the historical temperature value of a time-space point is used to predict the historical temperature value of a time-space point in the fire drive combustion front according to the horizontal distribution data and the vertical distribution data of the fire drive combustion front; The spatiotemporal empirical variation function determination submodule is used to establish the spatiotemporal empirical variation function based on the temperature history values ​​of the spatiotemporal points in the fire drive combustion front; Spatiotemporal theoretical variation function model determination submodule: used to establish a 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 and vertical distribution data of the fire drive combustion front through geostatistical methods and the space-time theoretical variation function model to obtain the three-dimensional distribution data of the fire drive combustion front.

31. The monitoring system of claim 30, wherein: The time-space point temperature history value determination submodule predicts the time-space point temperature history value in the fire drive combustion front based on the following formula: and Where: z0 is the temperature history value of the space-time point in the fire drive combustion front, z i (i=1, ..., n) is the temperature value of the known time and space point in the i-th fire drive combustion front, λ i is the weight coefficient.

32. The monitoring system of claim 30, 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 meet the defined spatial and temporal intervals.

33. The monitoring system of claim 30, wherein: The spatiotemporal theoretical variation function model determination submodule establishes the spatiotemporal 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 ) Where, γ S (h S ),γ T (h T ),γ ST (h ST ) are spatial variogram, temporal variogram and spatiotemporal variogram respectively.

34. The monitoring system of claim 24, wherein: The fire drive combustion front evolution process determination module includes: The fire drive combustion front spatiotemporal evolution speed determination submodule is used to determine the spatiotemporal evolution speed of the fire drive combustion front through a fitting regression method based on the horizontal distribution data of the fire drive combustion front at different periods, the vertical distribution data of the fire drive combustion front, and the three-dimensional distribution data of the fire drive combustion front; Spatiotemporal evolution process determination submodule: It is used to establish a spatiotemporal universal Kriging prediction model based on the spatiotemporal evolution speed of the fire flooding combustion front, predict the change process of the reservoir temperature, and then obtain the spatiotemporal evolution process of the fire flooding.

35. The monitoring system of claim 34, wherein: The fire drive combustion front spatiotemporal evolution speed determination submodule determines the spatiotemporal evolution speed of the fire drive combustion front based on the following formula: Z(x)={Z(s,t)|s∈S,t∈T} Where S represents the spatial domain of the fire drive combustion front, S∈R 2 ; T represents the time domain of the fire-driven combustion 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 time and space point (s, t) of the fire-driven combustion front, and represents the drift at the time and space point (s, t); R(s, t) is the remaining part of the fire-driven combustion front temperature 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).

36. The monitoring system of claim 34, wherein: The spatiotemporal evolution process determination submodule establishes a spatiotemporal universal Kriging prediction model based on the following formula: 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 fire drive combustion front 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).

37. A device for monitoring the combustion front of a heavy oil reservoir fire flooding based on satellite remote sensing technology, comprising a processor and a memory; wherein: Memory for storing computer programs; The processor is configured to implement the steps of the method for monitoring the combustion front of fire flooding in heavy oil reservoirs based on satellite remote sensing technology as described in any one of claims 1 to 23 when executing the program stored in the memory.

38. 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 the combustion front of fire flooding in heavy oil reservoirs based on satellite remote sensing technology according to any one of claims 1 to 23.

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