Jointed adjacent well fiber strain and construction pressure fracturing fracture parameter diagnosis method

Abstract

This invention provides a method for diagnosing fracturing fracture parameters by combining adjacent well fiber optic strain and construction pressure. The method includes: deploying distributed fiber optic sensors in the adjacent wellbore to monitor strain signals; deploying pressure sensors in the fracturing well to monitor pressure changes; comparing and processing fiber optic strain data and pressure data on the same time axis to identify the correlation between pressure fluctuations and fiber optic strain, and identifying the strain signal at the fracture front; establishing a strain-fracture geometry parameter inversion model by combining fiber optic strain and pressure fluctuations to obtain fracture geometry parameters at different times; verifying the accuracy of the strain-fracture geometry parameter inversion model; substituting the fracture parameters obtained from the strain-fracture geometry parameter inversion model into the Barree conductivity model to obtain the fracture conductivity; and obtaining the three-dimensional geometric morphology and dynamic change process of the fracture based on the obtained fracture geometry parameters and fracture conductivity. This invention can obtain more accurate fracture parameters.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, specifically to a method for diagnosing fracturing parameters by combining adjacent well fiber optic strain and construction pressure. Background Technology

[0002] Hydraulic fracturing technology is a key production enhancement method in oil and gas field development, playing a crucial role, especially in the development of unconventional reservoirs such as shale gas and tight sandstone gas. By injecting high-pressure fluids into the formation, hydraulic fracturing can create fractures in the reservoir, increasing the channels for oil and gas flow, thereby improving the conductivity between the wellbore and the reservoir, and ultimately increasing production. However, the geometry, propagation direction, and complexity of the fracture network play a vital role in the fracturing effect. Therefore, accurate diagnosis and real-time monitoring of fracture geometry parameters are essential for optimizing fracturing design and operational decisions.

[0003] In actual fracturing operations, microseismic monitoring, fracturing fluid volume analysis, and bottom hole pressure variation are commonly used to analyze fracture geometry. However, these methods have certain limitations. For example, microseismic monitoring can only provide the macroscopic location of fractures, but it is difficult to obtain detailed geometric information (such as fracture width and height), and bottom hole pressure analysis is often limited by the assumptions of fluid dynamics models. Therefore, traditional monitoring technologies face certain challenges in comprehensively and accurately revealing the real-time dynamics and propagation morphology of fractures, especially in complex multi-cluster and multi-layer fracture networks, where fine diagnosis is even more difficult.

[0004] In recent years, fiber optic distributed sensing technology (DSS / DAS) has attracted widespread attention as a fracture monitoring tool. This technology can capture strain and acoustic data near the wellbore in real time. Especially in fracturing operations, the application of adjacent-well fiber optic sensors can provide information about fracture geometry by sensing strain changes during fracture propagation. The high sensitivity of fiber optic strain data to the fracture front makes it more accurate than traditional monitoring methods during fracturing. Particularly before the fracture reaches the fiber, analyzing fiber strain changes can identify the speed and direction of fracture propagation. However, interpreting fracture parameters based on a single fiber strain signal still presents challenges. Fracture complexity, wellbore interference, and environmental noise can introduce noise signals, affecting the accuracy of strain data. Especially during fracture propagation, effectively extracting the relevant signals related to the fracture front remains a major technical challenge. Traditional fiber optic data analysis methods mainly focus on strain changes after the fracture contacts the fiber; however, inferring fracture geometry from strain data before the fracture reaches the fiber remains a problem to be solved. Therefore, providing a fracturing fracture parameter diagnosis method that combines adjacent-well fiber optic strain and operational pressure is of great significance. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to solve one or more problems existing in the prior art. For example, one objective of this invention is to solve the technical problem that relying on a single optical fiber strain signal for crack parameter interpretation cannot accurately diagnose crack geometric parameters.

[0006] To achieve the above objectives, the present invention provides a method for diagnosing fracturing fracture parameters by combining adjacent well fiber optic strain and construction pressure, the method comprising:

[0007] Distributed fiber optic sensors are deployed in adjacent wellbores to monitor strain signals generated during fracturing.

[0008] Pressure sensors are deployed in the fracturing well to monitor changes in bottom hole pressure at different stages of the construction process;

[0009] The obtained fiber strain data and pressure data are compared and processed on the same time axis to identify the correlation between pressure fluctuations and fiber strain changes, identify the crack propagation path and dynamic behavior, and identify the strain signal at the crack front.

[0010] By combining fiber strain and pressure fluctuation, an inversion model of strain-crack geometry parameters is established to obtain the geometric parameters of the crack at different times and to explain the hydraulic fracturing crack.

[0011] To verify the accuracy of the strain-crack geometry parameter inversion model, the crack parameters obtained from the strain-crack geometry parameter inversion model were substituted into the Barree conductivity model to obtain the conductivity of the crack.

[0012] Based on the obtained geometric parameters and conductivity of the cracks, the three-dimensional geometric morphology and dynamic change process of the cracks are obtained.

[0013] According to one or more exemplary embodiments of one aspect of the present invention, the inversion model of the strain-crack geometry parameters may include:

[0014] min

[0015] st 0<L≤S w

[0016] 0<H≤H max

[0017] 0 < w 0 < w max

[0018] 0≤k≤1

[0019] In the formula, |||2 is the 2-norm; ε f The strain is obtained from fiber strain calculations and is dimensionless; εm The strain of the optical fiber being monitored is dimensionless; z f Here, S represents the axial coordinate position of the optical fiber, in meters (m); w H represents the horizontal well spacing, in meters (m). max For maximum seam height, m; w max is the maximum seam width, mm; L is the half seam length, m; H is the half seam height, m; w0 is the seam opening width, mm; k is a power exponent, dimensionless; when k = 0, it is a toughness-dominated crack; when k = 1 / 3, it is a viscosity- and storage-dominated crack; when k = 3 / 8, it is a viscosity- and filtration-dominated crack.

[0020] According to one or more exemplary embodiments of one aspect of the present invention, the fiber strain calculation may include:

[0021]

[0022] In the formula: u zz L represents the axial displacement of the optical fiber, in meters (m). g ε is the gauge length, in meters; f denoted as axial strain of the optical fiber, which is dimensionless; z is the axial coordinate, in meters.

[0023] According to one or more exemplary embodiments of one aspect of the present invention, the inversion model of strain-crack geometry parameters may include setting the crack morphology as a rectangular crack and the crack height as a constant value.

[0024] According to one or more exemplary embodiments of one aspect of the present invention, verifying the accuracy of the inversion model of strain-fracture geometry parameters may include: using fiber optic monitoring of the fracture width at the well site under constant bottom net pressure, and comparing it with the fracture width of the inversion model of strain-fracture geometry parameters to verify the accuracy of the inversion model of strain-fracture geometry parameters.

[0025] According to one or more exemplary embodiments of the present invention, the calculation of the fracture width at the fiber optic monitoring well under constant bottom net pressure can be performed as follows:

[0026]

[0027] In the formula, W(x) is the width of the crack at a distance x from the crack center, in mm; x is the distance from the center point, in m; E is Young's modulus, in GPa; v is Poisson's ratio, dimensionless; P is the net pressure, in MPa; and L is the half-crack length, in m.

[0028] According to one or more exemplary embodiments of one aspect of the present invention, the Barree flow capability model may be as follows:

[0029]

[0030] In the formula, Km Let D be the filling permeability under a given stress; K0 be the initial permeability; and σ be the σ value. C The characteristic transition stress, psi, represents the rapid change in permeability with stress; σ is the closure stress, psi; k A w A , represents the flow conductivity, in md·ft; w represents the distribution pattern of the crack width, in mm; A and B are regression coefficients, dimensionless.

[0031] According to one or more exemplary embodiments of the present invention, the regression coefficients A and B can be obtained by fitting experimental data on the conductivity of the target reservoir.

[0032] According to one or more exemplary embodiments of one aspect of the present invention, the identification of the strain signal at the crack front may include: determining the moment when the crack hits the fiber well when the pressure fluctuation is large and the fiber strain signal shows a cardioid convergence phenomenon, and distinguishing the crack front signal from other interference signals.

[0033] According to one or more exemplary embodiments of one aspect of the present invention, the different stages may include a pumping stage and a pump shutdown stage; the monitoring data of the pressure sensor may include pumping pressure, net pressure, and pressure response curve after pump shutdown.

[0034] Compared with the prior art, the beneficial effects of the present invention include at least one of the following:

[0035] (1) This invention combines construction pressure data with fiber optic strain signals from adjacent wells, which can more effectively identify strain signals at the fracture front, thereby providing a more accurate interpretation of fracture geometric parameters.

[0036] (2) The method of the present invention improves the accuracy of interpreting fracture geometry parameters, especially in real-time monitoring and diagnosis of fracture length, width and height, providing reliable technical support, thereby effectively optimizing fracturing design and production decisions.

[0037] (3) This invention obtains the crack width at the crack collision point in real time through the accurate inversion of fiber strain data. Combined with the net pressure data obtained during construction, it can dynamically and accurately reflect the crack expansion behavior. Compared with the traditional method that relies on a single data source, the combination of multi-source information enables the crack geometric parameters (such as crack width and crack conductivity) to be calculated more accurately.

[0038] (4) The calculation of fracture geometry parameters and conductivity in this invention provides important reference for subsequent construction design, proppant selection and sand discharge design, which can guide the optimization of on-site fracturing construction, improve fracturing effect and reduce construction risk. Attached Figure Description

[0039] The above and other objects and features of the present invention will become clearer from the following description taken in conjunction with the accompanying drawings, in which:

[0040] Figure 1 A geometrical schematic diagram of the crack propagation-induced fiber strain model of the present invention is shown;

[0041] Figure 2 A schematic diagram of the process for the joint inversion of fiber strain and pressure fluctuation of the present invention is shown. Detailed Implementation

[0042] The following description, in conjunction with the accompanying drawings and exemplary embodiments, details the fracturing fracture parameter diagnosis method based on the combined adjacent-well fiber strain and construction pressure of the present invention.

[0043] Exemplary Example 1

[0044] This exemplary embodiment provides a method for diagnosing fracturing parameters by combining adjacent well fiber strain and construction pressure.

[0045] Bottomhole pressure variations during fracturing operations can reveal the hydrodynamic characteristics of fracture propagation. By analyzing pressure curves during fracturing, the conductivity and net pressure of the fracture can be inferred, and the propagation dynamics during pumping and shutdown can be further revealed. Therefore, combining fracturing pressure data with fiber optic strain signals from adjacent wells can more effectively identify strain signals at the fracture front, thus providing a more accurate interpretation of fracture geometry parameters.

[0046] The method for diagnosing fracturing fracture parameters by combining adjacent well fiber strain and construction pressure may include the following:

[0047] Step 1: Deploy distributed fiber optic sensors within the wellbore adjacent to the fractured well to monitor low-frequency (0.05–0.1 Hz) strain signals generated during fracture propagation during fracturing. The fiber optic strain sensors are deployed inside the wellbore, close to the well wall, to ensure high-precision strain data acquisition.

[0048] Step 2: Install pressure sensors in the fracturing well to monitor changes in bottom hole pressure in real time and record pressure changes at different stages of the fracturing process (including the pumping stage and the pump shutdown stage).

[0049] Step 3: Synchronously process the acquired fiber optic strain and pressure data, and compare them using the time axis as a reference. Through time series analysis, plot the acquired signals (fiber optic strain and pressure fluctuation data) according to the time series to capture the correlation between pressure fluctuations and strain changes, and identify the crack propagation path and dynamic behavior.

[0050] Step 4: Identify the strain signal at the crack front by observing fluctuations in construction pressure and changes in the fiber optic strain signal. Especially when pressure fluctuations are large and the fiber optic strain signal exhibits a cardioid convergence phenomenon, the moment when the crack encounters the fiber optic well can be determined, distinguishing the crack front signal from other interference signals.

[0051] Step 5: Use the fiber optic strain data inversion model (strain-crack geometry parameter inversion model) to obtain the geometric parameters of the crack at different times and interpret the hydraulic fracturing crack.

[0052] Step 6: Calculate the flow conductivity of the crack through joint analysis of construction pressure and fiber optic strain. Based on the crack geometric parameters and flow conductivity obtained from the joint analysis of fiber optic strain and construction pressure data, the three-dimensional geometric morphology and dynamic change process of the crack are finally output.

[0053] In this exemplary embodiment, step 1 may include: deploying distributed fiber optic sensors in the adjacent wellbore to monitor strain signals generated during fracturing in real time. The distributed fiber optic sensors may employ DSS (Distributed Strain Sensing) technology. The optical fibers are deployed along the entire length of the adjacent wellbore to ensure the capture of strain changes at various locations during fracture propagation. The optical fibers are deployed close to the wellbore wall to improve the sensitivity of the strain signals, especially to detect strain changes as the fracture propagates to the vicinity of the adjacent well. Fracture propagation simulation can verify the rationality of the fiber optic deployment by pre-calculating the fracture front position.

[0054] In this exemplary embodiment, step 2 may include: deploying pressure sensors in the fracturing well to monitor bottom hole pressure changes in real time at different stages of the fracturing process. The pressure sensors are deployed in the bottom hole region to record pressure fluctuation data during fracturing fluid injection, especially when the fracture extends to the vicinity of an adjacent well; the captured pressure changes will provide important data for subsequent fracture propagation behavior analysis. Pressure monitoring data includes pump injection pressure, net pressure, and pressure response curves after pump shutdown. Here, the pressure curves can be obtained through numerical simulation of fracture propagation.

[0055] In this exemplary embodiment, steps 3 and 4 may include: synchronously acquiring fiber optic strain data and pressure data, and comparing and processing the two types of data on the same time axis. Through time-series analysis, the correlation between pressure fluctuations and fiber optic strain changes is identified. In particular, when pressure fluctuations are large (large amplitude), the fiber optic strain signal may exhibit abnormal changes, indicating that the fracture has extended to the vicinity of an adjacent well. At this time, the fracture leading edge can be accurately inferred from the fiber optic strain signal waterfall plot. The location of the fracture leading edge inferred from the fiber optic strain signal waterfall plot can be obtained through fiber optic strain characteristic phenomena; when the fracture is close to the fiber optic monitoring well, a heart-shaped convergence will appear in the fiber optic strain waterfall plot.

[0056] In this exemplary embodiment, step 5 may include the following:

[0057] (1) In the acquisition of fiber strain signals, forward modeling is performed using the crack propagation induced fiber strain model (DDM model):

[0058]

[0059] In the formula: v is the Poisson's ratio of the rock, which is dimensionless; w i σ is the width of the i-th element, m; N is the number of crack elements; E is Young's modulus, GPa; σ xx σ yy σ zz These represent the stresses along the X, Y, and Z directions, respectively; u zz This represents the displacement in the Z direction (axial displacement of the optical fiber).

[0060] C z C zz C zzz C yy C xxz C yyz For kernel functions, the specific form is:

[0061]

[0062] Where r is the distance between any point in the three-dimensional coordinate system and the source point. (x,y) and (ξ,η) are the coordinates of the fiber's location and the local coordinates of the crack element, respectively (see...). Figure 1 ); Figure 1 The expression describes the fiber strain induced by the crack. When the crack propagates, each element will induce fiber strain. The sum of the strains induced by each element is the value of the fiber strain induced by the crack at the current moment.

[0063] d and the operator || are respectively:

[0064]

[0065] C(ξ,η)||=C(m,n)-C(m,-n)-C(-m,n)+C(-m,-n);

[0066] In the formula, m = 0.5Δx, n = 0.5Δy; Δx and Δy are the unit length and height, respectively, in meters.

[0067] It should be noted that C is an integration algorithm used to calculate the integral over this crack element. The expansion of the integral is shown above, where m and n represent 0.5 of the x and y distances from the mesh. Figure 1The hydraulic fracture is discretized into N rectangular units, and the displacement discontinuity fundamental solution of each unit is superimposed to obtain the stress and axial displacement generated by the fracture at the location of the optical fiber. The center point of the fracture is the origin of the coordinate system, and x, y, and z are along the fracture length, fracture height, and well shaft axis, respectively. The three-dimensional displacement discontinuity method is used to calculate the fracture-induced stress field, and the fracture element size is Δx×Δy.

[0068] The phase change of the optical fiber obtained by low-frequency optical fiber acoustic monitoring in adjacent wells is proportional to the axial displacement of the optical fiber. The optical fiber strain is obtained by the phase difference between adjacent measuring points. Assuming that the optical fiber is well coupled with the formation, the rock displacement induced by crack propagation is the optical fiber displacement.

[0069] The formula for calculating fiber strain is:

[0070]

[0071] In the formula: u zz L represents the axial displacement of the optical fiber, in meters (m). g ε is the gauge length (m); f denoted as axial strain of the optical fiber, which is dimensionless; z is the axial coordinate, in meters.

[0072] (2) In the interpretation of crack parameters using the fiber strain inversion model, it is assumed that the crack morphology is a rectangular crack and the crack height is a constant value. The length and width of the crack can be obtained through iterative calculation, and the obtained iterative parameters are compared with the crack parameters in the forward calculation.

[0073] Specifically, by combining real-time data of the bottom hole net pressure, the fracture width distribution at the fiber optic monitoring well location under constant net pressure can be calculated. The pressure inversion data and fiber optic strain inversion data are then combined for mutual verification. Further verification and optimization of the inversion results are achieved through joint analysis of fiber optic strain data and construction pressure. By comparing the errors of different inversion models, the optimal model (the model with the smallest error) is selected for the calculation and interpretation of fracture parameters.

[0074] (2-1) The crack width distribution assumed by the PKN analytical solution is as follows:

[0075]

[0076] In the formula, w represents the distribution pattern of the crack width; w0 represents the crack opening width (mm); L represents the half-crack length (m); H represents the half-crack height (m); x and y represent the coordinates in the x and y directions, respectively; k represents the power exponent, which is dimensionless; when k = 0, it is a ductility-dominated crack; when k = 1 / 3, it is a viscosity- and storage-dominated crack; when k = 3 / 8, it is a viscosity- and filtration-dominated crack.

[0077] It should be noted that w is the same as w in the above (1) content. i .

[0078] (2-2) By further analyzing the fiber strain signal and combining it with the fluctuation of construction pressure, based on the fiber strain monitoring data, the established strain-crack geometric parameter inversion model is used to calculate the crack geometric parameters.

[0079] Specifically, based on the assumed distribution morphology of the crack, the inversion model of fiber strain (i.e., the inversion model of strain-crack geometric parameters) is as follows:

[0080] min

[0081] st 0<L≤S w

[0082] 0<H≤H max

[0083] 0 < w 0 < w max

[0084] 0≤k≤1

[0085] In the formula, |||2 is the 2-norm; ε f The strain is obtained from fiber strain calculations and is dimensionless; ε m The strain of the optical fiber being monitored is dimensionless; z f Here, S represents the axial coordinate position of the optical fiber, in meters (m); w H represents the horizontal well spacing, in meters (m). max For maximum seam height, m; w max Let be the maximum crack width (mm); L be the half-crack length (m); H be the half-crack height (m); w0 be the crack opening width (mm); and k be the power exponent, conforming to the dimensionless crack width distribution assumed by the PKN analytical solution. When k = 0, the crack is dominated by ductility; when k = 1 / 3, it is dominated by viscosity and storage; and when k = 3 / 8, it is dominated by viscosity and filtration. Here, the maximum crack height and crack width can be determined empirically or by other monitoring methods.

[0086] The fiber strain was caused by crack propagation, and u was calculated using the expression in the DDM content. zz u zz The fiber displacement in the Z direction is used to calculate the fiber strain. Throughout this process, w is involved in the calculation, and it is assumed that w conforms to the solution expressed in (2-1).

[0087] (2-3) The net pressure P can also be used to calculate the crack width at the point where the crack collides with the optical fiber. The crack width at the optical fiber monitoring well can be calculated using the formula and compared with the optical fiber inversion results (w0 in the inversion model expression) to further verify the accuracy of the crack width.

[0088] Specifically, by combining real-time data of the bottom hole net pressure, the fracture width distribution at the fiber optic monitoring well location under constant net pressure can be calculated. The pressure inversion data and fiber optic strain inversion data are then combined for mutual verification. Further verification and optimization of the inversion results are achieved through joint analysis of fiber optic strain data and construction pressure. By comparing the errors of different inversion models, the optimal model is selected for the calculation and interpretation of fracture parameters.

[0089] According to the theory of elasticity, the crack width distribution under uniform stress is described by the following formula, which is the net pressure inversion formula for crack width:

[0090]

[0091] In the formula, W(x) is the width of the crack at a distance x from the crack center, in mm; x is the distance from the center point, in m; E is Young's modulus, in GPa; v is Poisson's ratio, dimensionless; P is the net pressure, in MPa; and L is the half-crack length, in m.

[0092] In this exemplary embodiment, step 6 may include: calculating the fracture conductivity using the Barree conductivity model (proposed by Barree and Conway in 2004) based on the fracture parameters obtained by inversion.

[0093] Specifically, the conductivity of the fracture is further analyzed by combining the inverted fracture geometric parameters. The conductivity calculation is based on the fiber optic strain inversion model and pressure fluctuation data, using the Barree conductivity model to estimate the fracture conductivity parameters. Conductivity reflects the contribution rate of the fracture in actual production and can be used to optimize fracturing construction design. The conductivity calculation includes dynamic analysis of fracture width and fracture propagation length. Based on changes in pressure fluctuations and strain signals, construction parameters (such as displacement and sand injection rate) are adjusted to improve the fracture conductivity.

[0094] The conductivity is calculated by substituting the crack parameters, which are derived from the inversion of fiber strain and pressure fluctuation, into the conductivity calculation model established by Barree.

[0095] Barree's flow capacity model:

[0096]

[0097] In the formula, K m Let D be the filling permeability under a given stress; K0 be the initial permeability; and σ be the σ value. C The characteristic transition stress, psi, represents the rapid change in permeability with stress; σ is the closure stress, psi; k A w A, represents the flow conductivity, in md·ft; w represents the distribution pattern of the crack width, in mm; A and B are regression coefficients, dimensionless.

[0098] Furthermore, regression coefficients A and B were obtained by fitting experimental data on the conductivity of the target reservoir to ensure the accuracy and applicability of the conductivity calculation model.

[0099] This invention employs a fiber optic strain inversion model to calculate the fracture width and a hydraulic fracturing pressure curve to calculate the fracture width. The two models are cross-validated, and the obtained fracture width distribution is then used to calculate the flow conductivity.

[0100] In this exemplary embodiment, Figure 2 The process of joint inversion of fiber optic strain and pressure fluctuation is illustrated, demonstrating the main concept of the method of this invention, including: obtaining fracturing parameters through a fracture propagation mathematical model; inputting the fracturing parameters into a fracture-induced fiber optic strain model (fracture propagation-induced fiber optic strain model) to obtain the fiber optic strain distribution in adjacent wells; constructing a fiber optic strain inversion model (strain-fracture geometry parameter inversion model) based on the fiber optic strain distribution in adjacent wells; performing fracture parameter inversion based on observation data; and using net pressure to calculate fracture width to verify the model; inputting the target reservoir conductivity data into a general conductivity calculation model (Barree conductivity model); fitting regression coefficients Sharp and EXP to obtain the regression coefficients; and calculating the conductivity based on Sharp and EXP and the inversion data of the verified inversion model.

[0101] Exemplary Example 2

[0102] This exemplary embodiment provides another method for diagnosing fracturing fracture parameters by combining adjacent well fiber strain and construction pressure.

[0103] The fracturing fracture parameter diagnosis method combining adjacent well fiber strain and construction pressure in this exemplary embodiment may include:

[0104] S1. Distributed fiber optic sensors are installed in the wellbore of the adjacent well to monitor the strain signals generated during the fracturing process.

[0105] S2. Install pressure sensors in the fracturing well to monitor changes in bottom hole pressure at different stages of the construction process.

[0106] S3. Compare and process the obtained fiber strain data and pressure data on the same time axis to identify the correlation between pressure fluctuations and fiber strain changes, identify the crack propagation path and dynamic behavior, and identify the strain signal at the crack front.

[0107] S4. By combining fiber strain and pressure fluctuation, an inversion model of strain-crack geometry parameters is established to obtain the geometric parameters of the crack at different times and to explain the hydraulic fracturing crack.

[0108] S5. Verify the accuracy of the strain-crack geometry parameter inversion model. Substitute the crack parameters obtained from the strain-crack geometry parameter inversion model into the Barree conductivity model to obtain the conductivity of the crack.

[0109] S6. Based on the obtained geometric parameters of the crack and the crack's conductivity, obtain the three-dimensional geometric morphology and dynamic change process of the crack.

[0110] In this exemplary embodiment, the different stages may include a pumping stage and a pump shutdown stage. The monitoring data from the pressure sensor may include the pumping pressure, net pressure, and the pressure response curve after pump shutdown.

[0111] In this exemplary embodiment, identifying the strain signal at the crack front may include: when the pressure fluctuation is large and the fiber strain signal shows a heart-shaped convergence (a heart-shaped convergence appears in the fiber strain waterfall plot), determining the moment when the crack hits the fiber well (when the crack is close to the fiber monitoring well), and distinguishing the crack front signal from other interference signals.

[0112] In this exemplary embodiment, the inversion model of strain-crack geometry parameters may include:

[0113] min

[0114] st 0<L≤S w

[0115] 0<H≤H max

[0116] 0 < w 0 < w max

[0117] 0≤k≤1

[0118] In the formula, |||2 is the 2-norm; ε f The strain is obtained from fiber strain calculations and is dimensionless; ε m The strain of the optical fiber being monitored is dimensionless; z f Here, S represents the axial coordinate position of the optical fiber, in meters (m); w H represents the horizontal well spacing, in meters (m). max For maximum seam height, m; w max denoted as maximum crack width (mm); L as half crack length (m); H as half crack height (m); w0 as crack opening width (mm); k as a power exponent, dimensionless; when k = 0, it is a ductility-dominated crack; when k = 1 / 3, it is a viscosity- and storage-dominated crack; when k = -3 / 8, it is a viscosity- and filtration-dominated crack. Here, the maximum crack height and width can be determined empirically or through other monitoring methods. Generally, crack width ranges from micrometers to centimeters, and crack height ranges from tens to hundreds of meters.

[0119] Among them, fiber strain calculation:

[0120]

[0121] In the formula: u zz L represents the axial displacement of the optical fiber, in meters (m). g ε is the gauge length (m); f denoted as axial strain of the optical fiber, which is dimensionless; z is the axial coordinate, in meters.

[0122] Furthermore, the interpretation of fracturing fractures may include:

[0123] Forward modeling was performed using a crack propagation-induced fiber strain model.

[0124]

[0125] In the formula: v is the Poisson's ratio of the rock, which is dimensionless; w i σ is the width of the i-th element, m; N is the number of crack elements; E is Young's modulus, GPa; σ xx σ yy σ zz These represent the stresses along the X, Y, and Z directions, respectively; u zz The axial displacement of the optical fiber is expressed in meters (m).

[0126] C z C zz C zzz C yy C xxz C yyz For kernel functions, the specific form is:

[0127]

[0128] in, (x, y) and (ξ, η) are the coordinates of the fiber location and the local coordinates of the crack element, respectively; r is the distance between any point in the three-dimensional coordinate system and the source point.

[0129] d and the operator || are respectively:

[0130]

[0131] C(ξ, η)||=C(m,n)-C(m,-n)-C(-m,n)+C(-m,-n);

[0132] In the formula, m = 0.5Δx and n = 0.5Δy; Δx and Δy are the unit length and height, respectively, both in meters.

[0133] In this exemplary embodiment, the inversion model of strain-crack geometry parameters assumes that the crack morphology is rectangular and the crack height is constant. The crack length and opening width can be obtained through iterative calculation, and the obtained iterative parameters are compared with the crack parameters in the forward calculation, showing minimal error.

[0134] The crack width distribution assumed by the PKN analytical solution can be:

[0135]

[0136] In the formula, w represents the distribution pattern of crack width in mm; w0 represents the crack opening width in mm; L represents the half-crack length in m; H represents the half-crack height in m; x and y represent the coordinates in the x and y directions, respectively; k represents the power exponent, which is dimensionless; when k = 0, it is a ductility-dominated crack; when k = 1 / 3, it is a viscosity- and storage-dominated crack; when k = 3 / 8, it is a viscosity- and filtration-dominated crack.

[0137] In this exemplary embodiment, verifying the accuracy of the strain-fracture geometry parameter inversion model may include: using fiber optic monitoring of the fracture width at the well site under constant bottom net pressure, and comparing it with w0 of the strain-fracture geometry parameter inversion model to verify the accuracy of the strain-fracture geometry parameter inversion model.

[0138] In this exemplary embodiment, the calculation of the fracture width at the fiber optic monitoring well under constant bottom net pressure can be performed as follows:

[0139]

[0140] In the formula, W(x) is the width of the crack at a distance x from the crack center, in mm; x is the distance from the center point, in m; E is Young's modulus, in GPa; v is Poisson's ratio, dimensionless; P is the net pressure, in MPa; and L is the half-crack length, in m.

[0141] In this exemplary embodiment, the Barree flow capability model can be as follows:

[0142]

[0143] In the formula, K m Let D be the filling permeability under a given stress; K0 be the initial permeability; and σ be the σ value. C The characteristic transition stress, psi, represents the rapid change in permeability with stress; σ is the closure stress, psi; k A w A The conductivity is expressed in md·ft; w represents the distribution of fracture width; A and B are regression coefficients, dimensionless. Here, the regression coefficients A and B are obtained by fitting experimental data on the conductivity of the target reservoir.

[0144] In summary, the beneficial effects include:

[0145] This invention provides a method for diagnosing fracturing fracture parameters by combining adjacent well fiber optic strain and operational pressure, primarily applied in the oil and gas field development field. By comprehensively analyzing fiber optic strain data with operational pressure variations, this invention can more accurately identify strain signals at the fracture front and verify the inversion results of fracture parameters by incorporating pressure fluctuations. This approach significantly improves the accuracy of fracture geometry parameter interpretation, particularly in real-time monitoring and diagnosis of fracture length, width, and height, providing reliable technical support and effectively optimizing fracturing design and production decisions.

[0146] Although the invention has been described above in conjunction with exemplary embodiments, those skilled in the art will understand that various modifications and changes can be made to the exemplary embodiments of the invention without departing from the spirit and scope defined by the claims.

Claims

1. A method for diagnosing fracturing fracture parameters by combining adjacent well fiber optic strain and construction pressure, characterized in that, The method includes: Distributed fiber optic sensors are deployed in adjacent wellbores to monitor strain signals generated during fracturing. Pressure sensors are deployed in the fracturing well to monitor changes in bottom hole pressure at different stages of the construction process; The obtained fiber strain data and pressure data are compared and processed on the same time axis to identify the correlation between pressure fluctuations and fiber strain changes, identify the crack propagation path and dynamic behavior, and identify the strain signal at the crack front. By combining fiber strain and pressure fluctuation, an inversion model of strain-crack geometry parameters is established to obtain the geometric parameters of the crack at different times and to explain the hydraulic fracturing crack. To verify the accuracy of the strain-crack geometry parameter inversion model, the crack parameters obtained from the strain-crack geometry parameter inversion model were substituted into the Barree conductivity model to obtain the conductivity of the crack. Based on the obtained geometric parameters and conductivity of the cracks, the three-dimensional geometric morphology and dynamic change process of the cracks are obtained.

2. The fracturing fracture parameter diagnosis method based on combined adjacent well fiber strain and construction pressure according to claim 1, characterized in that, The inversion model for strain-crack geometry parameters includes: s.t.0<L≤S w 0<H≤H max 0<w0<w max 0≤k≤1 In the formula, |||2 is the 2-norm; ε f The strain is obtained from fiber strain calculations and is dimensionless; ε m The strain of the optical fiber being monitored is dimensionless; z f Here, S represents the axial coordinate position of the optical fiber (m); w H represents the horizontal well spacing, in meters (m). max For maximum seam height, m; w max is the maximum seam width, mm; L is the half seam length, m; H is the half seam height, m; w0 is the seam opening width, mm; k is a power exponent, dimensionless; when k = 0, it is a toughness-dominated crack; when k = 1 / 3, it is a viscosity- and storage-dominated crack; when k = 3 / 8, it is a viscosity- and filtration-dominated crack.

3. The fracturing fracture parameter diagnosis method based on combined adjacent well fiber strain and construction pressure according to claim 2, characterized in that, The fiber strain calculation includes: In the formula: u zz L represents the axial displacement of the optical fiber, in meters (m). g ε is the gauge length, in meters; f denoted as axial strain of the optical fiber, which is dimensionless; z is the axial coordinate, in meters.

4. The fracturing fracture parameter diagnosis method based on combined adjacent well fiber strain and construction pressure according to claim 2, characterized in that, The strain-crack geometry parameter inversion model is configured to include: a rectangular crack shape and a constant crack height.

5. The fracturing fracture parameter diagnosis method based on combined adjacent well fiber strain and construction pressure according to claim 1, characterized in that, The accuracy of the inversion model for strain-fracture geometry parameters is verified by comparing the fracture width at the wellhead with the fracture width in the inversion model for strain-fracture geometry parameters under constant net pressure at the bottom of the well using fiber optic monitoring.

6. The fracturing fracture parameter diagnosis method based on combined adjacent well fiber optic strain and construction pressure according to claim 5, characterized in that, The calculation of the fracture width at the fiber optic monitoring well under constant bottom net pressure is as follows: In the formula, W(x) is the width of the crack at a distance x from the crack center, in mm; x is the distance from the center point, in m; E is Young's modulus, in GPa; ν is Poisson's ratio, dimensionless; P is the net pressure, in MPa; and L is the half-crack length, in m.

7. The fracturing fracture parameter diagnosis method based on combined adjacent well fiber strain and construction pressure according to claim 1, characterized in that, The Barree flow control capability model is as follows: In the formula, K m Let D be the filling permeability under a given stress; and K0 be the initial permeability, D. σ C The characteristic transition stress, psi, represents the rapid change in permeability with stress; σ is the closure stress, psi; k A w A , represents the flow conductivity, in md·ft; w represents the distribution pattern of the crack width, in mm; A and B are regression coefficients, dimensionless.

8. The fracturing fracture parameter diagnosis method based on combined adjacent well fiber strain and construction pressure according to claim 7, characterized in that, The regression coefficients A and B were obtained by fitting experimental data on the conductivity of the target reservoir.

9. The fracturing fracture parameter diagnosis method based on combined adjacent well fiber strain and construction pressure according to claim 1, characterized in that, The identification of the strain signal at the crack front includes: determining the moment when the crack hits the fiber well when the pressure fluctuation is large and the fiber strain signal shows a cardioid convergence phenomenon, and distinguishing the crack front signal from other interference signals.

10. The method for diagnosing fracturing fracture parameters based on combined adjacent well fiber optic strain and construction pressure according to claim 1, characterized in that, The different stages include the pumping stage and the pump shutdown stage; the monitoring data of the pressure sensor includes the pumping pressure, net pressure, and the pressure response curve after the pump is shut down.

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