A fracture distribution determination system and method

Abstract

This invention discloses a fracture distribution determination system and method. The system includes: an electromagnetic detection device and a fracture distribution determination device; the electromagnetic detection device includes an electromagnetic field excitation source and an electromagnetic field receiver; the electromagnetic field excitation source is used to generate electromagnetic fields at different stages of fracturing in the fracturing layer; the electromagnetic field receiver is used to receive the induced electromagnetic fields generated by the interaction between the electromagnetic field generated by the electromagnetic field excitation source and resistivity anomalies in the fractures of the fracturing layer at different fracturing stages; the fracture distribution determination device is used to determine the conductivity data of the fracturing layer based on the induced electromagnetic fields received by the electromagnetic field receiver at different fracturing stages; and to determine the fracture distribution data of the fracturing layer based on the conductivity data. This method can more accurately describe the spatial distribution of fracturing fractures generated in hydraulically fracturing reservoirs, thereby optimizing fracturing operation parameters.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration engineering technology, and in particular to a system and method for determining fracture distribution. Background Technology

[0002] With the exploration and development of tight sandstone, igneous rock or shale oil and gas reservoirs, hydraulic fracturing technology has become an essential technology for the efficient development of such reservoirs. During the hydraulic fracturing process, it is necessary to evaluate and monitor the spatial distribution and propagation law of fractures in the fracturing reservoir in order to optimize fracturing construction parameters and improve the scale and effectiveness of fracturing, so as to achieve high and stable production of the fracturing reservoir.

[0003] Existing detection methods mainly utilize microseismic methods, time-lapse logging methods, and inter-well interference testing methods to provide semi-quantitative or quantitative descriptions and analyses of the distribution of fractures in fractured reservoirs. Summary of the Invention

[0004] The inventors of this application have discovered that when using microseismic methods to analyze the spatial distribution of fractures, the imaging of microseismic wave fields received in wells or on the surface is not accurate enough due to the complexity of the formation of microseismic wavelengths. Therefore, the fractures imaged by microseismic imaging cannot accurately reflect the actual distribution of hydraulic fracturing fractures in underground formations. When using time-lapse logging methods to analyze the spatial distribution of fractures, this method has high vertical resolution but small lateral coverage of a single well and small radial detection depth, which cannot accurately describe the spatial distribution of the entire fracture. The inter-well interference testing method requires additional boreholes, and the evaluation results are limited to the plane range formed by two boreholes, making it difficult to describe the complete spatial distribution.

[0005] In view of the above problems, the present invention is proposed to provide a crack distribution determination system and method that overcomes or at least partially solves the above problems.

[0006] This invention provides a crack distribution determination system, comprising: an electromagnetic detection device and a crack distribution determination device;

[0007] The electromagnetic detection device includes an electromagnetic field excitation source and an electromagnetic field receiver; the electromagnetic field excitation source is used to generate electromagnetic fields at different stages of fracturing in the fracturing layer.

[0008] The electromagnetic field receiver is used to receive the induced electromagnetic field generated by the interaction between the electromagnetic field generated by the electromagnetic field excitation source and the resistivity anomaly in the fracture layer at different fracturing stages.

[0009] The fracture distribution determination device is used to determine the electrical conductivity data of the fracturing layer based on the induced electromagnetic fields received by the electromagnetic field receiver at different fracturing stages; and to determine the fracture distribution data of the fracturing layer based on the electrical conductivity data.

[0010] In some optional embodiments, the electromagnetic field excitation source includes: an electromagnetic field signal generator and an electromagnetic field transmitting antenna;

[0011] The electromagnetic field signal generator is used to provide electromagnetic field energy for the electromagnetic field transmitting antenna.

[0012] The electromagnetic field transmitting antenna is used to generate an electromagnetic field based on the electromagnetic field energy provided by the electromagnetic field signal generator.

[0013] In some optional embodiments, the electromagnetic detection device further includes an electromagnetic signal amplifier and an electromagnetic synchronization device;

[0014] The electromagnetic signal amplifier is used to amplify the induced electromagnetic field received by the electromagnetic field receiver; the electromagnetic synchronization device is used to synchronize the excitation time of the electromagnetic field excitation source and the amplification time of the electromagnetic signal amplifier.

[0015] In some optional embodiments, the electromagnetic field receiver in the electromagnetic detection device includes at least one; if it includes multiple electromagnetic field receivers, the multiple electromagnetic field receivers are arranged in a ring network or a square network.

[0016] This invention also provides a method for determining crack distribution, including:

[0017] A generalized measure regularization method based on logging constraints is used to invert the collected electromagnetic field data to determine the conductivity data of the fracturing layer. The conductivity data includes conductivity data of the first fracturing stage and the second fracturing stage, where the first fracturing stage represents the stage before fracturing and the second fracturing stage represents the stage during or after fracturing. The electromagnetic field data is acquired based on an electromagnetic detection device.

[0018] The fracture distribution data of the fracturing layer is determined based on the electrical conductivity data of the fracturing layer.

[0019] In some optional embodiments, the step of inverting the acquired electromagnetic field data using a generalized measure regularization method based on logging constraints to determine the conductivity data of the fracturing layer includes:

[0020] The constructed inversion objective functional equation is discretized to obtain the partial derivative equation of the objective inversion with respect to the model; the inversion objective functional equation is constructed based on the collected electromagnetic field data;

[0021] The partial derivative equations are transformed into a matrix system, and the conductivity update models for the first fracturing stage and the second fracturing stage are determined based on the matrix system.

[0022] The conductivity of the first fracturing stage and the second fracturing stage are determined based on the preset initial value and the conductivity update model, respectively.

[0023] In some optional embodiments, the process of constructing the inverse objective functional equation includes:

[0024] The parameters of the inversion target functional equation are determined based on the collected electromagnetic field data. The parameters include the mathematical expression of the forward simulation of electromagnetic field data, the roughness of the model space reconstructed for electromagnetic field data, the diagonal matrix of the electromagnetic field data weight matrix, the regularization coefficient, the label of the data fitting error functional based on the generalized measure, and the model similarity constraint functional.

[0025] Based on the parameters and the selected generalized measure, the following inversion objective functional equation is constructed:

[0026]

[0027] Where m1 and m2 represent the electrical conductivity data of the first fracturing stage and the second fracturing stage, respectively;

[0028] m1 ref m2 ref These represent the measured reference conductivity data for the first and second fracturing stages, respectively.

[0029] These are the labels representing the data fitting error functionals based on the generalized measure for the first and second fracturing stages, respectively. λ1 and λ2 represent the model similarity constraint functionals based on generalized measure for the first fracturing stage and the second fracturing stage, respectively, and represent the regularization coefficients corresponding to the model parameters for the first fracturing stage and the second fracturing stage, respectively.

[0030] d obs1 ,d obs2 Let G(m1) and G(m2) represent the electromagnetic field data of the first and second fracturing stages, respectively. Let W represent the mathematical expressions of the forward simulations of the first and second fracturing stages, respectively. m1 W m2 W represents the model space roughness of the electromagnetic field data reconstruction for the first and second fracturing stages, respectively. d1 W d2 Let be a diagonal matrix representing the weights of the electromagnetic field data for the first fracturing stage and the second fracturing stage, respectively.

[0031] The generalized measure selects the following perturbation L p Norm:

[0032] Φ(m i )=(m i 2 +ε 2 ) p2 i = 1, 2

[0033] Where ε and p represent positive real numbers that control the inversion behavior.

[0034] In some optional embodiments, determining the fracture distribution data of the fracturing layer based on the electrical conductivity data of the fracturing layer includes:

[0035] The simulated electrical conductivity data of the fracturing layer were determined based on the simulated fracture distribution parameters using an equivalent medium model.

[0036] Under the condition that the error between the simulated conductivity data and the conductivity data determined based on inversion is minimized, the simulated crack distribution parameters corresponding to the simulated conductivity data are the crack distribution data.

[0037] In some optional embodiments, the simulated fracture distribution parameters are used to determine the simulated electrical conductivity data of the fracturing layer through an equivalent medium model, including:

[0038] The simulated electrical conductivity data were obtained based on the simulated fracturing fracture distribution parameters using the following equivalent medium model. The fracturing fracture distribution parameters include: the number of horizontal or vertical fractures per unit length, fracture aperture, extension width, fracture extension length, fracture inclination angle, electrical conductivity of fracturing fluid, and electrical conductivity before fracturing.

[0039]

[0040] in:

[0041]

[0042]

[0043] Where, σ D To simulate conductivity data, ρ f ρ is the resistivity of the fracturing fluid. r denoted as Resistivity before fracturing, n as the number of horizontal or vertical cracks per unit length, calculated by projecting the inclined cracks onto the plane and vertical plane, e as crack aperture, w as extension width, A as crack extension length, n as crack inclination angle, α as crack inclination angle, and β as crack azimuth angle.

[0044] In some optional embodiments, under the condition that the error between the simulated conductivity data and the conductivity data determined based on inversion is minimized, the simulated crack distribution parameters corresponding to the simulated conductivity data are crack distribution data, including:

[0045] Based on the simulated conductivity data and the conductivity data determined by inversion, the following formula is used to minimize the data error between the two:

[0046] Q = ||σ L -σ D || 2=min

[0047] Where Q is the sum of squared errors between the inverted conductivity data and the simulated conductivity data, and σ L For the conductivity data obtained from the inversion, σ D For simulated conductivity data;

[0048] If Q reaches the minimum value of the preset error range, then the fracture distribution parameters corresponding to the simulated conductivity data are the fracturing fracture distribution data.

[0049] This invention also provides a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the crack distribution determination method described above.

[0050] This invention also provides a terminal device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the crack distribution determination method as described above.

[0051] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the crack distribution determination method described above.

[0052] The beneficial effects of the above-mentioned technical solutions provided by the embodiments of the present invention include at least the following: This embodiment provides a fracture distribution determination system, including an electromagnetic detection device and a fracture distribution determination device; the electromagnetic detection device includes an electromagnetic field excitation source and an electromagnetic field receiver, the electromagnetic field excitation source generates an electromagnetic field at different stages of fracturing, and correspondingly, the electromagnetic field receiver receives the induced electromagnetic field generated by the interaction between the electromagnetic field generated by the electromagnetic field excitation source and the resistivity anomaly in the fracture layer at different stages of fracturing; the induced electromagnetic field received at different stages of fracturing provides a good data foundation for subsequent determination of the spatial distribution of fracturing fractures;

[0053] The fracture distribution determination device is used to invert electromagnetic field data received at different stages of fracturing to obtain the electrical conductivity data of the fracturing layer at different stages of fracturing. Based on the electrical conductivity data at different stages of fracturing, the fracture distribution data of the fracturing layer is determined. This method can more accurately describe the spatial distribution of fracturing fractures generated in hydraulically fracturing reservoirs, so as to optimize fracturing construction parameters and improve the scale and effectiveness of fracturing, thereby achieving high and stable production of fracturing reservoirs. It also expands the application of well-to-surface electromagnetic exploration methods in the development of unconventional oil and gas reservoirs.

[0054] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0055] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0056] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0057] Figure 1 This is a schematic diagram of the crack distribution determination system in Embodiment 1 of the present invention;

[0058] Figure 2 This is a schematic diagram of the ring-shaped electromagnetic detection device in Embodiment 1 of the present invention;

[0059] Figure 3 This is a schematic diagram of the square measuring network electromagnetic detection device in Embodiment 1 of the present invention;

[0060] Figure 4 This is a schematic diagram of the electromagnetic detection device for the ring-shaped measuring network during fracturing in Embodiment 1 of the present invention;

[0061] Figure 5 This is a schematic diagram of the square measuring network electromagnetic detection device in fracturing according to Embodiment 1 of the present invention;

[0062] Figure 6 This is a schematic diagram of the electromagnetic detection device for the ring-shaped measuring network after fracturing in Embodiment 1 of the present invention;

[0063] Figure 7 This is a schematic diagram of the electromagnetic detection device for the square measuring network after fracturing in Embodiment 1 of the present invention;

[0064] Figure 8 This is a flowchart of the crack distribution determination method in Embodiment 2 of the present invention;

[0065] Explanation of reference numerals in the attached figures:

[0066] 11. Electromagnetic field transmitting antenna; 12. Electromagnetic field signal generator; 13. Electromagnetic field synchronization device; 14. Electromagnetic signal amplifier; 15. Surface; 16. Electromagnetic field receiver; 17. Borehole fluid; 18. Casing; 19. Fracturing layer;

[0067] 1901. Gas-bearing fracture; 1902. Water-bearing or fracturing fluid-bearing fracture; 1903. Oil-bearing fracture. Detailed Implementation

[0068] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0069] To address the problem of accurately describing the spatial distribution of fracturing fractures in target reservoirs in existing technologies, embodiments of the present invention provide a fracture distribution determination system and method that can more accurately identify the fracture distribution in fracturing layers.

[0070] Example 1

[0071] This invention provides a crack distribution determination system, such as... Figure 1 As shown, it includes an electromagnetic detection device and a crack distribution determination device;

[0072] The electromagnetic detection device includes an electromagnetic field excitation source and an electromagnetic field receiver; the electromagnetic field excitation source is used to generate electromagnetic fields at different stages of fracturing in the fracturing layer; the electromagnetic field receiver is used to receive the induced electromagnetic fields generated by the interaction between the electromagnetic field generated by the electromagnetic field excitation source and the resistivity anomaly in the fracturing layer fracture at different stages of fracturing.

[0073] The fracture distribution determination device is used to determine the electrical conductivity data of the fracturing layer based on the induced electromagnetic fields received by the electromagnetic field receiver at different fracturing stages; and to determine the fracture distribution data of the fracturing layer based on the electrical conductivity data.

[0074] In some optional embodiments, the electromagnetic field excitation source includes: an electromagnetic field signal generator and an electromagnetic field transmitting antenna; the electromagnetic field signal generator is used to provide electromagnetic field energy to the electromagnetic field transmitting antenna; the electromagnetic field transmitting antenna is used to generate an electromagnetic field based on the electromagnetic field energy provided by the electromagnetic field signal generator.

[0075] Optionally, the electromagnetic detection device further includes an electromagnetic signal amplifier and an electromagnetic synchronization device; the electromagnetic signal amplifier is used to amplify the induced electromagnetic field received by the electromagnetic field receiver; the electromagnetic synchronization device is used to synchronize the excitation time of the electromagnetic field excitation source and the amplification time of the electromagnetic signal amplifier.

[0076] The electromagnetic field receiver in the electromagnetic detection device includes at least one; if it includes multiple electromagnetic field receivers, the multiple electromagnetic field receivers are arranged in a ring network or a square network.

[0077] like Figure 2 The diagram shown is a schematic of the electromagnetic detection device in this embodiment, including:

[0078] An electromagnetic field excitation source placed inside the target formation borehole;

[0079] The casing 18, installed in the wellbore section, is sealed to the target formation;

[0080] An electromagnetic field receiver 16, an electromagnetic signal amplifier 14, and an electromagnetic field synchronization device 13 are installed on the ground in the direction of the wellhead; the electromagnetic signal amplifier 14 is used to amplify the detected electromagnetic signal; the electromagnetic field receiver 16 is used to receive the electromagnetic signal amplified by the signal amplifier 14; the electromagnetic field synchronization device 13 is used to synchronize the emission time of the electromagnetic field excitation source and the amplification time of the electromagnetic signal amplifier 14.

[0081] Highly conductive fracturing fluid or highly conductive solid particles are used to fill the fractures in the fracturing layer 19 of the target formation to generate a low-resistivity anomaly that is sensitive to electromagnetic signals.

[0082] The electromagnetic field excitation source includes an electromagnetic field signal generator 12 and an electromagnetic field transmitting antenna 11; the electromagnetic field signal generator 12 is used to provide electromagnetic field energy to the electromagnetic field transmitting antenna 11; the electromagnetic field transmitting antenna 11 is used to generate an electromagnetic field based on the electromagnetic field energy provided by the electromagnetic field signal generator 12.

[0083] Optionally, at least one well is drilled in the target formation. The diameter of the well can be between 18 and 30 inches. This well serves as the transmitting well for the electromagnetic field excitation source. The electromagnetic signal generator 12 of the electromagnetic field excitation source is placed inside the well in the target formation to provide electromagnetic field energy to the electromagnetic field transmitting antenna 11. The electromagnetic field transmitting antenna 11 generates an electromagnetic field based on the electromagnetic field energy provided by the electromagnetic field signal generator 12. The electromagnetic signal generator 12 can be an electrical source or a magnetic source. The electromagnetic field it emits interacts with conductive anomalies in the target formation to generate a detectable induced electromagnetic field.

[0084] Optionally, at least a portion of the wellbore may be sealed with the target formation using casing 18 to support the wellbore during drilling, prevent the collapse of loose soil near the surface, and make the formation less prone to collapse.

[0085] Optionally, the number of electromagnetic field receivers 16 deployed in the direction of the wellhead is at least one. If multiple electromagnetic field receivers 16 are deployed, they can be arranged in a ring network with the wellhead as the center, such as... Figure 2 , Figure 4 , Figure 6 As shown; it can also be laid out as a square survey network, such as Figure 3 , Figure 5 , Figure 7 As shown, it is used to receive at least one electromagnetic field component generated by the interaction between the electromagnetic field emitted by the electromagnetic field excitation source and the strata.

[0086] like Figure 2 , Figure 3 As shown, the target formation schematically includes one or more overlying strata such as topsoil and aquifers, as well as a fractured layer 19, such as tight sandstone, shale, or igneous reservoirs. The fractured layer contains oil, natural gas, and formation water. During fracturing, the heterogeneous distribution of porosity and permeability of the fractured layer and the fractures leads to a non-uniform distribution of oil, natural gas, formation water, and injected fracturing fluid. The locations of oil and natural gas distribution form high-resistivity anomaly zones, while the locations of formation water and fracturing fluid distribution form low-resistivity anomaly zones. The low-resistivity anomaly zones where the fracturing fluid enters are easier to analyze for fracture distribution, such as... Figure 4 , Figure 5 As shown, during the fracturing process, gas-bearing fractures 1901, water-bearing or fracturing fluid-bearing fractures 1902, and oil-bearing fractures 1903 gradually appeared; after fracturing, more gas-bearing fractures 1901, water-bearing or fracturing fluid-bearing fractures 1902, and oil-bearing fractures 1903 appeared, such as... Figure 6 , Figure 7 As shown in the figure. Among them, gas-bearing fracture 1901 is represented by a yellow line, water-bearing or fracturing fluid-bearing fracture 1902 is represented by a blue line, and oil-bearing fracture 1903 is represented by a red line.

[0087] The working principle of the electromagnetic detection device is as follows: the electromagnetic field transmitting antenna 11 in the wellbore generates an electromagnetic field based on the electromagnetic field energy provided by the electromagnetic field signal generator 12 at preset time intervals, and the electromagnetic field generates an induced electromagnetic field after interacting with the target reservoir.

[0088] An electromagnetic field receiver 16, installed on the ground in the direction of the wellhead, receives the induced electromagnetic field of the underground formation. The induced electromagnetic field is the induced electromagnetic field generated by the interaction between the source electromagnetic field generated by the excitation and the heterogeneous conductive anomalies in the target reservoir, such as oil, natural gas, formation water and fracturing fluid.

[0089] In this embodiment, the electromagnetic detection device generates an electromagnetic field at different stages of fracturing, and the corresponding electromagnetic field receiver receives the induced electromagnetic field, providing a good data foundation for the subsequent fracture distribution determination device to determine the spatial distribution of fracturing fractures.

[0090] Example 2

[0091] This invention provides a method for determining fracture distribution. Based on the electromagnetic detection device in the fracture determination system of Embodiment 1, induced electromagnetic field data at different fracturing stages are collected. Optionally, the first fracturing stage is before fracturing, and the second fracturing stage is during or after fracturing. The fracture distribution is determined based on the electromagnetic field data of the first and second fracturing stages. The process is as follows: Figure 8 As shown, it includes:

[0092] Step S101: The collected electromagnetic field data is inverted using a generalized measure regularization method based on logging constraints to determine the electrical conductivity data of the fracturing layer. The electrical conductivity data includes the electrical conductivity data of the first fracturing stage and the second fracturing stage. The first fracturing stage represents the stage before fracturing, and the second fracturing stage represents the stage during or after fracturing.

[0093] In this step, the electromagnetic field data collected in the first and second fracturing stages are inverted to obtain the conductivity data for the first and second fracturing stages; specifically, this includes:

[0094] 1) Discretize the constructed inversion objective functional equation to obtain the partial derivative equation of the objective inversion with respect to the model; the inversion objective functional equation is constructed based on the collected electromagnetic field data;

[0095] The process of constructing the inverse objective functional equation includes:

[0096] The parameters of the inversion target functional equation are determined based on the collected electromagnetic field data. The parameters include the mathematical expression of the forward simulation of electromagnetic field data, the roughness of the model space reconstructed for electromagnetic field data, the diagonal matrix of the electromagnetic field data weight matrix, the regularization coefficient, the label of the data fitting error functional based on the generalized measure, and the model similarity constraint functional.

[0097] Based on the parameters and the selected generalized measure, the following inversion objective functional equation is constructed:

[0098]

[0099] Where m1 and m2 represent the electrical conductivity data of the first fracturing stage and the second fracturing stage, respectively;

[0100] m1 ref m2 ref These represent the measured reference conductivity data for the first and second fracturing stages, respectively.

[0101] These are the labels representing the data fitting error functionals based on the generalized measure for the first and second fracturing stages, respectively. λ1 and λ2 represent the model similarity constraint functionals based on generalized measure for the first fracturing stage and the second fracturing stage, respectively, and represent the regularization coefficients corresponding to the model parameters for the first fracturing stage and the second fracturing stage, respectively.

[0102] d obs1 ,d obs2 Let G(m1) and G(m2) represent the electromagnetic field data of the first and second fracturing stages, respectively. Let W represent the mathematical expressions of the forward simulations of the first and second fracturing stages, respectively. m1 W m2W represents the model space roughness of the electromagnetic field data reconstruction for the first and second fracturing stages, respectively. d1 W d2 Let be a diagonal matrix representing the weights of the electromagnetic field data for the first fracturing stage and the second fracturing stage, respectively.

[0103] The generalized measure selects the following perturbation L p Norm:

[0104] Φ(m i )=(m i 2 +ε 2 ) p2 ,i=1,2 (2)

[0105] Where ε and p represent positive real numbers that control the inversion behavior.

[0106] After the inversion objective function is constructed, the constructed inversion objective functional equation is discretized to obtain the partial derivative equation of the objective inversion with respect to the model. Specifically, the linearization inversion strategy is applied to expand the forward equation f(m) = d using a Taylor series and ignores the second-order and other higher-order terms. The equations are substituted into the inversion objective functional and the partial derivative equation of the objective inversion with respect to the model is obtained. As shown below, formulas (3) and (4) are the discretized expressions of formula (1), and formulas (5) and (6) are the discretized expressions of formula (2). Formulas (3) and (5) correspond to the first fracturing stage, and formulas (4) and (6) correspond to the second fracturing stage.

[0107]

[0108] Where, r di and r mi It is a diagonal matrix related to the Ekblom norm chosen by the generalized measure:

[0109]

[0110] 2) Transform the partial derivative equations into a matrix system, and determine the conductivity update models for the first fracturing stage and the second fracturing stage based on the matrix system;

[0111] For complex nonlinear transformation relationships, it is necessary to preset initial quantities and determine the update quantity for each iteration, and obtain the final data based on the iteration method; specifically, in order to obtain a more accurate solution, the following matrix system (7) is obtained based on the transformation of formulas (3), (4), (5) and (6):

[0112]

[0113] Where, Δm j k(j=1,2) represent the updated models of conductivity in the first fracturing stage and the second fracturing stage, respectively, for the kth iteration.

[0114] 3) Determine the conductivity of the first fracturing stage and the second fracturing stage based on the preset initial quantity and conductivity update model.

[0115] Based on the preset initial values ​​and conductivity update model, the conductivity of the first fracturing stage and the second fracturing stage is determined using the following relationship:

[0116] m j k+1 =m j k +Δm j k (j=1,2) (8)

[0117] Where j=1 represents the first fracturing stage and j=2 represents the second fracturing stage;

[0118] m j k Let Δm represent the initial conductivity value in the k-th iteration. j k m represents the conductivity update in the k-th iteration. j k+1 This represents the conductivity obtained in the k-th iteration;

[0119] Repeat the above formula (8) until the final conductivity is obtained through iteration.

[0120] Step S102: Determine the fracture distribution data of the fracturing layer based on the electrical conductivity data of the fracturing layer.

[0121] Using an initial conductivity distribution model that closely resembles the underground strata properties of the actual fractured layer, the fracture distribution parameters of the model are adjusted through appropriate mathematical algorithms, such as the steepest descent iteration algorithm, until the conductivity obtained from the initial conductivity distribution model and the conductivity obtained from the inversion based on the collected electromagnetic data are within a certain allowable error range. In this case, the fracture distribution parameters corresponding to the initial conductivity distribution model are close to the fracture distribution space of the actual fractured layer.

[0122] An initial model for the electrical conductivity distribution can be an imaging model, such as a three-dimensional pixelation model, which divides the formation into pixels with physical property parameters such as electrical conductivity or magnetic permeability constants. It can be assumed that the initial attribute parameter distribution is consistent with the extension or interpolation distribution of well logging or other data. The attribute parameters of the pixels are adjusted until they fit well with the calculated electrical conductivity data.

[0123] For initial models of conductivity distribution in complex reservoirs, under conditions of strong heterogeneity and anisotropy, rectangular pixelated models exhibit relatively poor accuracy in characterizing the reservoir medium. However, equivalent medium theory can provide the average distribution of physical parameters within each pixel, equivalent to the parameters of the complex medium. Within each pixel, equivalent medium theory provides attribute values ​​and anisotropy to give the equivalent electromagnetic response of the complex medium. The final model may not necessarily correspond to the true conductivity distribution of the complex medium, but it provides an equivalent electromagnetic response for the complex medium parameter model. More specifically, the results of this equivalence process will provide the parameter distribution, anisotropic parameters, and overall scale of the resistivity or permeability anomaly support region through pixel parameter inversion. In this embodiment, the conductivity response of the fracturing layer is reconstructed using an equivalent medium model.

[0124] Specifically, based on the simulated fracture distribution parameters, simulated electrical conductivity data is obtained through an equivalent medium model, which is then reasonably fitted with the electrical conductivity data obtained by inversion in step S101. Thus, the fracture distribution parameters simulated in the equivalent medium theory are the fracture distribution data of the fracturing layer.

[0125] 1) Based on the simulated fracture distribution parameters, the simulated electrical conductivity data of the fracturing layer is determined through an equivalent medium model;

[0126] The simulated electrical conductivity data were obtained based on the simulated fracturing fracture distribution parameters using the following equivalent medium model. The fracturing fracture distribution parameters include: the number of horizontal or vertical fractures per unit length, fracture aperture, extension width, fracture extension length, fracture inclination angle, electrical conductivity of fracturing fluid, and electrical conductivity before fracturing.

[0127]

[0128] in:

[0129]

[0130]

[0131] Where, σ D To simulate conductivity data, ρ f ρ is the resistivity of the fracturing fluid. r denoted as Resistivity of the reservoir before fracturing, n as the number of horizontal or vertical fractures per unit length, calculated by projecting the inclined fractures onto the plane and vertical plane, e as fracture aperture, w as extension width, A as fracture extension length, n as fracture dip angle, α as fracture dip angle, and β as fracture azimuth angle.

[0132] 2) Under the condition that the error between the simulated conductivity data and the conductivity data determined by inversion is minimized, the simulated crack distribution parameters corresponding to the simulated conductivity data are the crack distribution data;

[0133] Based on the simulated conductivity data obtained from the equivalent medium theory and the conductivity data determined by inversion in step S101, the following formula is used to minimize the data error between the two:

[0134] Q = ||σ L -σ D || 2 =min

[0135] Where Q is the sum of squared errors between the inverted conductivity data and the simulated conductivity data, and σ L For the conductivity data obtained from the inversion, σ D For simulated conductivity data;

[0136] If Q reaches the minimum value of the preset error range, then the fracture distribution parameters corresponding to the simulated electrical conductivity data are the fracturing fracture distribution data.

[0137] To minimize the sum of squared errors Q, according to the optimization theory of quadratic forms, we know that:

[0138]

[0139] From a mathematical perspective, the conductivity is obtained by inverting the collected electromagnetic data, and the crack distribution data is further determined. This process can only obtain the least squares solution of known constraints under multiple constraints and regularization conditions.

[0140] Regularization can eliminate solutions with drastic changes in adjacent parameters by adding a smoothing constraint to the data fitting process. Let the theoretical response data vector obtained from forward simulation be σ. D The model parameter vector used is Δσ ij When solving inversion problems, this type of vector representation is also used. Therefore, in the forward modeling of the conductivity response, we discretize the model into N rectangular grids and assume that the conductivity within each grid is constant. The inversion process involves finding a set of parameter vectors m. i ={Δσ i1 ,Δσ i2 ,…,Δσ in}, m={m1,m2,…,m k} T The resistivity response generated using this parameter vector can appropriately fit the conductivity data σ. L Even if the conductivity response obtained from forward modeling based on the equivalent medium model differs from the observed conductivity data σ L ={σ L1 ,σ L2 ,…,σ Lm The squared error of the fit was reduced to an acceptable level.

[0141] In mathematical terms, the least squares inversion process minimizes the following error functional to obtain a smoothed model parameter result:

[0142] Φ=||W D (AΔm-Δσ D )|| 2 +μ||W m (mm b )|| 2 +λ||Cm||

[0143] In the formula: A is the sensitivity of the electromagnetic response to water saturation, Δσ D Electromagnetic data inversion conductivity data σ L And simulation calculation of conductivity response σ D The difference, m is the conductivity model, Δm is the correction amount of the conductivity model m in the iteration, m b C is the parameter vector of the background model or reference model, and C is the second-order difference operator that considers the smoothness of the model in adjacent blocks.

[0144] W D and W m These are the data weighting matrix and the parameter weighting matrix, W. D It is a diagonal matrix whose elements are the reciprocals of conductivity, assuming the data have the same standard deviation; W m When model parameters are constrained, W is an operator used to control the degree of similarity to the background model or initial model parameters. If all model parameters have the same weight, W... m It will degenerate into an identity matrix;

[0145] The parameter μ is a Lagrange multiplier used to control the effect of the smoothing constraint, and α is a coefficient that determines the degree of closeness to the reference model.

[0146] In determining the fracture distribution based on the equivalent medium model, it is assumed that only fractures containing formation water or fracturing fluid are present, and oil or gas-bearing fractures are not considered. In this formula, A represents the sensitivity of the electromagnetic response to water saturation, which is equivalent to correcting the contribution of oil or gas-bearing fractures to the electromagnetic response, making the inverted fracture distribution more accurate.

[0147] Before execution, the method in this embodiment performs a detectability analysis of the low-resistivity anomaly zone between the wellbore and the surface: a model of the actual reservoir type is designed, and a resistivity anomaly is set between the well and the surface, consisting of a high-resistivity body rich in oil and gas and a low-resistivity body with fracturing fractures containing well-conducting fluids and / or well-conducting solid particles. This resistivity anomaly differs from the reservoir background resistivity. The influence of changes in the geometric dimensions and electrical parameters of the resistivity anomaly on the electromagnetic signal is calculated using a numerical simulation method of well-to-surface electromagnetic response. Subsequently, with the application of a certain amount of random noise, the location and parameter distribution of the electromagnetically sensitive resistivity anomaly are described using well-to-surface electromagnetic detection data. Based on the described electromagnetic detection data, the fracture distribution is analyzed and studied.

[0148] To better apply the method of determining fracture distribution based on electromagnetic detection data, numerical simulation, core analysis, and well logging interpretation are used to verify the method. For example, in this embodiment, the fluid saturation result of determining fracture distribution based on electromagnetic detection data is calibrated and verified based on the fluid saturation result calculated using partial well logging data. The conductivity obtained by inverting electromagnetic data is used to establish a statistical relationship between the conductivity and the fluid saturation obtained by well logging or core analysis. This relationship is then used to establish an objective function for predicting inter-well fluid saturation, ensuring that the relative error between the calculated final fluid saturation distribution and the well logging calculation result is within 10%, which basically meets the practical requirements. This verifies the feasibility of the method and proves its reliability.

[0149] In this embodiment, electromagnetic data collected at different fracturing stages are inverted to obtain the electrical conductivity at different fracturing stages. Based on the simulated fracture distribution parameters, simulated electrical conductivity data is obtained through an equivalent medium model, minimizing the error between the simulated electrical conductivity data and the inverted electrical conductivity data. When the error reaches the minimum within a preset range, the simulated fracture distribution parameters are the fracture spatial distribution data of the fracturing layer. At the same time, oil-bearing fractures are considered to correct the fracture distribution data, which can more accurately describe the fracture spatial distribution state in the fracturing layer.

[0150] The method described above in this invention more accurately describes the spatial distribution of fractures in the fracturing layer and solves the problems existing in the evaluation and monitoring of fractures generated by hydraulic fracturing.

[0151] This invention also provides a computer storage medium, characterized in that the computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the crack distribution determination method described above.

[0152] This invention also provides a terminal device, characterized in that it includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the crack distribution determination method as described above.

[0153] This invention also provides a computer program product, characterized in that the computer program product includes a computer program, which, when executed by a processor, implements the crack distribution determination method as described above.

[0154] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0155] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0156] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.

[0157] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0158] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0159] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0160] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term "comprising" as used in the specification or claims is interpreted in a manner similar to the term "including," as interpreted when used as a conjunction in the claims. Additionally, the use of any term "or" in the specification of the claims is intended to mean "non-exclusive or."

Claims

1. A crack distribution determination system, characterized in that, include: Electromagnetic detection device and crack distribution determination device; The electromagnetic detection device includes an electromagnetic field excitation source and an electromagnetic field receiver. The electromagnetic field excitation source is used to generate an electromagnetic field at different stages of fracturing in the fracturing layer. The electromagnetic field receiver is used to receive the induced electromagnetic field generated by the interaction between the electromagnetic field generated by the electromagnetic field excitation source and the resistivity anomaly in the fracture layer at different fracturing stages. The fracture distribution determination device is used to determine the conductivity data of the fractured layer based on the induced electromagnetic fields received by the electromagnetic field receiver at different fracturing stages. The fracture distribution data of the fracturing layer is determined based on the electrical conductivity data.

2. The crack distribution determination system as described in claim 1, characterized in that, The electromagnetic field excitation source includes: an electromagnetic field signal generator and an electromagnetic field transmitting antenna; The electromagnetic field signal generator is used to provide electromagnetic field energy for the electromagnetic field transmitting antenna. The electromagnetic field transmitting antenna is used to generate an electromagnetic field based on the electromagnetic field energy provided by the electromagnetic field signal generator.

3. The crack distribution determination system as described in claim 1, characterized in that, The electromagnetic detection device also includes an electromagnetic signal amplifier and an electromagnetic synchronization device; The electromagnetic signal amplifier is used to amplify the induced electromagnetic field received by the electromagnetic field receiver; the electromagnetic synchronization device is used to synchronize the excitation time of the electromagnetic field excitation source and the amplification time of the electromagnetic signal amplifier.

4. The crack distribution determination system as described in claim 1, characterized in that, The electromagnetic field receiver in the electromagnetic detection device includes at least one; if it includes multiple electromagnetic field receivers, the multiple electromagnetic field receivers are arranged in a ring network or a square network.

5. A method for determining crack distribution, characterized in that, include: The collected electromagnetic field data is inverted using a generalized measure regularization method based on well logging constraints to determine the electrical conductivity data of the fracturing layer. The electrical conductivity data includes electrical conductivity data for a first fracturing stage and a second fracturing stage, where the first fracturing stage represents the stage before fracturing and the second fracturing stage represents the stage during or after fracturing. The electromagnetic field data is acquired using the electromagnetic detection device described in claims 1-4. The fracture distribution data of the fracturing layer is determined based on the electrical conductivity data of the fracturing layer.

6. The method as described in claim 5, characterized in that, The method employs a generalized measure regularization method based on well logging constraints to invert the acquired electromagnetic field data and determine the conductivity data of the fracturing layer, including: The constructed inversion objective functional equation is discretized to obtain the partial derivative equation of the objective inversion with respect to the model; the inversion objective functional equation is constructed based on the collected electromagnetic field data; The partial derivative equations are transformed into a matrix system, and the conductivity update models for the first fracturing stage and the second fracturing stage are determined based on the matrix system. The conductivity of the first fracturing stage and the second fracturing stage are determined based on the preset initial value and the conductivity update model, respectively.

7. The method as described in claim 6, characterized in that, The process of constructing the inverse objective functional equation includes: The parameters of the inversion target functional equation are determined based on the collected electromagnetic field data. The parameters include the mathematical expression of the forward simulation of electromagnetic field data, the roughness of the model space reconstructed for electromagnetic field data, the diagonal matrix of the electromagnetic field data weight matrix, the regularization coefficient, the label of the data fitting error functional based on the generalized measure, and the model similarity constraint functional. Based on the parameters and the selected generalized measure, the following inversion objective functional equation is constructed: Where m1 and m2 represent the electrical conductivity data of the first fracturing stage and the second fracturing stage, respectively; m1 ref m2 ref These represent the measured reference conductivity data for the first and second fracturing stages, respectively. These are the labels representing the data fitting error functionals based on the generalized measure for the first and second fracturing stages, respectively. λ1 and λ2 represent the model similarity constraint functionals based on generalized measure for the first fracturing stage and the second fracturing stage, respectively, and represent the regularization coefficients corresponding to the model parameters for the first fracturing stage and the second fracturing stage, respectively. d obs1 ,d obs2 Let G(m1) and G(m2) represent the electromagnetic field data of the first and second fracturing stages, respectively. Let W represent the mathematical expressions of the forward simulations of the first and second fracturing stages, respectively. m1 W m2 W represents the model space roughness of the electromagnetic field data reconstruction for the first and second fracturing stages, respectively. d1 W d2 Let be a diagonal matrix representing the weights of the electromagnetic field data for the first fracturing stage and the second fracturing stage, respectively. The generalized measure selects the following perturbation L p Norm: Φ(m i )=(m i 2 +e 2 ) p2 ,i=1.2 Where ε and p represent positive real numbers that control the inversion behavior.

8. The method as described in claim 5, characterized in that, The determination of fracture distribution data of the fracturing layer based on the electrical conductivity data of the fracturing layer includes: The simulated electrical conductivity data of the fracturing layer were determined based on the simulated fracture distribution parameters using an equivalent medium model. Under the condition that the error between the simulated conductivity data and the conductivity data determined based on inversion is minimized, the simulated crack distribution parameters corresponding to the simulated conductivity data are the crack distribution data.

9. The method as described in claim 8, characterized in that, The simulated fracture distribution parameters are determined using an equivalent medium model to obtain the simulated electrical conductivity data of the fracturing layer, including: The simulated electrical conductivity data were obtained based on the simulated fracturing fracture distribution parameters using the following equivalent medium model. The fracturing fracture distribution parameters include: the number of horizontal or vertical fractures per unit length, fracture aperture, extension width, fracture extension length, fracture inclination angle, electrical conductivity of fracturing fluid, and electrical conductivity before fracturing. in: Where, σ D To simulate conductivity data, ρ f ρ is the resistivity of the fracturing fluid. r denoted as Resistivity before fracturing, n as the number of horizontal or vertical cracks per unit length, calculated by projecting the inclined cracks onto the plane and vertical plane, e as crack aperture, w as extension width, A as crack extension length, n as crack inclination angle, α as crack inclination angle, and β as crack azimuth angle.

10. The method as described in claim 8, characterized in that, Under the condition that the error between the simulated conductivity data and the conductivity data determined based on inversion is minimized, the simulated crack distribution parameters corresponding to the simulated conductivity data are the crack distribution data, including: Based on the simulated conductivity data and the conductivity data determined by inversion, the following formula is used to minimize the data error between the two: Q=||σ L -s D || 2 =min Where Q is the sum of squared errors between the inverted conductivity data and the simulated conductivity data, and σ L For the conductivity data obtained from the inversion, σ D For simulated conductivity data; If Q reaches the minimum value of the preset error range, then the crack distribution parameters corresponding to the simulated conductivity data are the crack distribution data.

11. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the crack distribution determination method according to any one of claims 5-10.

12. A terminal device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the crack distribution determination method according to any one of claims 5-10.

13. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the crack distribution determination method according to any one of claims 5-10.

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