WO2023202047A1 - 基于温压耦合电阻率约束的深部地温场预测方法及装置 - Google Patents

基于温压耦合电阻率约束的深部地温场预测方法及装置 Download PDF

Info

Publication number
WO2023202047A1
WO2023202047A1 PCT/CN2022/131137 CN2022131137W WO2023202047A1 WO 2023202047 A1 WO2023202047 A1 WO 2023202047A1 CN 2022131137 W CN2022131137 W CN 2022131137W WO 2023202047 A1 WO2023202047 A1 WO 2023202047A1
Authority
WO
WIPO (PCT)
Prior art keywords
resistivity
temperature
normalized
log
data
Prior art date
Application number
PCT/CN2022/131137
Other languages
English (en)
French (fr)
Inventor
胡祥云
黄国疏
蔡建超
杨健
马火林
周文龙
Original Assignee
中国地质大学（武汉）
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 中国地质大学（武汉） filed Critical 中国地质大学（武汉）
Publication of WO2023202047A1 publication Critical patent/WO2023202047A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/38—Processing data, e.g. for analysis, for interpretation, for correction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K13/00—Thermometers specially adapted for specific purposes
    • G01K13/10—Thermometers specially adapted for specific purposes for measuring temperature within piled or stacked materials
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N25/00—Investigating or analyzing materials by the use of thermal means
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
    • G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/10—Geothermal energy

Definitions

  • the present invention relates to the field of geothermal field prediction, and in particular to a deep geothermal field prediction method and device based on temperature-pressure coupling resistivity constraints.
  • Temperature is one of the key characteristics of the Earth's interior, and understanding it determines our ability to study basic geoscience questions and applied geothermal problems. Therefore, it is extremely important to estimate the temperature distribution characteristics of underground space as accurately as possible.
  • the first type of direct measurement method mainly obtains the temperature characteristics along the depth direction through borehole logging, and obtains the regional temperature field based on spatial interpolation of irregularly distributed borehole temperature measurements.
  • the cost of borehole temperature measurement is high and requires only a small number of borehole measurements.
  • Well temperature interpolation often leads to considerable errors, especially in areas with complex geological structures.
  • the second type of indirect calculation method is mainly based on geochemical or geophysical means to predict the geothermal field.
  • geochemical methods mainly use geochemical geothermometers to predict the temperature field, that is, infer temperature characteristics through collected data such as geochemical isotopes or gas components.
  • the geothermometer can predict the distribution range of heat storage temperatures, But they cannot estimate regional temperature distributions and cannot match predicted temperatures to depth.
  • the geothermometer based on geophysical detection on the one hand, predicts the geothermal field by building a heat transfer model in the study area, and on the other hand, it builds the coupling relationship between geophysical parameters (such as resistivity, wave speed, etc.) and temperature and based on geophysical detection Inversely infer the geothermal field; however, the establishment of the temperature model in the former requires accurate definition of underground space thermophysical parameters and model boundary conditions. Since these numerical values and a priori constraints can usually only be roughly estimated, the predicted temperature field will have a larger error.
  • the temperature model established for current underground temperature field prediction, especially deep space temperature prediction, has large errors, and it is unreasonable to predict the ground temperature field based on purely empirical formulas.
  • the present invention proposes a deep geothermal field prediction method and device based on temperature-pressure coupling resistivity constraints. Through the borehole logging resistivity-overlying formation pressure-temperature data pairs in the study area or adjacent areas, the different underground spaces are deduced and constructed. Characterize the precise relationship between normalized resistivity, temperature and pressure of the section (different depths); carry out fine structure inversion of electromagnetic detection data and gravity field data to obtain resistivity and density distribution characteristics, and normalize the resistivity respectively. processing and converting density data into overlying formation pressure; finally, combining the accurate characterization of the relationship between normalized resistivity and temperature and pressure at different depths in the study area and the normalized resistivity and overlying formation pressure to predict the deep underground temperature field.
  • the present invention proposes a deep geothermal field prediction method based on temperature-pressure coupling resistivity constraints.
  • the deep geothermal field prediction method includes the following steps:
  • Pressure control coefficient A(i), temperature control coefficient B(i) and constant coefficient C(i), i represents the i-th layer;
  • the values of the pressure control coefficient A(i), temperature control coefficient B(i) and constant coefficient C(i) in different well locations and different layers are used to construct a target data set with the depth of the corresponding layer. Regression analysis was performed on the data set to obtain the relationship between the pressure control coefficient A(i), temperature control coefficient B(i) and constant coefficient C(i) with depth z, which are expressed as A(z) and B(z) respectively. and C(z);
  • the resistivity profile is divided into M segments with depth, and the inverted resistivity of each segment is normalized to obtain the normalized inverted resistivity RN inv (x, y, z) of different nodes in different layers; at the same time, based on
  • the three-dimensional density distribution ⁇ g (x, y, z) is inverted from the gravity observation data and converted into the overlying formation pressure distribution Pre (x, y, z); where x is the lateral distance of the underground space node, and y is the underground space node.
  • the longitudinal distance of , z is the vertical depth of the underground space node;
  • the precise relationship between the normalized inversion resistivity and temperature at different nodes P(x,y,z) in different layers deep underground in the study area is obtained.
  • the precise relationship representation and the normalized inversion resistivity R Ninv (x, y, z) the distribution characteristics of the deep geothermal field T (x, y, z) in the study area are predicted.
  • the density in the well log resistivity-density-temperature data pair is converted into overlying formation pressure data, where the calculation formula for overlying formation pressure is:
  • P over (h) is the overlying formation pressure at different depths
  • ⁇ log is the formation log density
  • g is the gravity acceleration
  • h is the formation depth
  • ⁇ h is the log data spacing
  • N P is the log from the depth h position to the surface Number of data nodes.
  • the log resistivity-overlying formation pressure-temperature data are equally divided into N sections according to depth, and the log resistivity of each section of each well is normalized to obtain the normalization of different sections.
  • Normalized log resistivity-overlying formation pressure-temperature data set in which the log resistivity normalization processing formula is:
  • R log (i) is the log resistivity data set of all measuring points in the i-th section of each well;
  • max[R log (i) ] represents the maximum value of all logging resistivity data sets of each well in the i-th interval;
  • H Lmax is the maximum depth of the logging depth in each well;
  • H Lmin is the minimum depth of the logging depth in each well;
  • D Lseg is the thickness of the well logging interval, express Round to an integer.
  • F RTP (i) represents the constraint equation of the normalized resistivity-overlying formation pressure-temperature coupling relationship of the i-th layer, which is a function of R Norm (i), T i and P iover (i), expressed as F (R Norm (i),T i ,P iover (i));
  • R Norm (i) represents the normalized resistivity data set of all well logs in the i-th interval of each well;
  • P 0 represents normal pressure;
  • P over ( i) represents the data set of all overlying formation pressures in the i-th section of each well;
  • T i represents the set of all logging temperature data of each well in the i-th section.
  • the inversion resistivity of each section is normalized to obtain the normalized inversion resistivity R Ninv (x, y, z) of different nodes in different layers, where the inversion resistivity is normalized to
  • the unified processing formula is:
  • R inv (k,x,y,z) represents the inverted resistivity data
  • max[R inv (k)] is the inverted resistivity data R inv (x, y, z) in The maximum value in the k-th layer
  • R Ninv (k, x, y, z) is the data set of the normalized inversion resistivity R Ninv (x, y, z) in the k-th layer.
  • the three-dimensional density distribution ⁇ g (x, y, z) is inverted based on the gravity observation data and converted into the overlying formation pressure distribution Pre (x, y, z), where the conversion formula is:
  • P pre (x, y, z) represents the overlying formation pressure distribution calculated based on the inverted three-dimensional density distribution ⁇ g (x, y, z); ⁇ g (x, y, z) is the inverted three-dimensional density from gravity observation data Distribution; g is gravity acceleration; ⁇ z inv is the spacing of gravity inversion data along the depth z direction; N P is the number of inversion density data nodes from the depth z position to the surface.
  • the distribution characteristics of the deep geothermal field T (x, y, z) in the study area are predicted based on the precise relationship representation and the normalized inversion resistivity R Ninv (x, y, z), where,
  • the prediction formula for predicting the distribution characteristics of the deep geothermal field in the study area is:
  • T(x,y,z) is the temperature prediction value of each node;
  • a cal represents the pressure control coefficient used when calculating the deep temperature;
  • B cal represents the temperature control coefficient used when calculating the deep temperature;
  • C cal represents the calculated deep temperature temperature, the constant coefficient used.
  • a deep geothermal field prediction device based on temperature-pressure coupling resistivity constraints, the deep geothermal field prediction device includes the following modules:
  • the data set acquisition module is used to obtain the log resistivity-density-temperature data pair of the m borehole in the study area or adjacent area, and convert the density in the log resistivity-density-temperature data pair into overlying Formation pressure data, construct log resistivity-overlying formation pressure-temperature data pairs, divide the log resistivity-overlying formation pressure-temperature data pairs into N segments according to depth, and measure each segment of each well
  • the well resistivity is normalized to obtain the normalized log resistivity-overlying formation pressure-temperature data set of different layers;
  • the constraint equation construction and coefficient calculation module is used to construct the normalized resistivity-overlying formation pressure-temperature coupling relationship constraint equation F RTP based on multiple sets of log resistivity-overlying pressure-temperature data sets, and calculate different The pressure control coefficient A(i), temperature control coefficient B(i) and constant coefficient C(i) in F RTP at different sections of the well location, i represents the i-th section;
  • the coefficient regression analysis module is used to construct the target by comparing the values of the pressure control coefficient A(i), temperature control coefficient B(i) and constant coefficient C(i) in different well locations and different sections with the corresponding section depths. Data set, perform regression analysis on the target data set, and obtain the relationship between the pressure control coefficient A(i), the temperature control coefficient B(i) and the constant coefficient C(i) with the depth z, which are respectively expressed as A( z), B(z) and C(z);
  • the inversion resistivity normalization processing module is used to perform three-dimensional fine inversion of the electromagnetic data volume in the study area and obtain the inversion resistivity R inv (x, y, z) of each node P (x, y, z) in the underground space. ) distribution characteristics, and the inverted resistivity profile is divided into M segments along with the depth, and the inverted resistivity of each segment is normalized to obtain the normalized inverted resistivity of different nodes in different layers.
  • the overlying formation pressure conversion module is used to invert the three-dimensional density distribution ⁇ g (x, y, z) of gravity observation data and convert it into the overlying formation pressure distribution Pre (x, y, z); where x is the underground space node The lateral distance of , y is the longitudinal distance of underground space nodes, z is the vertical depth of underground space nodes;
  • the geothermal field distribution prediction module is used to constrain the pressure control coefficient A(i) and temperature control coefficient B(i) contained in the equation F RTP according to the normalized resistivity-overlying formation pressure-temperature coupling relationship at different depths underground.
  • the relationship between the normalized inversion resistivity and temperature at different nodes P(x,y,z) in different deep underground sections in the study area is obtained According to the precise relationship representation and the normalized inversion resistivity R Ninv (x, y, z), the distribution characteristics of the deep geothermal field T (x, y, z) in the study area are predicted.
  • the present invention establishes an accurate representation of the relationship between the normalized inversion resistivity and temperature of different underground layers and nodes in the study area based on the overlying formation pressure constraints.
  • the above-mentioned precise relationship representation converts the macroscopic resistivity characteristics of underground media into intuitive temperature field distribution, and the accuracy of the predicted value reaches 85.51%. It has strong practicability, wide and deep prediction range, and is of great significance for geothermal resource evaluation and temperature monitoring of geothermal development.
  • Figure 1 is a flow chart of a deep geothermal field prediction method based on temperature-pressure coupling resistivity constraints of the present invention
  • Figure 2 is a characteristic diagram of the variation of pressure control coefficient A(i), temperature control coefficient B(i), and constant coefficient C(i) with depth according to the present invention
  • Figure 3 is an inversion resistivity profile of the underground space in the research area of the present invention.
  • Figure 4 is a pressure profile of the overlying formation in the underground space of the research area of the present invention.
  • Figure 5 is a predicted cross-sectional view of the underground space temperature field in the research area of the present invention.
  • Figure 6 is a comparison verification diagram between the predicted temperature and the measured temperature in the research area of the present invention.
  • Figure 7 is a structural diagram of a deep geothermal field prediction device based on temperature-pressure coupling resistivity constraints of the present invention.
  • Embodiment 1 Refer to Figure 1, which is a flow chart of a deep geothermal field prediction method based on temperature and pressure coupling resistivity constraints of the present invention.
  • the embodiment provides a deep geothermal field prediction based on temperature and pressure coupling resistivity constraints. method, including the following steps:
  • the resistivity data of each layer is normalized using the following formula:
  • step S2 Based on the normalized well log resistivity-overlying formation pressure-temperature data set in step S1, calculate the normalized resistivity-overlying formation pressure-temperature coupling relationship constraint equations in different well locations and different sections through multiple regression.
  • F The pressure control coefficient A(i), temperature control coefficient B(i) and constant coefficient C(i) in RTP , and the relationship between the pressure control coefficient, temperature control coefficient and constant coefficient with the formation depth is obtained as A(h) ,B(h),C(h).
  • step S2 is specifically: based on the normalized resistivity-overlying formation pressure-temperature data set in step S1, construct the normalized resistivity-overlying formation pressure-temperature coupling relationship constraint equation F RTP , Calculate the pressure control coefficient A(i), temperature control coefficient B(i), and constant coefficient C(i) in F RTP (i) at different well locations and in different layers through multiple regression; divide the pressure control coefficient A(i) , the temperature control coefficient B(i) and the constant coefficient C(i) are respectively constructed with the depth of the corresponding layer to construct a target data set.
  • the target data set is subjected to regression analysis to obtain the pressure control coefficient, temperature control coefficient and constant coefficient.
  • the changing relationship of depth is expressed as A(h), B(h), C(h) respectively (refer to Figure 2).
  • the expression of the normalized resistivity-overlying formation pressure-temperature coupling relationship constraint equation F RTP (i) is:
  • F RTP (i) represents the constraint equation of the normalized resistivity-overlying formation pressure-temperature coupling relationship of the i-th layer, which is a function of R Norm (i), T i and P iover (i), expressed as F (R Norm (i),T i ,P iover (i));
  • R Norm (i) represents the normalized resistivity data set of all well logs in the i-th interval of each well;
  • P 0 represents normal pressure;
  • P over ( i) represents the data set of all overlying formation pressures in the i-th section of each well;
  • T i represents the set of all logging temperature data of each well in the i-th section.
  • step S3 is specifically: perform three-dimensional fine inversion of the electromagnetic data volume in the study area, and obtain the inverted resistivity R inv (x, y, z) of each node P (x, y, z) in the underground space.
  • Distribution characteristics (refer to Figure 3, which is to slice the three-dimensional inverted resistivity R inv (x, y, z) to obtain the resistivity profile corresponding to a certain electromagnetic survey line).
  • max[R inv (k)] is the inverted resistivity data R inv (x,y,z) The maximum value in the k-th layer
  • R Ninv (k,x,y,z) is the data set of the normalized inversion resistivity R Ninv (x,y,z) of the k-th layer.
  • step S4 is specifically: carry out detailed inversion of the gravity data volume of the study area, obtain the density ⁇ g (x, y, z) distribution characteristics of each node P (x, y, z) in the underground space, and then Use equation (8) to convert the inverted density into the overlying formation pressure P pre (x, y, z) distribution (refer to Figure 4, which is to slice the three-dimensional overlying formation pressure P pre (x, y, z) to obtain The pressure distribution of the overlying formation in the corresponding section of the electromagnetic survey line in step S3).
  • P pre (x, y, z) represents the overlying formation pressure distribution calculated based on the inverted three-dimensional density distribution ⁇ g (x, y, z); ⁇ g (x, y, z) is the inverted three-dimensional density from gravity observation data Distribution; g is gravity acceleration; ⁇ z inv is the spacing of gravity inversion data along the depth z direction; N P is the number of inversion density data nodes from the depth z position to the surface.
  • step S5 is specifically: the pressure control coefficient A(i) and the temperature control coefficient B(i) contained in the constraint equation F ⁇ TP based on the normalized resistivity-overlying formation pressure-temperature coupling relationship in step S2 ), the constant coefficient C(i) and their changes with depth A(z), B(z), C(z) and the overlying formation pressure P pre (x, y, z) in step S4, can be obtained Accurate representation of the relationship between P(x, y, z) normalized resistivity and temperature at different nodes deep in the study area, using the precise relationship representation and the normalized inversion resistivity data R Ninv (x in step S3 , y, z), the distribution characteristics of the temperature field T (x, y, z) of each node P (x, y, z) in the underground space can be calculated layer by layer and point by point using equation (9) (refer to Figure 5, which is The deep underground three-dimensional temperature field T (x, y, z) is sliced
  • T(x,y,z) is the temperature prediction value of each node;
  • a cal represents the pressure control coefficient used when calculating the deep temperature;
  • B cal represents the temperature control coefficient used when calculating the deep temperature;
  • C cal represents the calculated deep temperature temperature, the constant coefficient used.
  • Figure 6 is a comparative verification diagram between the predicted temperature and the measured temperature in the research area of the present invention; in this embodiment, the temperature variation curve with depth at the borehole temperature measurement point in the predicted temperature profile of the research area is extracted, and compared with Compare with actual well log temperatures.
  • Embodiment 2 Referring to Figure 7, this embodiment provides a deep geothermal field prediction device based on temperature-pressure coupling resistivity constraints, including the following modules:
  • Data set acquisition module 1 is used to obtain the log resistivity-density-temperature data pair of the m borehole in the study area or adjacent area, and convert the density in the log resistivity-density-temperature data pair to
  • the overlying formation pressure data is used to construct a log resistivity-overlying formation pressure-temperature data pair, and the log resistivity-overlying formation pressure-temperature data pair is divided into N segments according to the depth, and each segment of each well is The log resistivity is normalized to obtain the normalized log resistivity-overlying formation pressure-temperature data set of different layers;
  • Constraint equation construction and coefficient calculation module 2 is used to construct the normalized resistivity-overlying formation pressure-temperature coupling relationship constraint equation F RTP based on multiple sets of well log resistivity-overlying pressure-temperature data sets, and calculate The pressure control coefficient A(i), temperature control coefficient B(i) and constant coefficient C(i) in F RTP at different well locations and different sections, i represents the i-th section;
  • Coefficient regression analysis module 3 is used to construct the values of the pressure control coefficient A(i), temperature control coefficient B(i) and constant coefficient C(i) in different well locations and different sections with the corresponding section depths.
  • Target data set conduct regression analysis on the target data set, and obtain the relationship between the pressure control coefficient A(i), the temperature control coefficient B(i) and the constant coefficient C(i) with the depth z, respectively expressed as A (z), B(z) and C(z);
  • the inversion resistivity normalization processing module 4 is used to perform three-dimensional fine inversion of the electromagnetic data volume in the study area and obtain the inversion resistivity R inv (x, y, z) distribution characteristics, and the inverted resistivity profile is divided into M segments along with the depth, and the inverted resistivity of each segment is normalized to obtain the normalized inverted resistance of different nodes in different layers. Rate R Ninv (x,y,z);
  • Overlying formation pressure conversion module 5 is used to invert the three-dimensional density distribution ⁇ g (x, y, z) of gravity observation data and convert it into overlying formation pressure distribution Pre (x, y, z); where x is the underground space The lateral distance of the node, y is the longitudinal distance of the underground space node, z is the vertical depth of the underground space node;
  • the ground temperature field distribution prediction module 6 is used to constrain the pressure control coefficient A(i) and temperature control coefficient B( The relationship A(z), B(z) between i) and the constant coefficient C(i) and the pressure control coefficient A(i), the temperature control coefficient B(i) and the constant coefficient C(i) with the depth z, C(z) and the overlying formation pressure distribution Pre(x,y,z), the relationship between the normalized inversion resistivity and temperature at different nodes P(x,y,z) in different deep underground sections in the study area is obtained. According to the precise relationship representation and the normalized inversion resistivity R Ninv (x, y, z), the distribution characteristics of the deep geothermal field T (x, y, z) in the study area are predicted.
  • Embodiments of the present invention provide a deep geothermal field prediction method and device based on temperature-pressure coupling resistivity constraints.
  • the log resistivity-overlying formation pressure-temperature data of the m borehole in the study area or adjacent area are based on the depth, etc.

Abstract

一种基于温压耦合电阻率约束的深部地温场预测方法及装置，包括：将研究区或相邻区域m口钻孔的测井电阻率-上覆地层压力-温度数据依据深度等分成N段，且归一化处理各层段电阻率；推导不同地层不同层段归一化电阻率与温度、压力精确关系表征；反演研究区电磁数据体获取电阻率分布特征，将反演电阻率等分成M段并归一化处理为归一化反演电阻率；利用重力观测数据反演密度分布并换算成上覆地层压力；基于不同层段(深度)精确关系表征及归一化反演电阻率与上覆地层压力，便可逐层逐点计算地下深部温度场的展布特征。能够基于上覆地层压力约束，将地下介质宏观电阻率特征精确转换为可视化温度场分布，预测范围广且深。

Description

基于温压耦合电阻率约束的深部地温场预测方法及装置 技术领域
本发明涉及地温场预测领域，尤其涉及一种基于温压耦合电阻率约束的深部地温场预测方法及装置。
背景技术
温度是地球内部的关键特征之一，对它的了解决定了我们研究基础地球科学问题和应用地热问题的能力。因此，最大限度地准确估计地下空间温度分布特征显得极为重要。
目前，获取地球内部温度的方式主要有两大类：直接测量及间接计算。第一类方法直接测量主要是通过钻孔测井获取沿深度方向温度特征，并基于不规则分布的钻孔测温空间插值获取区域温度场，但钻孔测温成本高且通过少量钻孔测井温度插值常常导致相当大的误差，尤其在地质构造复杂区域。第二类方法间接计算主要是基于地球化学或地球物理手段预测地温场。其中，地球化学手段主要是利用地球化学地温计来预测温度场，即通过收集到的地球化学同位素或气体成分等数据反推温度特征，虽然这类间接地温计可以预测热储温度的分布范围，但它们不能估算区域性温度分布且无法将预测温度与深度匹配。而基于地球物理探测的地温计，一方面是通过构建研究区传热模型预测地温场，另一方面是通过搭建地球物理参数(如电阻率、波速等)与温度的耦合关系并基于地球物理探测反推地温场；但前者对于温度模型的建立需要准确定义地下空间热物性参数及模型边界条件，由于这些数值及先验约束条件通常只能粗略估计，因此导致预测的温度场会有较大的误差；而后者，利用地球物理参数(如电阻率)预测温度场目前主要是使用纯经验公式，其有效性被假设为不随空间位置的变化，即经验公式中的各项参数在任意地质环境及深度下均假设为定值，很明显这种方法是不合理的。因此，现有的温度估算方法无法准确预测钻井未到达深度的温度，也无法有效提供井间空间的温度展布，更无法准确预测区域性深部地温场展布特征。
发明内容
针对目前地下温度场预测尤其深部空间温度预测所建立的温度模型误差较大，且基于纯经验公式预测地温场的不合理性。本发明提出了一种基于温压耦合电阻率约束的深部地温场预测方法及装置，通过研究区或相邻区域钻孔测井电阻率-上覆地层压力-温度数据对，推导构建地下空间不同层段(不同深度)归一化电阻率与温度、压力之间的精确关系表征；开展电磁探测数据及重力场数据精细结构反演获取电阻率及密度分布特征，并分别将电阻率进行归一化处理、密度数据换算成上覆地层压力；最终结合研究区不同深度归一化电阻率与温度、压力之间精确关系表征及归一化电阻率、上覆地层压力实现地下深部温度场预测。
为了实现上述目的，根据本发明的第一方面，本发明提出了一种基于温压耦合电阻率约束的深部地温场预测方法，所述深部地温场预测方法包括以下步骤：
获取研究区或相邻区域m口钻孔的测井电阻率-密度-温度数据对，将所述测井电阻率-密度-温度数据对中的密度换算成上覆地层压力数据，构建测井电阻率-上覆地层压力-温度数据对，依据深度将所述测井电阻率-上覆地层压力-温度数据对等分成N段，并将每口井各段测井电阻率进行归一化处理，获得不同层段归一化测井电阻率-上覆地层压力-温度数据集；
根据多组所述测井电阻率-上覆压力-温度数据集构造归一化电阻率-上覆地层压力-温度耦合关系约束方程F RTP，并计算不同井位不同层位处F RTP中的压力控制系数A(i)、温度控制系数B(i)及常系数C(i)，i表示第i层段；
将所述压力控制系数A(i)、温度控制系数B(i)及常系数C(i)在不同井位不同层段的取值分别与对应层段深度构建目标数据集，对所述目标数据集进行回归分析，得到所述压力控制系数A(i)、温度控制系数B(i)及常系数C(i)随深度z的变化关系，分别表示为A(z)、B(z)和C(z)；
对研究区电磁数据体进行三维精细反演，获取地下空间各节点P(x,y,z)的反演电阻率R inv(x,y,z)的分布特征，并将所述反演电阻率的剖面随深度等分成M段，对每段的反演电阻率进行归一化处理，得到不同层段不同节点的归一化反演电阻率RN inv(x,y,z)；同时基于重力观测数据反演三维密度分布ρ g(x,y,z)并换算成上覆地层压力分布Pre(x,y,z)；其中，x为地下空间节点的横向距离，y为地下 空间节点的纵向距离，z为地下空间节点垂向深度；
根据地下不同深度的所述归一化电阻率-上覆地层压力-温度耦合关系约束方程F RTP(i)中包含的压力控制系数A(i)、温度控制系数B(i)与常系数C(i)及所述压力控制系数A(i)、温度控制系数B(i)及常系数C(i)随深度z的变化关系A(z)、B(z)、C(z)及所述上覆地层压力分布Pre(x,y,z)，得到研究区地下深部不同层段不同节点P(x,y,z)处归一化反演电阻率与温度之间的精确关系表征，根据所述精确关系表征及所述归一化反演电阻率R Ninv(x,y,z)预测研究区深部地温场T(x,y,z)展布特征。
优选地，所述将所述测井电阻率-密度-温度数据对中的密度换算成上覆地层压力数据，其中，上覆地层压力的计算公式为：
Figure PCTCN2022131137-appb-000001
P over(h)为不同深度上覆地层压力；ρ log为地层测井密度；g为重力加速度；h为地层深度；Δh为测井数据间距；N P为深度h位置处至地表处测井数据节点数。
优选地，所述依据深度将所述测井电阻率-上覆地层压力-温度数据对等分成N段，并将每口井各段测井电阻率进行归一化处理，获得不同层段归一化测井电阻率-上覆地层压力-温度数据集，其中，测井电阻率归一化处理公式为：
Figure PCTCN2022131137-appb-000002
各等分层段H range(i)表示为：
H Lmin+DL seg×(i-1)≤H range(i)≤H Lmin+DL seg×i
Figure PCTCN2022131137-appb-000003
R Norm(i,j)即为每口井在第i层段内第j个测点归一化测井电阻率，i＝1,2,3…N；R log(i,j)为每口井第i层段内第j个测点电阻率测井数据；R log(i)为每口井在第i层段内所有测点测井电阻率数据集；max[R log(i)]表示每口井在第i层段内所有测井电阻率数据集的最大值；H Lmax为每口井中测井深度的最大深度；H Lmin为每 口井中测井深度的最小深度；D Lseg为测井等分层段的厚度，
Figure PCTCN2022131137-appb-000004
表示
Figure PCTCN2022131137-appb-000005
四舍五入取整数。
优选地，所述归一化电阻率-上覆地层压力-温度耦合关系约束方程F RTP(i)的表达式为：
F RTP(i)＝F(R Norm(i),T i,P iover(i))
其中，R Norm(i)、T i及P iover三者之间耦合关系表示为：
Figure PCTCN2022131137-appb-000006
F RTP(i)表示第i层段归一化电阻率-上覆地层压力-温度耦合关系约束方程，为R Norm(i)、T i及P iover(i)的函数，表示为F(R Norm(i),T i,P iover(i))；R Norm(i)表示每口井在第i层段内所有测井归一化电阻率数据集；P 0表示常压；P over(i)表示每口井在第i层段内所有上覆地层压力数据集；T i表示每口井在第i层段内所有测井温度数据集。
优选地，所述对每段的反演电阻率进行归一化处理，得到不同层段不同节点的归一化反演电阻率R Ninv(x,y,z)，其中，反演电阻率归一化处理公式为：
Figure PCTCN2022131137-appb-000007
x为地下空间各节点横向距离，y为地下空间各节点纵向距离，z为地下空间各节点沿垂向深度；R inv(k,x,y,z)表示反演电阻率数据R inv(x,y,z)在第k层段中的数据集，k＝1,2,3,…M；max[R inv(k)]为反演电阻率数据R inv(x,y,z)在第k层段内的最大值；R Ninv(k,x,y,z)为归一化反演电阻率R Ninv(x,y,z)在第k层段的数据集。
优选地，所述基于重力观测数据反演三维密度分布ρ g(x,y,z)并换算成上覆地层压力分布Pre(x,y,z)，其中，换算公式为：
Figure PCTCN2022131137-appb-000008
P pre(x,y,z)表示基于反演三维密度分布ρ g(x,y,z)计算的上覆地层压力分布；ρ g(x,y,z)为重力观测数据反演三维密度分布；g为重力加速度；Δz inv为重力反演 数据沿深度z方向间距；N P为深度z位置处至地表处反演密度数据节点数。
优选地，所述根据所述精确关系表征及所述归一化反演电阻率R Ninv(x,y,z)预测研究区深部地温场T(x,y,z)展布特征，其中，预测研究区深部地温场展布特征的预测公式为：
Figure PCTCN2022131137-appb-000009
Figure PCTCN2022131137-appb-000010
Figure PCTCN2022131137-appb-000011
Figure PCTCN2022131137-appb-000012
T(x,y,z)为各节点温度预测值；A cal表示计算深部温度时，所采用的压力控制系数；B cal表示计算深部温度时，所采用的温度控制系数；C cal表示计算深部温度时，所采用的常系数。
根据本发明的第二方面，一种基于温压耦合电阻率约束的深部地温场预测装置，所述深部地温场预测装置包括以下模块：
数据集获取模块，用于获取研究区或相邻区域m口钻孔的测井电阻率-密度-温度数据对，将所述测井电阻率-密度-温度数据对中的密度换算成上覆地层压力数据，构建测井电阻率-上覆地层压力-温度数据对，依据深度将所述测井电阻率-上覆地层压力-温度数据对等分成N段，并将每口井各段测井电阻率进行归一化处理，获得不同层段归一化测井电阻率-上覆地层压力-温度数据集；
约束方程构造及系数计算模块，用于根据多组所述测井电阻率-上覆压力-温度数据集构造归一化电阻率-上覆地层压力-温度耦合关系约束方程F RTP，并计算不同井位不同层段处F RTP中的压力控制系数A(i)、温度控制系数B(i)及常系数C(i)，i表示第i层段；
系数回归分析模块，用于将所述压力控制系数A(i)、温度控制系数B(i)及常系数C(i)在不同井位不同层段的取值分别与对应层段深度构建目标数据集，对所述目标数据集进行回归分析，得到所述压力控制系数A(i)、温度控制系数B(i)及常系数C(i)随深度z的变化关系，分别表示为A(z)、B(z)和C(z)；
反演电阻率归一化处理模块，用于对研究区电磁数据体进行三维精细反演，获取地下空间各节点P(x,y,z)的反演电阻率R inv(x,y,z)的分布特征，并将所述反演电阻率的剖面随深度等分成M段，对每段的反演电阻率进行归一化处理，得到不同层段不同节点的归一化反演电阻率R Ninv(x,y,z)；
上覆地层压力换算模块，用于反演重力观测数据三维密度分布ρ g(x,y,z)并换算成上覆地层压力分布Pre(x,y,z)；其中，x为地下空间节点的横向距离，y为地下空间节点的纵向距离，z为地下空间节点垂向深度；
地温场分布预测模块，用于根据地下不同深度的所述归一化电阻率-上覆地层压力-温度耦合关系约束方程F RTP中包含的压力控制系数A(i)、温度控制系数B(i)与常系数C(i)及所述压力控制系数A(i)、温度控制系数B(i)与常系数C(i)随深度z的变化关系A(z)、B(z)、C(z)及所述上覆地层压力分布Pre(x,y,z)，得到研究区地下深部不同层段不同节点P(x,y,z)处归一化反演电阻率与温度之间的精确关系表征，根据所述精确关系表征及所述归一化反演电阻率R Ninv(x,y,z)预测研究区深部地温场T(x,y,z)展布特征。
本发明所采取的技术方案带来的有益效果是：本发明基于上覆地层压力约束建立研究区地下不同层段不同节点的归一化反演电阻率与温度之间的精确关系表征，根据所述精确关系表征将地下介质宏观电阻率特征转换为直观的温度场分布，预测值的准确率达到了85.51％。实用性强，预测范围广且深，对于地热资源评价、地热开发温度监测等具有重要意义。
附图说明
图1为本发明的一种基于温压耦合电阻率约束的深部地温场预测方法流程图；
图2为本发明的压力控制系数A(i)、温度控制系数B(i)、常系数C(i)随深度变化特征图；
图3为本发明的研究区地下空间反演电阻率剖图；
图4为本发明的研究区地下空间上覆地层压力剖图；
图5为本发明的研究区地下空间温度场预测剖面图；
图6为本发明的研究区预测温度与实测温度对比验证图；
图7为本发明的一种基于温压耦合电阻率约束的深部地温场预测装置的结构图。
具体实施方式
为了使本发明的目的、技术方案和效果更加清楚的理解，现将结合附图对本发明实施方式作进一步的描述。
实施例一：参考图1，图1为本发明的一种基于温压耦合电阻率约束的深部地温场预测方法流程图，实施例提供的一种基于温压耦合电阻率约束的深部地温场预测方法，包括以下步骤：
S1、获取研究区或相邻区域m口钻孔的测井电阻率-密度-温度数据对，将所述测井电阻率-密度-温度数据对中的密度换算成上覆地层压力数据，构建测井电阻率-上覆地层压力-温度数据对，依据深度将所述测井电阻率-上覆地层压力-温度数据对等分成N段，并将每口井各段测井电阻率进行归一化处理，获得不同层段归一化测井电阻率-上覆地层压力-温度数据集；
在本实施例中，步骤S1具体为：利用研究区或相邻区域的m＝4口钻孔的测井电阻率-密度-温度数据对，利用(1)式将m＝4口钻孔的测井密度换算成上覆地层压力数据，然后将m＝4口钻孔的测井电阻率-上覆地层压力-温度数据依据深度将各层段等分成D Lseg＝200m，并利用(2)式将各层段电阻率数据进行归一化处理：
Figure PCTCN2022131137-appb-000013
Figure PCTCN2022131137-appb-000014
各等分层段H range(i)表示为：
H Lmin+D Lseg×(i-1)≤H range(i)≤H Lmin+D Lseg×i  (3)
Figure PCTCN2022131137-appb-000015
P over(h)为不同深度上覆地层压力；ρ log为地层测井密度；g为重力加速度；h为地层深度；Δh为测井数据间距；N P为深度h位置处至地表处测井数据节点数； R Norm(i,j)即为每口井在第i层段内第j个测点归一化测井电阻率，i＝1,2,3…N；R log(i,j)为每口井第i层段内第j个测点电阻率测井数据；R log(i)为每口井在第i层段内所有测点测井电阻率数据集；max[R log(i)]表示每口井在第i层段内所有测井电阻率数据集的最大值；H Lmax为每口井中测井深度的最大深度；H Lmin为每口井中测井深度的最小深度；D Lseg为测井等分层段的厚度，
Figure PCTCN2022131137-appb-000016
表示
Figure PCTCN2022131137-appb-000017
四舍五入取整数。
S2、基于步骤S1中归一化测井电阻率-上覆地层压力-温度数据集，通过多元回归计算不同井位不同层段的归一化电阻率-上覆地层压力-温度耦合关系约束方程F RTP中的压力控制系数A(i)、温度控制系数B(i)及常系数C(i)，并获取压力控制系数、温度控制系数、常系数随地层深度的关系分别为A(h)，B(h)，C(h)。
在本实施例中，步骤S2具体为：基于步骤S1中归一化电阻率-上覆地层压力-温度数据集，构建归一化电阻率-上覆地层压力-温度耦合关系约束方程F RTP，通过多元回归计算不同井位不同层段F RTP(i)中的压力控制系数A(i)、温度控制系数B(i)、常系数C(i)；将所述压力控制系数A(i)、温度控制系数B(i)及常系数C(i)分别与对应层段深度构建目标数据集，对所述目标数据集进行回归分析，得到所述压力控制系数、温度控制系数及常系数随深度的变化关系，分别表示为A(h)，B(h)，C(h)(参考图2)。其中，归一化电阻率-上覆地层压力-温度耦合关系约束方程F RTP(i)的表达式为：
F RTP(i)＝F(R Norm(i),T i,P iover(i))
其中，R Norm(i)、T i及P iover三者之间耦合关系表示为：
Figure PCTCN2022131137-appb-000018
F RTP(i)表示第i层段归一化电阻率-上覆地层压力-温度耦合关系约束方程，为R Norm(i)、T i及P iover(i)的函数，表示为F(R Norm(i),T i,P iover(i))；R Norm(i)表示每口井在第i层段内所有测井归一化电阻率数据集；P 0表示常压；P over(i)表示每口井在第i层段内所有上覆地层压力数据集；T i表示每口井在第i层段内所有测井温度数据集。
S3、对研究区电磁数据体进行三维精细反演，获取地下空间各节点P(x,y,z)的反演电阻率ρ inv(x,y,z)的分布特征，并将所述反演电阻率归一化处理为 R Ninv(x,y,z)；其中，x为地下空间节点的横向距离，y为地下空间节点的纵向距离，z为地下空间节点垂向深度；
在本实施例中，步骤S3具体为：对研究区电磁数据体进行三维精细反演，获取地下空间各节点P(x,y,z)的反演电阻率R inv(x,y,z)的分布特征(参考图3，是对三维反演电阻率R inv(x,y,z)进行切片，获取某条电磁测线对应的电阻率剖面)。将所述反演电阻率剖面随深度等分成M＝54段，利用(7)式对每段的反演电阻率进行归一化处理，得到不同层段不同节点的归一化反演电阻率R Ninv(x,y,z)。
Figure PCTCN2022131137-appb-000019
x为地下空间各节点横向距离，y为地下空间各节点纵向距离，z为地下空间各节点沿垂向深度；R inv(k,x,y,z)表示反演电阻率数据R inv(x,y,z)在第k(k＝1,2,3,…M)层段中的数据集；max[R inv(k)]为反演电阻率数据R inv(x,y,z)在第k层段内的最大值；R Ninv(k,x,y,z)为归一化反演电阻率R Ninv(x,y,z)第k层段的数据集。
S4、基于重力观测数据反演三维密度分布ρ g(x,y,z)并换算成上覆地层压力分布Pre(x,y,z)；
在本实施例中，步骤S4具体为：对研究区重力数据体开展精细反演，获取地下空间各节点P(x,y,z)的密度ρ g(x,y,z)分布特征，然后利用(8)式将反演密度换算成上覆地层压力P pre(x,y,z)分布(参考图4，是对三维上覆地层压力P pre(x,y,z)进行切片，获取步骤S3中电磁测线对应剖面的上覆地层压力分布)。
Figure PCTCN2022131137-appb-000020
P pre(x,y,z)表示基于反演三维密度分布ρ g(x,y,z)计算的上覆地层压力分布；ρ g(x,y,z)为重力观测数据反演三维密度分布；g为重力加速度；Δz inv为重力反演数据沿深度z方向间距；N P为深度z位置处至地表处反演密度数据节点数。
S5、基于所述地下不同深度(层段)归一化电阻率-上覆地层压力-温度耦合关系约束方程F ρTP及所述Pre(x,y,z)与ρ Ninv(x,y,z)，可预测研究区深部地温场展 布特征。
在本实施例中，步骤S5具体为：基于步骤S2中归一化电阻率-上覆地层压力-温度耦合关系约束方程F ρTP中包含的压力控制系数A(i)、温度控制系数B(i)、常系数C(i)及三者随深度的变化A(z)、B(z)、C(z)及步骤S4中的上覆地层压力P pre(x,y,z)，可获取研究区深部不同节点处P(x,y,z)归一化电阻率与温度之间精确关系表征，利用所述精确关系表征及步骤S3中的归一化反演电阻率数据R Ninv(x,y,z)，可利用式(9)逐层逐点计算地下空间各节点P(x,y,z)的温度场T(x,y,z)展布特征(参考图5，是将地下深部三维温度场T(x,y,z)进行切片，获取步骤S3中电磁测线对应剖面的地下温度场分布特征)。
Figure PCTCN2022131137-appb-000021
Figure PCTCN2022131137-appb-000022
Figure PCTCN2022131137-appb-000023
Figure PCTCN2022131137-appb-000024
T(x,y,z)为各节点温度预测值；A cal表示计算深部温度时，所采用的压力控制系数；B cal表示计算深部温度时，所采用的温度控制系数；C cal表示计算深部温度时，所采用的常系数。
参考图6，图6为本发明的研究区预测温度与实测温度对比验证图；在本实施例中，提取研究区预测温度剖面中钻孔测温点位处温度随深度的变化曲线，并与实际测井温度进行对比。实验结果表明，D35钻孔预测温度与实测温度的拟合优度为R 2＝0.8551，由此表明，该实施例温度场预测精度为85.51％，预测温度与实测温度吻合程度较高。
实施例二：参考图7，本实施例提供了一种基于温压耦合电阻率约束的深部地温场预测装置，包括以下模块：
数据集获取模块1，用于获取研究区或相邻区域m口钻孔的测井电阻率-密度-温度数据对，将所述测井电阻率-密度-温度数据对中的密度换算成上覆地层压 力数据，构建测井电阻率-上覆地层压力-温度数据对，依据深度将所述测井电阻率-上覆地层压力-温度数据对等分成N段，并将每口井各段测井电阻率进行归一化处理，获得不同层段归一化测井电阻率-上覆地层压力-温度数据集；
约束方程构造及系数计算模块2，用于根据多组所述测井电阻率-上覆压力-温度数据集构造归一化电阻率-上覆地层压力-温度耦合关系约束方程F RTP，并计算不同井位不同层段处F RTP中的压力控制系数A(i)、温度控制系数B(i)及常系数C(i)，i表示第i层段；
系数回归分析模块3，用于将所述压力控制系数A(i)、温度控制系数B(i)及常系数C(i)在不同井位不同层段的取值分别与对应层段深度构建目标数据集，对所述目标数据集进行回归分析，得到所述压力控制系数A(i)、温度控制系数B(i)及常系数C(i)随深度z的变化关系，分别表示为A(z)、B(z)和C(z)；
反演电阻率归一化处理模块4，用于对研究区电磁数据体进行三维精细反演，获取地下空间各节点P(x,y,z)的反演电阻率R inv(x,y,z)的分布特征，并将所述反演电阻率的剖面随深度等分成M段，对每段的反演电阻率进行归一化处理，得到不同层段不同节点的归一化反演电阻率R Ninv(x,y,z)；
上覆地层压力换算模块5，用于反演重力观测数据三维密度分布ρ g(x,y,z)并换算成上覆地层压力分布Pre(x,y,z)；其中，x为地下空间节点的横向距离，y为地下空间节点的纵向距离，z为地下空间节点垂向深度；
地温场分布预测模块6，用于根据地下不同深度的所述归一化电阻率-上覆地层压力-温度耦合关系约束方程F RTP中包含的压力控制系数A(i)、温度控制系数B(i)与常系数C(i)及所述压力控制系数A(i)、温度控制系数B(i)与常系数C(i)随深度z的变化关系A(z)、B(z)、C(z)及所述上覆地层压力分布Pre(x,y,z)，得到研究区地下深部不同层段不同节点P(x,y,z)处归一化反演电阻率与温度之间的精确关系表征，根据所述精确关系表征及所述归一化反演电阻率R Ninv(x,y,z)预测研究区深部地温场T(x,y,z)展布特征。
本发明实施例提供一种基于温压耦合电阻率约束的深部地温场预测方法及装置，将研究区或相邻区域m口钻孔的测井电阻率-上覆地层压力-温度数据依据深度等分成N段，且归一化处理各层段电阻率；推导不同地层不同层段归一化电阻率与温度、压力精确关系表征F RTP(i)；反演研究区电磁数据体获取电阻率分 布特征，将反演电阻率等分成M段并归一化处理为R Ninv(x,y,z)；利用重力观测数据反演密度分布并换算成上覆地层压力Pre(x,y,z)；基于所述不同层段(深度)精确关系表征F RTP及R Ninv(x,y,z)与P pre(x,y,z)，便可逐层逐点计算地下深部温度场T(x,y,z)的展布特征。本发明能够基于上覆地层压力约束，将地下介质宏观电阻率特征精确转换为可视化温度场分布，预测范围广且深。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者系统不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者系统所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者系统中还存在另外的相同要素。
上述本发明实施例序号仅仅为了描述，不代表实施例的优劣。在列举了若干装置的单元权利要求中，这些装置中的若干个可以是通过同一个硬件项来具体体现。词语第一、第二、以及第三等的使用不表示任何顺序，可将这些词语解释为标识。
以上仅为本发明的优选实施例，并非因此限制本发明的专利范围，凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本发明的专利保护范围内。

Claims (8)

  1. 一种基于温压耦合电阻率约束的深部地温场预测方法，其特征在于，所述深部地温场预测方法包括以下步骤：
    获取研究区或相邻区域m口钻孔的测井电阻率-密度-温度数据对，将所述测井电阻率-密度-温度数据对中的密度换算成上覆地层压力数据，构建测井电阻率-上覆地层压力-温度数据对，依据深度将所述测井电阻率-上覆地层压力-温度数据对等分成N段，并将每口井各段测井电阻率进行归一化处理，获得不同层段归一化测井电阻率-上覆地层压力-温度数据集；
    根据多组所述测井电阻率-上覆压力-温度数据集构造归一化电阻率-上覆地层压力-温度耦合关系约束方程F RTP，并计算不同井位不同层位处F RTP中的压力控制系数A(i)、温度控制系数B(i)及常系数C(i)，i表示第i层段；
    将所述压力控制系数A(i)、温度控制系数B(i)及常系数C(i)在不同井位不同层段的取值分别与对应层段深度构建目标数据集，对所述目标数据集进行回归分析，得到所述压力控制系数A(i)、温度控制系数B(i)及常系数C(i)随深度z的变化关系，分别表示为A(z)、B(z)和C(z)；
    对研究区电磁数据体进行三维精细反演，获取地下空间各节点P(x,y,z)的反演电阻率R inv(x,y,z)的分布特征，并将所述反演电阻率的剖面随深度等分成M段，对每段的反演电阻率进行归一化处理，得到不同层段不同节点的归一化反演电阻率R Ninv(x,y,z)；同时基于重力观测数据反演三维密度分布ρ g(x,y,z)并换算成上覆地层压力分布Pre(x,y,z)；其中，x为地下空间节点的横向距离，y为地下空间节点的纵向距离，z为地下空间节点垂向深度；
    根据地下不同深度的所述归一化电阻率-上覆地层压力-温度耦合关系约束方程F RTP(i)中包含的压力控制系数A(i)、温度控制系数B(i)与常系数C(i)及所述压力控制系数A(i)、温度控制系数B(i)及常系数C(i)随深度z的变化关系A(z)、B(z)、C(z)及所述上覆地层压力分布Pre(x,y,z)，得到研究区地下深部不同层段不同节点P(x,y,z)处归一化反演电阻率与温度之间的精确关系表征，根据所述精确关系表征及所述归一化反演电阻率R Ninv(x,y,z)预测研究区深部地温场T(x,y,z)展布特征。
  2. 如权利要求1所述的深部地温场预测方法，其特征在于，所述将所述测井电阻率-密度-温度数据对中的密度换算成上覆地层压力数据，其中，上覆地层 压力的计算公式为：
    Figure PCTCN2022131137-appb-100001
    P over(h)为不同深度上覆地层压力；ρ log为地层测井密度；g为重力加速度；h为地层深度；Δh为测井数据间距；N P为深度h位置处至地表处测井数据节点数。
  3. 如权利要求1所述的深部地温场预测方法，其特征在于，所述依据深度将所述测井电阻率-上覆地层压力-温度数据对等分成N段，并将每口井各段测井电阻率进行归一化处理，获得不同层段归一化测井电阻率-上覆地层压力-温度数据集，其中，测井电阻率归一化处理公式为：
    Figure PCTCN2022131137-appb-100002
    各等分层段H range(i)表示为：
    H Lmin+D Lseg×(i-1)≤H range(i)≤H Lmin+D Lseg×i
    Figure PCTCN2022131137-appb-100003
    R Norm(i,j)即为每口井在第i层段内第j个测点归一化测井电阻率，i＝1,2,3…N；R log(i,j)为每口井第i层段内第j个测点电阻率测井数据；R log(i)为每口井在第i层段内所有测点测井电阻率数据集；max[R log(i)]表示每口井在第i层段内所有测井电阻率数据集的最大值；H Lmax为每口井中测井深度的最大深度；H Lmin为每口井中测井深度的最小深度；D Lseg为测井等分层段的厚度，
    Figure PCTCN2022131137-appb-100004
    表示
    Figure PCTCN2022131137-appb-100005
    四舍五入取整数。
  4. 如权利要求1所述的深部地温场预测方法，其特征在于，所述归一化电阻率-上覆地层压力-温度耦合关系约束方程F RTP(i)的表达式为：
    F RTP(i)＝F(R Norm(i),T i,P iover(i))
    其中，R Norm(i)、T i及P iover三者之间耦合关系表示为：
    Figure PCTCN2022131137-appb-100006
    F RTP(i)表示第i层段归一化电阻率-上覆地层压力-温度耦合关系约束方程，为R Norm(i)、T i及P iover(i)的函数，表示为F(R Norm(i),T i,P iover(i))；R Norm(i)表示每口井在第i层段内所有测井归一化电阻率数据集；P 0表示常压；P over(i)表示每口井在第i层段内所有上覆地层压力数据集；T i表示每口井在第i层段内所有测井温度数据集。
  5. 如权利要求1所述的深部地温场预测方法，其特征在于，所述对每段的反演电阻率进行归一化处理，得到不同层段不同节点的归一化反演电阻率R Ninv(x,y,z)，其中，反演电阻率归一化处理公式为：
    Figure PCTCN2022131137-appb-100007
    x为地下空间各节点横向距离，y为地下空间各节点纵向距离，z为地下空间各节点沿垂向深度；R inv(k,x,y,z)表示反演电阻率数据R inv(x,y,z)在第k层段中的数据集，k＝1,2,3,…M；max[R inv(k)]为反演电阻率数据R inv(x,y,z)在第k层段内的最大值；R Ninv(k,x,y,z)为归一化反演电阻率R Ninv(x,y,z)在第k层段的数据集。
  6. 如权利要求1所述的深部地温场预测方法，其特征在于，所述基于重力观测数据反演三维密度分布ρ g(x,y,z)并换算成上覆地层压力分布Pre(x,y,z)，其中，换算公式为：
    Figure PCTCN2022131137-appb-100008
    P pre(x,y,z)表示基于反演三维密度分布ρ g(x,y,z)计算的上覆地层压力分布；ρ g(x,y,z)为重力观测数据反演三维密度分布；g为重力加速度；Δz inv为重力反演数据沿深度z方向间距；N P为深度z位置处至地表处反演密度数据节点数。
  7. 如权利要求1所述的深部地温场预测方法，其特征在于，所述根据所述精确关系表征及所述归一化反演电阻率R Ninv(x,y,z)预测研究区深部地温场T(x,y,z)展布特征，其中，预测研究区深部地温场展布特征的预测公式为：
    Figure PCTCN2022131137-appb-100009
    Figure PCTCN2022131137-appb-100010
    Figure PCTCN2022131137-appb-100011
    Figure PCTCN2022131137-appb-100012
    T(x,y,z)为各节点温度预测值；A cal表示计算深部温度时，所采用的压力控制系数；B cal表示计算深部温度时，所采用的温度控制系数；C cal表示计算深部温度时，所采用的常系数。
  8. 一种基于温压耦合电阻率约束的深部地温场预测装置，其特征在于，所述深部地温场预测装置包括以下模块：
    数据集获取模块，用于获取研究区或相邻区域m口钻孔的测井电阻率-密度-温度数据对，将所述测井电阻率-密度-温度数据对中的密度换算成上覆地层压力数据，构建测井电阻率-上覆地层压力-温度数据对，依据深度将所述测井电阻率-上覆地层压力-温度数据对等分成N段，并将每口井各段测井电阻率进行归一化处理，获得不同层段归一化测井电阻率-上覆地层压力-温度数据集；
    约束方程构造及系数计算模块，用于根据多组所述测井电阻率-上覆压力-温度数据集构造归一化电阻率-上覆地层压力-温度耦合关系约束方程F RTP，并计算不同井位不同层段处F RTP中的压力控制系数A(i)、温度控制系数B(i)及常系数C(i)，i表示第i层段；
    系数回归分析模块，用于将所述压力控制系数A(i)、温度控制系数B(i)及常系数C(i)在不同井位不同层段的取值分别与对应层段深度构建目标数据集，对所述目标数据集进行回归分析，得到所述压力控制系数A(i)、温度控制系数B(i)及常系数C(i)随深度z的变化关系，分别表示为A(z)、B(z)和C(z)；
    反演电阻率归一化处理模块，用于对研究区电磁数据体进行三维精细反演，获取地下空间各节点P(x,y,z)的反演电阻率R inv(x,y,z)的分布特征，并将所述反演电阻率的剖面随深度等分成M段，对每段的反演电阻率进行归一化处理，得到不同层段不同节点的归一化反演电阻率R Ninv(x,y,z)；
    上覆地层压力换算模块，用于反演重力观测数据三维密度分布ρ g(x,y,z)并换 算成上覆地层压力分布Pre(x,y,z)；其中，x为地下空间节点的横向距离，y为地下空间节点的纵向距离，z为地下空间节点垂向深度；
    地温场分布预测模块，用于根据地下不同深度的所述归一化电阻率-上覆地层压力-温度耦合关系约束方程F RTP(i)中包含的压力控制系数A(i)、温度控制系数B(i)与常系数C(i)及所述压力控制系数A(i)、温度控制系数B(i)与常系数C(i)随深度z的变化关系A(z)、B(z)、C(z)及所述上覆地层压力分布Pre(x,y,z)，得到研究区地下深部不同层段不同节点P(x,y,z)处归一化反演电阻率与温度之间的精确关系表征，根据所述精确关系表征及所述归一化反演电阻率R Ninv(x,y,z)预测研究区深部地温场T(x,y,z)展布特征。
PCT/CN2022/131137 2022-04-22 2022-11-10 基于温压耦合电阻率约束的深部地温场预测方法及装置 WO2023202047A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202210428404.5A CN114895364B (zh) 2022-04-22 2022-04-22 基于温压耦合电阻率约束的深部地温场预测方法及装置
CN202210428404.5 2022-04-22

Publications (1)

Publication Number Publication Date
WO2023202047A1 true WO2023202047A1 (zh) 2023-10-26

Family

ID=82717893

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/131137 WO2023202047A1 (zh) 2022-04-22 2022-11-10 基于温压耦合电阻率约束的深部地温场预测方法及装置

Country Status (2)

Country Link
CN (1) CN114895364B (zh)
WO (1) WO2023202047A1 (zh)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109931054A (zh) * 2018-12-27 2019-06-25 西南石油大学 致密砂岩储层压力的预测方法
CN110566171A (zh) * 2019-07-15 2019-12-13 西南石油大学 一种超高压致密裂缝性砂岩气藏出砂预测方法
CN113176618A (zh) * 2021-03-23 2021-07-27 中国地质大学（武汉） 一种深部地温场预测方法、设备及存储介质
US20210264262A1 (en) * 2020-02-21 2021-08-26 Saudi Arabian Oil Company Physics-constrained deep learning joint inversion
CN113625364A (zh) * 2021-08-18 2021-11-09 西南石油大学 一种基于双重校正的泥页岩地层孔隙压力的计算方法
CN113901681A (zh) * 2021-09-22 2022-01-07 中国石油大学(华东) 一种全寿命周期页岩气储层双甜点三维可压性评估方法

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
MXPA01009829A (es) * 1999-04-02 2003-07-21 Conoco Inc Metodo para inversion de datos magneticos y de gravedad utilizando metodos de vector y tensor con formacion de imagenes sismicas y prediccion de geopresion para petroleo, gas y produccion y exploracion mineral.
CN106401574B (zh) * 2015-07-28 2020-06-19 中国石油化工股份有限公司 一种钻前高温地热井地层压力的预测方法
CN106814388B (zh) * 2016-12-27 2018-11-02 中国石油大学(北京) 砂泥岩储层地层压力的地震预测方法及装置
GB2563048A (en) * 2017-06-01 2018-12-05 Equinor Energy As Method of calculating temperature and porosity of geological structure
CN109667573B (zh) * 2018-12-12 2022-07-15 中国石油化工股份有限公司江汉油田分公司勘探开发研究院 三维页岩储层孔隙压力预测方法、装置和电子设备

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109931054A (zh) * 2018-12-27 2019-06-25 西南石油大学 致密砂岩储层压力的预测方法
CN110566171A (zh) * 2019-07-15 2019-12-13 西南石油大学 一种超高压致密裂缝性砂岩气藏出砂预测方法
US20210264262A1 (en) * 2020-02-21 2021-08-26 Saudi Arabian Oil Company Physics-constrained deep learning joint inversion
CN113176618A (zh) * 2021-03-23 2021-07-27 中国地质大学（武汉） 一种深部地温场预测方法、设备及存储介质
CN113625364A (zh) * 2021-08-18 2021-11-09 西南石油大学 一种基于双重校正的泥页岩地层孔隙压力的计算方法
CN113901681A (zh) * 2021-09-22 2022-01-07 中国石油大学(华东) 一种全寿命周期页岩气储层双甜点三维可压性评估方法

Also Published As

Publication number Publication date
CN114895364A (zh) 2022-08-12
CN114895364B (zh) 2023-04-18

Similar Documents

Publication Publication Date Title
US20210332694A1 (en) Method to predict overpressure uncertainty from normal compaction trendline uncertainty
Altaf et al. Downscaling the 2D Bénard convection equations using continuous data assimilation
Diaz et al. Drilling data from an enhanced geothermal project and its pre-processing for ROP forecasting improvement
He et al. Three-dimensional reservoir description from multiwell pressure data and prior information
EP2810101B1 (en) Improving efficiency of pixel-based inversion algorithms
US20130204534A1 (en) Method Of Estimating A Subterranean Formation Property
CN110517794A (zh) 一种建立页岩气藏的埋藏-热演化史图的方法
US10989831B2 (en) Determining permeability in subsurface anisotropic formations
CN113176618B (zh) 一种深部地温场预测方法、设备及存储介质
CN108301825B (zh) 一种高温高压储层孔隙压力分析方法
Landa et al. Reservoir characterization constrained to well-test data: a field example
Gamal et al. Predicting the rock sonic logs while drilling by random forest and decision tree-based algorithms
Ismadi et al. The use of combined static-and dynamic-material-balance methods with real-time surveillance data in volumetric gas reservoirs
Badertdinova et al. Numerical method for solving the inverse problem of nonisothermal filtration
WO2023202047A1 (zh) 基于温压耦合电阻率约束的深部地温场预测方法及装置
Xu et al. The information content and integration of distributed-temperature-sensing data for near-wellbore-reservoir characterization
Yanran et al. Mathematical modeling for total organic carbon content prediction with logging parameters by neural networks: A case study of shale gas well in South China
CN114895365B (zh) 高温高压岩心电阻率标定的深部地温场预测方法及装置
CN111594156B (zh) 一种天然气水合物饱和度计算方法及系统
Worthington et al. Optimizing the value of reservoir simulation through quality-assured initialization
WO2018069742A1 (en) Petrophysical field evaluation using self-organized map
Karkevandi-Talkhooncheh et al. Estimating reservoir properties using downhole temperature and pressure data
Mu et al. Influence of correlation scale errors on aquifer hydraulic conductivity inversion precision
CN112709566B (zh) 一种碳酸盐岩气藏产层的预测方法
Deng et al. A copula-based statistical method of estimating production profile from temperature measurement for perforated horizontal wells

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22938266

Country of ref document: EP

Kind code of ref document: A1