A three-dimensional electromagnetic method for fracture monitoring

Abstract

The present application relates to the technical field of geophysical exploration, and provides a three-dimensional electromagnetic method fracturing monitoring method, comprising: collecting parameter data of a target well; establishing a target well work area geoelectric model, and selecting reasonable acquisition parameters through forward modeling of the target well work area geoelectric model; arranging a three-dimensional measuring network according to a fracturing construction scheme; collecting natural field source and artificial field source data in the fracturing monitoring process; obtaining natural potential data before and after fracturing, induced polarization method data before and after fracturing, time-frequency electromagnetic method data before and after fracturing, controllable source magnetotelluric method apparent resistivity data before and after fracturing and controllable source magnetotelluric method phase data through processing; obtaining differential anomaly data through calculation, and comprehensively analyzing all differential anomaly data to obtain fracturing fluid and fracture distribution pattern data in the complete fracturing process. The present application reduces the multi-solution, improves the precision and monitoring accuracy, and reduces the construction cost.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, and in particular to a three-dimensional electromagnetic fracturing monitoring method. Background Technology

[0002] With the deepening of oil and gas exploration and development, oil and gas well fracturing technology has been widely applied. During the oil and gas well fracturing process, the propagation of fractures has a significant impact on the production capacity and development effect of the oil and gas well. Therefore, monitoring the propagation of fractures is particularly important.

[0003] The current electromagnetic fracturing monitoring technology mainly collects the horizontal electric field component. The information obtained from observing single electromagnetic component data is limited, and its ability to reflect anomalies caused by changes in fracturing fluid is insufficient. It cannot monitor various electromagnetic anomalies that reflect the dynamic information of fracturing fluid during the fracturing process, which affects the monitoring of fracture propagation. Summary of the Invention

[0004] In view of this, the present invention provides a three-dimensional electromagnetic fracturing monitoring method to solve the problem that the existing technology cannot monitor various electromagnetic anomaly data that reflect the dynamic information of fracturing fluid during the fracturing process, thus making it impossible to accurately obtain characteristic data of fracturing fluid and fracture distribution morphology.

[0005] This invention provides a three-dimensional electromagnetic fracturing monitoring method, comprising:

[0006] S1. Collect parameter data of the target well, including well logging data, well trajectory data, and geological data of the target area;

[0007] S2. Establish a geoelectric model of the target well area based on the parameter data of the target well, and select reasonable acquisition parameter data through forward modeling of the target well area geoelectric model. The reasonable acquisition parameter data includes: excitation frequency, measuring line distance, point distance, transmit / receive distance, and excitation electrode distance in the transmission frequency table.

[0008] S3. According to the target well fracturing construction plan, a three-dimensional measuring network is set up directly above the fracturing section of the target well to monitor the entire fracturing process;

[0009] S4. During the fracturing monitoring process, according to the target well fracturing construction plan, the system transmits data at set time intervals according to the frequencies in the frequency table to collect natural and artificial field source data.

[0010] S5. Extract the natural source data during the non-emission period to obtain the natural source electric field time series data before and after fracturing, and obtain the natural potential data before and after fracturing based on the natural source electric field time series data before and after fracturing.

[0011] During the artificial field source emission period, the artificial field source data within the emission period is extracted to obtain the artificial source electric field time series data and magnetic field time series data before and after fracturing. The artificial source electric field time series data and magnetic field time series data before and after fracturing are processed separately to obtain the induced polarization method data before and after fracturing, the time-frequency electromagnetic method data before and after fracturing, the controlled source magnetotelluric method apparent resistivity data before and after fracturing, and the controlled source magnetotelluric method phase data before and after fracturing.

[0012] S6. Based on the natural potential data before and after fracturing, the induced polarization data before and after fracturing, the time-frequency electromagnetic data before and after fracturing, and the apparent resistivity data and phase data of controlled-source magnetotelluric method before and after fracturing, differential anomaly data are calculated and obtained. All differential anomaly data are then comprehensively analyzed to obtain characteristic data of the distribution morphology of fracturing fluid and fractures during the complete fracturing process.

[0013] Furthermore, the deployment of the three-dimensional survey network includes:

[0014] Design a survey network based on known fracturing sections and clusters;

[0015] The point spacing, the transmit / receive distance, and the excitation electrode distance are designed based on the reasonable parameters.

[0016] Furthermore, the frequencies in the transmission frequency table are the transmission frequencies of artificial field sources.

[0017] Furthermore, the acquisition of the natural potential data before and after fracturing includes:

[0018] During the non-emission period, the natural source data is extracted to obtain the time series data of the natural source electric field before and after fracturing. The data of the time series data of the natural source electric field before and after fracturing with a signal-to-noise ratio higher than a set value are selected, and their average value is calculated to obtain the natural potential data before and after fracturing.

[0019] Furthermore, the acquisition of the induced polarization method data before and after fracturing includes:

[0020] The artificial source electric field time series data before and after fracturing are processed by DC drift correction, same-frequency halving superposition and electrode distance correction. Then, the long-period flat segment data is extracted and its average value is calculated to obtain the excitation polarization method data before and after fracturing. The long period refers to the period when the frequency is the lowest.

[0021] Furthermore, the acquisition of the time-frequency electromagnetic method data before and after fracturing includes:

[0022] The artificial source electric field time series data and magnetic field time series data before and after fracturing are subjected to DC drift correction, halved superposition at the same frequency, Fourier transform and current normalization to obtain frequency domain electric field data and magnetic field data before and after fracturing. The frequency domain electric field data is time-frequency electromagnetic method data.

[0023] Furthermore, the acquisition of the controlled-source magnetotelluric apparent resistivity data and controlled-source magnetotelluric phase data before and after fracturing includes:

[0024] Based on the frequency domain electric field and magnetic field data before and after fracturing, the controlled-source magnetotelluric apparent resistivity and controlled-source magnetotelluric phase data before and after fracturing are obtained by calculating according to the Carnia apparent resistivity formula.

[0025] Furthermore, the signal-to-noise ratio setting range in S5 is higher than 30 dB.

[0026] Furthermore, the expression for calculating the anomaly difference is as follows:

[0027] dX=(X2-X1) / (X2+X1)

[0028] Wherein, dX represents the data differential anomaly, X2 represents the data after fracturing, and X1 represents the data before fracturing. The data before fracturing includes the spontaneous potential method data, the induced polarization method data, the time-frequency electromagnetic method data, the controlled-source magnetotelluric apparent resistivity data, and the controlled-source magnetotelluric phase data before fracturing. The parameter data after fracturing includes the spontaneous potential method data, the induced polarization method data, the time-frequency electromagnetic method data, the controlled-source magnetotelluric apparent resistivity data, and the controlled-source magnetotelluric phase data after fracturing.

[0029] Furthermore, after S6, it also includes:

[0030] By using differential anomaly data during the complete fracturing process, characteristic data of the fracturing fluid during the complete fracturing process are obtained. Characteristic data of the fracture distribution morphology are then deduced from the characteristic data of the fracturing fluid. The characteristic data of the fracturing fluid includes volume, and the characteristic data of the fracture distribution morphology includes length, width, and height.

[0031] The advantages of this invention compared to the prior art are:

[0032] 1. This invention collects electric field and magnetic field components by setting up a three-dimensional measurement network, and monitors differential anomaly data that reflects the dynamic information of fracturing fluid during the fracturing process;

[0033] 2. Compared with the traditional electrical resistivity monitoring method, the present invention obtains five types of abnormal data, thereby obtaining the distribution morphology of fracturing fluid and fractures, reducing ambiguity, improving accuracy and monitoring precision, and reducing construction costs;

[0034] 3. Based on differential anomaly data, this invention obtains characteristic data of fracturing fluid during the complete fracturing process, and inversely derives characteristic data of fracture distribution morphology, thereby providing data for predicting fracturing channeling and casing deformation risks. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in this invention, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of a three-dimensional electromagnetic fracturing monitoring method provided in an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram of the deployment of the three-dimensional electromagnetic fracturing monitoring field device provided in an embodiment of the present invention;

[0038] Figure 3 This is a planar schematic diagram of the amplitude difference anomaly of the time-frequency electromagnetic method provided in the embodiment of the present invention;

[0039] Figure 4 This is a planar schematic diagram of the apparent resistivity differential anomaly using the controlled-source magnetotelluric method provided in an embodiment of the present invention;

[0040] Figure 5 This is a schematic cross-sectional view of the phase differential anomaly of the controlled-source magnetotelluric method provided in an embodiment of the present invention;

[0041] Figure 6 This is a planar schematic diagram of the height prediction of the hydraulic fracturing network provided in an embodiment of the present invention. Detailed Implementation

[0042] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0043] The following will describe in detail, with reference to the accompanying drawings, a three-dimensional electromagnetic fracturing monitoring method of the present invention.

[0044] Figure 1 This is a flowchart of a three-dimensional electromagnetic fracturing monitoring method provided in an embodiment of the present invention.

[0045] like Figure 1 As shown, this three-dimensional electromagnetic fracturing monitoring method includes:

[0046] S1. Collect parameter data of the target well, including well logging data, well trajectory data, and geological data of the target area;

[0047] The target well is the well in which the fracturing process needs to be monitored. The parameters of the target well include logging data, well trajectory data, geological data of the target area, and some auxiliary data.

[0048] S2. Establish a geoelectric model of the target well area based on the parameter data of the target well, and select reasonable acquisition parameter data through forward modeling of the target well area.

[0049] The reasonable acquisition parameters include: excitation frequency, measuring line distance, point distance, transmit / receive distance, and excitation electrode distance from the transmission frequency table. For example, in the application scenario, the excitation frequency is 50-0.05Hz, the measuring line distance is 60-90m, the point distance is 20-50m, the transmit / receive distance is 2-5km, and the excitation electrode distance is 4-6km.

[0050] Figure 2 This is a schematic diagram of the deployment of the three-dimensional electromagnetic fracturing monitoring field device provided in an embodiment of the present invention.

[0051] S3. According to the target well fracturing construction plan, a three-dimensional measuring network is set up directly above the fracturing section of the target well to monitor the entire fracturing process;

[0052] The fracturing construction plan is provided by Party A or the fracturing operator, and Party B's fracturing monitoring team carries out fracturing monitoring operations according to the fracturing construction plan, including setting up a three-dimensional measurement network.

[0053] like Figure 2 As shown, the high-power transmitter transmits square wave signals of different frequencies into the ground through transmitting electrodes A and B according to the frequencies in the transmission frequency table. The electrodes and magnetic rods are arranged in a three-dimensional array above the fracturing well to receive electric field signals and magnetic field signals, respectively.

[0054] The establishment of the three-dimensional survey network includes:

[0055] Design a survey network based on known fracturing sections and clusters;

[0056] Among them, the survey network is a combination of survey lines in three dimensions.

[0057] The point spacing, the transmit / receive distance, and the excitation electrode distance are designed based on the reasonable parameters.

[0058] This invention collects electric and magnetic field components by setting up a three-dimensional measurement network, and monitors electromagnetic anomaly data of fracturing fluid dynamics during the entire process before and after reactive fracturing.

[0059] S4. During the fracturing monitoring process, according to the target well fracturing construction plan, the system transmits data at set time intervals according to the frequencies in the frequency table to collect natural and artificial field source data.

[0060] The set time interval is set according to human needs, the frequency in the emission frequency table is the emission frequency of the artificial field source, and the collection of natural field source data and artificial field source data means collecting the electric field component parallel to the emission source and the magnetic field component perpendicular to the emission source.

[0061] S5. Extract the natural source data during the non-emission period to obtain the natural source electric field time series data before and after fracturing, and obtain the natural potential data before and after fracturing based on the natural source electric field time series data before and after fracturing.

[0062] In step S5, obtaining the natural potential data before and after fracturing includes:

[0063] During the non-emission period, the natural source data is extracted to obtain the natural source electric field time series data before and after fracturing. Data from periods with a signal-to-noise ratio higher than the set value are selected from the natural source electric field time series data before and after fracturing, and their average value is taken to obtain the natural potential data before and after fracturing.

[0064] The signal-to-noise ratio is set to 30 dB.

[0065] During the artificial field source emission period, the artificial field source data within the emission period is extracted to obtain the artificial source electric field time series data and magnetic field time series data before and after fracturing. The artificial source electric field time series data and magnetic field time series data before and after fracturing are processed separately to obtain the induced polarization method data before and after fracturing, the time-frequency electromagnetic method data before and after fracturing, the controlled source magnetotelluric method apparent resistivity data before and after fracturing, and the controlled source magnetotelluric method phase data before and after fracturing.

[0066] The artificial field source emission time period refers to the time period during which emission is carried out according to the frequency of the emission frequency table.

[0067] In step S5, obtaining the induced polarization method data before and after fracturing includes:

[0068] The artificial source electric field time series data before and after fracturing are processed by DC drift correction, same-frequency halving superposition and electrode distance correction. Then, the long-period flat segment data is extracted and its average value is calculated to obtain the excitation polarization method data before and after fracturing. The long period refers to the period when the emission frequency is the lowest.

[0069] Figure 3 This is a planar schematic diagram of the amplitude difference anomaly of the time-frequency electromagnetic method provided in the embodiment of the present invention.

[0070] like Figure 3 As shown, after processing the time-frequency electromagnetic method data collected before and after fracturing operations, a time-frequency electromagnetic method amplitude difference anomaly plane map is obtained. Figure 3 A significant amplitude difference anomaly zone appeared on both sides of the horizontal well. The black cross represents the midpoint of each fracturing section of the horizontal well. The range of the anomaly zone can be used to predict the distribution range of fracturing fluid.

[0071] In step S5, obtaining the time-frequency electromagnetic method data before and after fracturing includes:

[0072] The artificial source electric field time series data and magnetic field time series data before and after fracturing are subjected to DC drift correction, halved superposition at the same frequency, Fourier transform and current normalization to obtain frequency domain electric field data and magnetic field data before and after fracturing. The frequency domain electric field data is time-frequency electromagnetic method data.

[0073] Figure 4 This is a planar schematic diagram of the apparent resistivity difference anomaly using the controlled-source magnetotelluric method provided in an embodiment of the present invention.

[0074] After processing the controlled-source magnetotelluric (CMT) data collected before and after fracturing operations, a controlled-source magnetotelluric amplitude difference anomaly plane map was obtained. Figure 4 A distinct apparent resistivity difference anomaly zone appeared on both sides of the horizontal well. The black cross represents the midpoint of each fracturing section of the horizontal well. The extent of the anomaly zone can be used to predict the distribution range of fracturing fluid.

[0075] Figure 5 This is a schematic cross-sectional view of the phase difference anomaly obtained by the controlled-source magnetotelluric method provided in an embodiment of the present invention.

[0076] After processing the controlled-source magnetotelluric data collected before and after fracturing operations, a phase difference anomaly profile was obtained. In the figure, obvious phase difference anomaly zones appeared above, below, left, and right sides of the circled area of ​​the horizontal well. The range of the anomaly zone can be used to predict the distribution range of fracturing fluid.

[0077] In step S5, obtaining the controlled-source magnetotelluric apparent resistivity data and controlled-source magnetotelluric phase data before and after fracturing includes:

[0078] Based on the frequency domain electric field and magnetic field data before and after fracturing, the controlled-source magnetotelluric apparent resistivity and controlled-source magnetotelluric phase data before and after fracturing are obtained by calculating according to the Carnia apparent resistivity formula.

[0079] Existing electrical resistivity monitoring methods can only obtain a single anomaly data, while the present invention, compared with traditional electrical resistivity monitoring methods, obtains five types of anomaly data, thereby obtaining the distribution morphology of fracturing fluid and fractures, reducing ambiguity, improving accuracy and monitoring precision, and reducing costs.

[0080] Figure 6 This is a planar schematic diagram of the height prediction of the hydraulic fracturing network provided in an embodiment of the present invention.

[0081] By comprehensively analyzing and interpreting the differential anomalies of various data sources, a predicted planar map of the fracture network height is obtained. Figure 6 The colors in the middle describe the height of the hydraulic fracturing network in different areas.

[0082] S6. Based on the natural potential data before and after fracturing, the induced polarization data before and after fracturing, the time-frequency electromagnetic data before and after fracturing, and the apparent resistivity data and phase data of controlled-source magnetotelluric method before and after fracturing, differential anomaly data are calculated and obtained. All differential anomaly data are then comprehensively analyzed to obtain data on the distribution morphology of fracturing fluid and fractures during the complete fracturing process.

[0083] The expression for calculating the anomaly difference is as follows:

[0084] dX=(X2-X1) / (X2+X1)

[0085] Wherein, dX represents the data differential anomaly, X2 represents the data after fracturing, and X1 represents the data before fracturing. The data before fracturing includes the spontaneous potential method data, the induced polarization method data, the time-frequency electromagnetic method data, the controlled-source magnetotelluric apparent resistivity data, and the controlled-source magnetotelluric phase data before fracturing. The parameter data after fracturing includes the spontaneous potential method data, the induced polarization method data, the time-frequency electromagnetic method data, the controlled-source magnetotelluric apparent resistivity data, and the controlled-source magnetotelluric phase data after fracturing.

[0086] Following S6, the following is also included:

[0087] By using differential anomaly data during the complete fracturing process, characteristic data of the fracturing fluid during the complete fracturing process are obtained. Characteristic data of the fracture distribution morphology are then deduced from the characteristic data of the fracturing fluid. The characteristic data of the fracturing fluid includes volume, and the characteristic data of the fracture distribution morphology includes length, width, and height.

[0088] This invention uses a three-dimensional measurement network to collect electric field components parallel to the emission source and magnetic field components perpendicular to the emission source. It monitors electromagnetic anomaly data of fracturing fluid dynamics during the entire fracturing process, from before to after fracturing, and obtains characteristic data of the fracturing fluid during the entire fracturing process. From this, characteristic data of the fracture distribution morphology can be deduced, thus providing data for predicting fracturing channeling and fracture risk. Compared with traditional electrical monitoring methods that can only obtain single anomaly data, this invention obtains five types of anomaly data, thereby obtaining the fracturing fluid and fracture distribution morphology, reducing ambiguity and improving accuracy and monitoring precision.

[0089] Example 1

[0090] Step 1. Collect parameter data of the target well, including well logging data, well trajectory data, and geological data of the target area;

[0091] Step 2. Establish a geoelectric model of the target well area based on the parameter data of the target well, and select reasonable acquisition parameter data through forward modeling of the target well area: point distance 25m, survey line distance 85m, transmit / receive distance 4.5km, excitation electrode distance 4km, excitation frequency range 64-0.1Hz;

[0092] Step 3. According to the fracturing operation plan, a three-dimensional monitoring network is set up directly above the fracturing section of the target well to monitor the entire fracturing process; specifically as follows:

[0093] The measurement network is designed based on known fracturing sections and clusters, with a point spacing of 25m. The transmitter is laid out parallel to the measurement line, with a transmitter-receiver distance of 4.5km and an excitation electrode distance of 4km. The collected components are the electric field component parallel to the transmitter and the magnetic field component perpendicular to the transmitter, with an electrode distance of 25m.

[0094] Step 4. Half an hour before fracturing begins and during fracturing, launch at certain time intervals according to the frequencies in the frequency table. The frequencies in the launch frequency table are determined by selecting reasonable acquisition parameter data from the forward modeling results.

[0095] Step 5. Perform time-division extraction on the data collected in Step 4, extract the artificial field source data within the launch time period, and obtain the time-series data of the artificial source electric field and magnetic field before and after fracturing.

[0096] Step 6. Perform time-division extraction on the data collected in Step 4, extract the natural field source data during the non-emission period, obtain the time series data of the natural field source electric field before and after fracturing, select the period with less electromagnetic interference, calculate its average value, and obtain the natural potential data before and after fracturing, denoted as A1 and A2 respectively, where A1 represents the natural potential data before fracturing and A2 represents the natural potential data after fracturing.

[0097] Step 7. Perform DC drift correction, same frequency superposition processing, and electrode distance correction processing on the artificial source electric field time series data and magnetic field time series data obtained in Step 5 to obtain artificial source electric field data before and after fracturing.

[0098] Step 8. Perform a halving and superposition of the artificial source electric field time series data before and after fracturing as described in Step 7, then extract the long-period flat segment data, calculate its average value, and obtain the induced polarization method data before and after fracturing, denoted as B1 and B2, where B1 represents the induced polarization method data before fracturing and B2 represents the induced polarization method data after fracturing.

[0099] Step 9. Perform Fourier transform on the superimposed data from Step 7 and normalize the current to obtain frequency domain electric field and magnetic field data before and after fracturing.

[0100] The frequency domain electric field data are time-frequency electromagnetic data, denoted as C1 and C2, where C1 represents the time-frequency electromagnetic data before fracturing and C2 represents the time-frequency electromagnetic data after fracturing.

[0101] Step 10. Using the Carnia apparent resistivity formula, calculate the apparent resistivity and phase data of controlled source magnetotellurics (CMT) before and after fracturing based on the frequency domain electric field and magnetic field data obtained in Step 9. These are denoted as Dr1, Dr2, Dp1, and Dp2, where Dr1 represents the apparent resistivity data of controlled source magnetotellurics before fracturing, Dr2 represents the apparent resistivity data of controlled source magnetotellurics after fracturing, Dp1 represents the phase data of controlled source magnetotellurics before fracturing, and Dp2 represents the phase data of controlled source magnetotellurics after fracturing.

[0102] Step 11. Perform differential calculations on the natural potential method data, the induced polarization method data, the time-frequency electromagnetic method data, the controlled-source magnetotelluric apparent resistivity data, and the controlled-source magnetotelluric phase data before and after fracturing, respectively. The calculation formula is dX=(X2-X1) / (X2+X1), thereby obtaining the differential anomalies of the natural potential method, the induced polarization method, the time-frequency electromagnetic method, the controlled-source magnetotelluric apparent resistivity, and the controlled-source magnetotelluric phase difference anomalies.

[0103] Wherein, dX represents the data differential anomaly, X2 represents the data after fracturing, and X1 represents the data before fracturing. The data before fracturing includes the spontaneous potential method data, the induced polarization method data, the time-frequency electromagnetic method data, the controlled-source magnetotelluric apparent resistivity data, and the controlled-source magnetotelluric phase data before fracturing. The parameter data after fracturing includes the spontaneous potential method data, the induced polarization method data, the time-frequency electromagnetic method data, the controlled-source magnetotelluric apparent resistivity data, and the controlled-source magnetotelluric phase data after fracturing.

[0104] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0105] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0106] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A three-dimensional electromagnetic fracturing monitoring method, characterized in that, include: S1. Collect parameter data of the target well, including well logging data, well trajectory data, and geological data of the target area; S2. Establish a geoelectric model of the target well area based on the parameter data of the target well, and select reasonable acquisition parameter data through forward modeling of the target well area geoelectric model. The reasonable acquisition parameter data includes: excitation frequency, measuring line distance, point distance, transmit / receive distance, and excitation electrode distance in the transmission frequency table. S3. According to the target well fracturing construction plan, a three-dimensional measuring network is set up directly above the fracturing section of the target well to monitor the entire fracturing process; S4. During the fracturing monitoring process, according to the target well fracturing construction plan, the system transmits data at set time intervals according to the frequencies in the frequency table to collect natural and artificial field source data. S5. Extract the natural source data during the non-emission period to obtain the natural source electric field time series data before and after fracturing, and obtain the natural potential data before and after fracturing based on the natural source electric field time series data before and after fracturing. During the artificial field source emission period, the artificial field source data within the emission period is extracted to obtain the artificial source electric field time series data and magnetic field time series data before and after fracturing. The artificial source electric field time series data and magnetic field time series data before and after fracturing are processed separately to obtain the induced polarization method data before and after fracturing, the time-frequency electromagnetic method data before and after fracturing, the controlled source magnetotelluric method apparent resistivity data before and after fracturing, and the controlled source magnetotelluric method phase data before and after fracturing. S6. Based on the natural potential data before and after fracturing, the induced polarization data before and after fracturing, the time-frequency electromagnetic data before and after fracturing, and the apparent resistivity data and phase data of controlled-source magnetotelluric method before and after fracturing, differential anomaly data are calculated and obtained. All differential anomaly data are then comprehensively analyzed to obtain characteristic data of the distribution morphology of fracturing fluid and fractures during the complete fracturing process.

2. The three-dimensional electromagnetic fracturing monitoring method according to claim 1, characterized in that, In step S3, the deployment of the three-dimensional survey network includes: Design a survey network based on known fracturing sections and clusters; The point spacing, the transmit / receive distance, and the excitation electrode distance are designed based on the reasonable parameters.

3. The three-dimensional electromagnetic fracturing monitoring method according to claim 1, characterized in that, In step S4, the frequencies in the transmission frequency table are the transmission frequencies of artificial field sources.

4. The three-dimensional electromagnetic fracturing monitoring method according to claim 1, characterized in that, In step S5, obtaining the natural potential data before and after fracturing includes: During the non-emission period, the natural source data is extracted to obtain the time series data of the natural source electric field before and after fracturing. The data of the time series data of the natural source electric field before and after fracturing with a signal-to-noise ratio higher than a set value are selected, and their average value is calculated to obtain the natural potential data before and after fracturing.

5. The three-dimensional electromagnetic fracturing monitoring method according to claim 1, characterized in that, In step S5, obtaining the induced polarization method data before and after fracturing includes: The artificial source electric field time series data before and after fracturing are processed by DC drift correction, same-frequency halving superposition and electrode distance correction. Then, the long-period flat segment data is extracted and its average value is calculated to obtain the excitation polarization method data before and after fracturing. The long period refers to the period when the frequency is the lowest.

6. The three-dimensional electromagnetic fracturing monitoring method according to claim 1, characterized in that, In step S5, obtaining the time-frequency electromagnetic method data before and after fracturing includes: The artificial source electric field time series data and magnetic field time series data before and after fracturing are subjected to DC drift correction, halved superposition at the same frequency, Fourier transform and current normalization to obtain frequency domain electric field data and magnetic field data before and after fracturing. The frequency domain electric field data is time-frequency electromagnetic method data.

7. The three-dimensional electromagnetic fracturing monitoring method according to claim 1, characterized in that, In step S5, obtaining the controlled-source magnetotelluric apparent resistivity data and controlled-source magnetotelluric phase data before and after fracturing includes: Based on the frequency domain electric field and magnetic field data before and after fracturing, the controlled-source magnetotelluric apparent resistivity and controlled-source magnetotelluric phase data before and after fracturing are obtained by calculating according to the Carnia apparent resistivity formula.

8. The three-dimensional electromagnetic fracturing monitoring method according to claim 1, characterized in that, In step S5, obtaining the natural potential data before and after fracturing includes: during the non-emission period, intercepting the natural source data to obtain the time series data of the natural source electric field before and after fracturing; selecting data from the time series data of the natural source electric field before and after fracturing that is higher than the signal-to-noise ratio set value, and taking the average value to obtain the natural potential data before and after fracturing; the signal-to-noise ratio set range is higher than 30 dB.

9. The three-dimensional electromagnetic fracturing monitoring method according to claim 1, characterized in that, In step S6, the expression for calculating the differential outlier data is as follows: dX = (X2 - X1) / (X2 + X1) Wherein, dX represents the data differential anomaly, X2 represents the data after fracturing, and X1 represents the data before fracturing. The data before fracturing includes the spontaneous potential method data, the induced polarization method data, the time-frequency electromagnetic method data, the controlled-source magnetotelluric apparent resistivity data, and the controlled-source magnetotelluric phase data before fracturing. The parameter data after fracturing includes the spontaneous potential method data, the induced polarization method data, the time-frequency electromagnetic method data, the controlled-source magnetotelluric apparent resistivity data, and the controlled-source magnetotelluric phase data after fracturing.

10. The three-dimensional electromagnetic fracturing monitoring method according to claim 1, characterized in that, Following S6, the following is also included: By using differential anomaly data during the complete fracturing process, characteristic data of the fracturing fluid during the complete fracturing process are obtained. Characteristic data of the fracture distribution morphology are then deduced from the characteristic data of the fracturing fluid. The characteristic data of the fracturing fluid includes volume, and the characteristic data of the fracture distribution morphology includes length, width, and height.

Images