Method for evaluating fractured fracture parameters of shale oil multi-section fractured horizontal well

Abstract

The invention provides a fracturing crack parameter evaluation method for a shale oil multi-section fracturing horizontal well. The shale oil multi-section fractured horizontal well post-fracturing crack parameter evaluation method comprises the following steps: multi-source data acquisition: acquiring fracturing construction data, logging data, production dynamic data and inter-well interference information of a target shale oil well; and monitoring the content of the osmotic adjusting substance: realizing real-time monitoring of the osmotic adjusting substance after pressure through a shaft sensor system. According to the method for evaluating the fractured fracture parameters of the shale oil multi-section fractured horizontal well, a systematic fracture parameter evaluation method is constructed by fusing key technical links such as multi-source data acquisition, osmotic regulation substance real-time monitoring, inter-section interference modeling inversion, phenotype-biochemical index coupling analysis and the like; and the bottlenecks of insufficient crack evaluation precision and difficult identification of inter-segment crack interference in the prior art are broken through. And the method has good engineering practical value and popularization prospect.

Description

Technical Field

[0001] The present invention relates to the field of oil and natural gas exploitation, and in particular to a method for evaluating fracture parameters after multi-stage fracture of a shale oil horizontal well. Background Art

[0002] With the widespread development of unconventional oil and gas resources, shale reservoirs have become a key target for exploration and development due to their rich resources and enormous potential. Multi-stage hydraulic fracturing has become a key technical means for the economic and efficient development of shale oil. Multi-stage fracturing in horizontal wells, in particular, can significantly increase reservoir stimulation volume and single-well productivity. After fracturing, accurate assessment of fracture propagation characteristics and stimulation effects is crucial for optimizing fracturing design and guiding subsequent development. Therefore, quantitative evaluation of post-fracturing fracture parameters has become a critical step in shale oil development.

[0003] However, existing post-fracturing evaluation methods have significant shortcomings. On the one hand, traditional methods rely on single methods such as well testing, microseismic monitoring, or production data inversion to obtain fracture parameters, which suffer from limited accuracy, strong interpretation diversity, high cost, or restricted application conditions. On the other hand, under multi-stage fracturing conditions, the fracture system is complex and there is mutual interference between fractures. Traditional models cannot accurately characterize the spatial distribution and variation of fracture parameters, making it difficult to accurately evaluate the fracturing effect. Existing technologies still lack a comprehensive fracture parameter evaluation method that is highly systematic, widely adaptable, and capable of integrating multi-source data, making it difficult to meet the actual needs of efficient shale oil development. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a method for evaluating fracture parameters after multi-stage fracturing of shale oil horizontal wells, which solves the problem of insufficient accuracy in fracture parameter evaluation and the difficulty in characterizing the interference and heterogeneous effects of multi-stage fracturing fractures.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for evaluating fracture parameters after multi-stage fracture of a shale oil horizontal well, comprising:

[0006] S1. Multi-source data acquisition: Collect fracturing operation data, well logging data, production performance data, and inter-well interference information of the target shale oil well to establish an original data set under multi-stage fracturing conditions. The data includes fracture propagation path, pressure response, fluid production characteristics, and geological attribute information;

[0007] S2. Osmotic Control Substance Content Monitoring: Real-time monitoring of osmotic control substances after fracturing is achieved through a wellbore sensor system. The spatial distribution and evolution of osmotic control substances within the fracture network are captured, generating continuous time series data.

[0008] S3. Fracture parameter inversion: Based on the acquired multi-source data, a multi-stage hydraulic fracture response inversion model is constructed. This model considers the interference effects and flow heterogeneity between different segments and identifies the length, conductivity, height, and width of each fracture segment.

[0009] S4. Phenotype-Biochemical Parameter Coupling Analysis: This study correlates external productivity phenotypes with the dynamic characteristics of materials within the internal fractures at multiple scales. This coupling is achieved through structural equation modeling, revealing the dynamic control mechanism of the fracture system on productivity.

[0010] S5. Fracture parameter classification and fracturing optimization feedback: Fracture parameters are scored and graded based on the inversion results, and a fracture effectiveness evaluation report is output to guide subsequent fracturing design and optimization of inter-stage interference adjustment strategies.

[0011] Preferably, the multi-source data acquisition in S1 includes resistivity, density, acoustic wave time difference, natural gamma logging curve, and further includes fracture identification logging data for constructing an initial fracture spatial distribution model, and the fracture identification logging includes imaging logging or microelectrode array logging.

[0012] Preferably, the monitoring of the permeability regulating substance content in S2 is achieved by combining a wellbore sensor with a fracture grid model, and constructing a time series based on the following concentration distribution function:

[0013]

[0014] Where C(x,t) is the concentration at position x in the crack at time t, C0 is the initial concentration, λ is the attenuation coefficient, v is the flow velocity in the crack, and D is the diffusion coefficient.

[0015] Preferably, the crack parameter inversion in S3 is combined with the construction parameters to set the boundary conditions, and the following residual objective function is used to optimize the inversion accuracy:

[0016]

[0017] in, is the actual measured pressure, is the simulation value, α is the regularization factor, and Ω(m) is the model parameter constraint term.

[0018] Preferably, the multi-stage hydraulic fracturing crack response inversion model described in S3 takes into account the interference between stages, and further introduces the inter-stage interference coupling mechanism. By constructing an interference weight matrix based on liquid production, inter-stage distance and pressure propagation coefficient, it is used to quantify the response intensity and mutual interference relationship of each stage of the crack, and divide each fracture stage into a main control stage and an interference stage according to a set threshold, thereby eliminating overlapping response areas and improving the convergence and accuracy of the inversion model.

[0019] Preferably, the phenotype-biochemical parameter coupling analysis described in S4 further includes: external productivity phenotypes include daily oil and gas production, pressure recovery rate and return fluid volume, and internal material parameters include permeability concentration gradient and pH value change in the fracture section, and the two are quantitatively coupled through a structural equation model.

[0020] Preferably, the fracture parameter classification in S5 is based on the conductivity calculation result obtained by inversion, and the conductivity is obtained by comprehensive evaluation of fracture width, fracture length and proppant laying density; wherein, fracture conductivity higher than 5D·cm is high grade, between 1D·cm and 5D·cm is medium grade, and lower than 1D·cm is low grade; the classification result is imported into the three-dimensional reservoir modeling software to generate a fracturing recommendation area layer, and serves as the input condition for subsequent segment optimization and fracturing construction design.

[0021] Preferably, the fracture parameter classification and fracturing optimization feedback described in S5 construct a fracturing section optimization table based on the fracture effectiveness results and the comprehensive reservoir score, taking into account geological heterogeneity, construction feasibility and economy, and outputting the section optimization ranking to guide the next round of fracturing deployment.

[0022] The present invention provides a method for evaluating fracture parameters after multi-stage fracture in shale oil horizontal wells. It has the following beneficial effects:

[0023] This method for evaluating fracture parameters after multi-stage fracture in shale oil horizontal wells integrates key technical links such as multi-source data acquisition, real-time monitoring of permeability-regulating substances, inter-stage interference modeling and inversion, and coupled analysis of phenotypic and biochemical indicators to create a systematic fracture parameter evaluation method. This method overcomes the bottlenecks of existing technologies, which include insufficient fracture evaluation accuracy and difficulty identifying inter-stage fracture interference. In particular, the introduction of a material migration concentration field function and a residual optimization inversion model in steps S2 and S3, respectively, dynamically restores the fracture development process and inter-fracture flow behavior, achieving high-resolution identification of fracture spatial structure.

[0024] Furthermore, the present invention proposes a coupling mechanism between the functionality of the fracture system and its productivity response in steps S4 and S5, establishing a data channel between the "external productivity phenotype" and "internal fracture material changes," significantly enhancing the ability to interpret fracture parameters and fracturing effects. Through a conductivity grading and three-dimensional modeling feedback mechanism, the present invention can output a highly adaptable table of recommended fracturing zones and sections, providing data support and decision-making basis for subsequent fracturing design, demonstrating promising engineering practicality and potential for widespread adoption. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flowchart for implementing the invention. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0027] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating fracture parameters after multi-stage fracture of a shale oil horizontal well, including: S1. Acquisition of multi-source data:

[0028] a. Fracturing data collection: Pumping parameters for each fracturing stage were obtained from the construction records, including injection fluid volume, displacement, sand ratio, proppant type, and maximum pump pressure. Data for six fracturing stages were collected for this well, with recording intervals of 10 seconds.

[0029] b. Conventional logging data acquisition: Standard logging operations are performed on the well to obtain the following logging curves:

[0030] Resistivity curve: range 1–50Ω·m, resolution 0.1Ω·m; density: 2.35–2.65 g / cm3; acoustic wave time difference: 75–120μs / ft; natural gamma: 35–120API; data depth interval is 0.125m, and the logging depth coverage well section is: 2700m–3350m.

[0031] c. Fracture identification logging: To accurately determine the orientation, dip, and development intensity of fractures, imaging logging was performed in the target well section between 2900 and 3100 m, acquiring imaging images and microelectrode array logging data.

[0032] d. Imaging logging reveals natural fractures at depths of 2907.3 m, 2921.5 m, and 2958.6 m, with dipping angles of 42°, 38°, and 60°, respectively. The fractures strike primarily in the NEE-SWW direction. The fracture widths are estimated to be 0.2–0.5 mm, and the vertical fracture lengths range from 1.2–2.8 m.

[0033] e. Production dynamic data: Use continuous flow meter to obtain daily oil and gas production data for the first 30 days of production, and simultaneously record the changes in casing pressure and oil pressure. The oil production range is 2.1–5.4m 3 / d, with a gas production of 500–1200m 3 The casing pressure recovery rate is used for pressure response fitting in the subsequent inversion model.

[0034] f. Interwell interference monitoring data: Three comparison wells were deployed within approximately 300 meters of the well to record casing pressure disturbances and fluid production changes during fracturing and within 24 hours after fracturing. Synchronous compressive stress disturbance peaks were observed in the comparison wells, with the maximum disturbance amplitude being approximately 0.6 MPa.

[0035] g. Original dataset construction: categorize the various types of data collected above by segment and store them in a database to build a data structure that supports subsequent inversion analysis. The dataset format is standardized to LAS and CSV formats supported by standard geological modeling platforms, and includes metadata tags such as well segment number, timestamp, physical quantity name and unit.

[0036] S2. Osmotic Control Substance Content Monitoring: Real-time monitoring of osmotic control substances after fracturing is achieved through a wellbore sensor system. The spatial distribution and evolution of osmotic control substances in the fracture network are obtained, generating continuous time series data. Osmotic control substance content monitoring is achieved by combining wellbore sensors with a fracture gridding model, and a time series is constructed based on the following concentration distribution function:

[0037]

[0038] Where C(x,t) is the concentration at position x in the crack at time t, C0 is the initial concentration, λ is the attenuation coefficient, v is the flow velocity in the crack, and D is the diffusion coefficient.

[0039] S3. Fracture parameter inversion: Based on the acquired multi-source data, a multi-segment hydraulic fracture response inversion model is constructed. The interference effects and flow heterogeneity between different segments are jointly considered to identify the length, conductivity, fracture height, and fracture width parameters of each fracture segment. The fracture parameter inversion is combined with the construction parameters to set the boundary conditions, and the following residual objective function is used to optimize the inversion accuracy:

[0040]

[0041] in, is the actual measured pressure, is the simulation value, α is the regularization factor, and Ω(m) is the model parameter constraint term. The multi-stage hydraulic fracturing fracture response inversion model takes inter-segment interference into consideration and further introduces an inter-segment interference coupling mechanism. By constructing an interference weight matrix based on liquid production, inter-segment distance, and pressure propagation coefficient, it is used to quantify the response intensity and mutual interference relationship of each fracture segment. Each fracture segment is divided into a main control segment and an interference segment according to a set threshold, and then the overlapping response areas are eliminated to improve the convergence and accuracy of the inversion model.

[0042] S4. Phenotype-biochemical parameter coupling analysis: The external productivity phenotype is multi-scale correlated with the dynamic characteristics of the material in the internal fracture, and the indicator coupling is achieved through structural equation modeling to reveal the dynamic control mechanism of the fracture system on productivity. In order to reveal the dynamic impact of the fracture system on productivity, the daily oil production, pressure recovery rate and flowback volume of a shale oil horizontal well within 30 days after pressure were selected as external productivity phenotype indicators. At the same time, the penetrant concentration gradient and pH value changes in the fracture section were collected as internal biochemical parameters. The specific data are as follows: Daily oil production range: 2.3–4.9m 3 / d, pressure recovery rate: 0.15–0.42 MPa / d, flowback volume: 100–320 m 3 / d, the rate of change of penetrant concentration within the fracture was 0.05–0.18 mol / L / d, and the pH fluctuation range was 6.1–7.8. A structural equation model was used to establish the indicator coupling pathway, with oil yield as the outcome variable, penetrant concentration gradient and pH value as mediating variables, and flowback fluid volume and pressure recovery rate as exogenous variables. The model fit was good, and the results showed that changes in fracture materials had a significant mediating effect on the productivity phenotype. In particular, the standardized path coefficient between penetrant concentration change and daily oil production was 0.67, indicating a significant positive correlation.

[0043] S5. Fracture parameter classification and fracturing optimization feedback: Fracture parameters are scored and graded based on the inversion results. A fracture effectiveness evaluation report is output to guide subsequent fracturing design and optimization of inter-stage interference adjustment strategies. Fracture parameter classification is based on the conductivity calculation results obtained by inversion. The conductivity is obtained through a comprehensive evaluation of fracture width, fracture length, and proppant placement density. Fracture conductivity greater than 5D·cm is classified as high, between 1D·cm and 5D·cm as medium, and below 1D·cm as low. The classification results are imported into the 3D reservoir modeling software to generate a fracturing recommendation area layer, which serves as the input condition for subsequent stage optimization and fracturing construction design. Fracture parameter classification and fracturing optimization feedback construct a fracturing stage optimization table based on the fracture effectiveness results and the comprehensive reservoir score, taking into account geological heterogeneity, construction feasibility, and economy. The output stage optimization ranking is used to guide the next round of fracturing deployment.

[0044] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating fracture parameters after multi-stage fracture of shale oil horizontal wells, characterized in that: include: S1. Multi-source data acquisition: Collecting fracturing operation data, well logging data, production performance data, and inter-well interference information of the target shale oil well to establish an original data set under multi-stage fracturing conditions. The multi-source data includes fracture propagation path, pressure response, fluid production characteristics, and geological attribute information; S2. Osmotic Control Substance Content Monitoring: Real-time monitoring of osmotic control substances after fracturing is achieved through a wellbore sensor system. The spatial distribution and evolution of osmotic control substances within the fracture network are captured, generating continuous time series data. S3. Fracture parameter inversion: Based on the acquired multi-source data, a multi-stage hydraulic fracture response inversion model is constructed. This model considers the interference effects and flow heterogeneity between different segments and identifies the length, conductivity, height, and width of each fracture segment. S4. Phenotype-Biochemical Parameter Coupling Analysis: This involves multi-scale correlation of external productivity phenotypes with the dynamic characteristics of materials within the internal fractures. This coupling is achieved through structural equation modeling, revealing the dynamic control mechanism of the fracture system on productivity. S5. Fracture parameter classification and fracturing optimization feedback: Fracture parameters are scored and graded based on the inversion results, and a fracture effectiveness evaluation report is output to guide subsequent fracturing design and optimization of inter-stage interference adjustment strategies.

2. The method for evaluating fracture parameters after fracture in a shale oil multi-stage fractured horizontal well according to claim 1, wherein: The multi-source data acquisition in S1 includes resistivity, density, acoustic wave time difference, natural gamma logging curve, and further includes fracture identification logging data for constructing an initial fracture spatial distribution model. The fracture identification logging includes imaging logging or microelectrode array logging.

3. The method for evaluating fracture parameters after fracture in a shale oil multi-stage fractured horizontal well according to claim 1, wherein: The permeability regulating substance content monitoring described in S2 is achieved by combining wellbore sensors with a fracture gridding model, and a time series is constructed based on the following concentration distribution function: Where C(x,t) is the concentration at position x in the crack at time t, C0 is the initial concentration, λ is the attenuation coefficient, v is the flow velocity in the crack, and D is the diffusion coefficient.

4. The method for evaluating fracture parameters after fracture in a shale oil multi-stage fractured horizontal well according to claim 1, wherein: The crack parameter inversion described in S3 is combined with the construction parameters to set the boundary conditions, and the following residual objective function is used to optimize the inversion accuracy: in, is the actual measured pressure, is the simulation value, α is the regularization factor, and Ω(m) is the model parameter constraint term.

5. The method for evaluating fracture parameters after fracture in a shale oil multi-stage fractured horizontal well according to claim 1, wherein: The multi-stage hydraulic fracturing fracture response inversion model described in S3 takes inter-stage interference into consideration and further introduces an inter-stage interference coupling mechanism by constructing an interference weight matrix based on liquid production, inter-stage distance, and pressure propagation coefficient.

6. The method for evaluating fracture parameters after multi-stage fracture of a shale oil horizontal well according to claim 1, characterized in that: The coupled phenotypic-biochemical parameter analysis described in S4 further includes: External production phenotypes, including daily oil and gas production, pressure recovery rate, and flowback volume; Internal material parameters, including penetrant concentration gradient and pH value changes within the fracture section.

7. The method for evaluating fracture parameters after multi-stage fracture of a shale oil horizontal well according to claim 1, characterized in that: The fracture parameter classification in S5 is based on the conductivity calculation result obtained by inversion, and the conductivity is obtained by comprehensive evaluation of fracture width, fracture length and proppant laying density.

8. The method for evaluating fracture parameters after multi-stage fracture of a shale oil horizontal well according to claim 1, characterized in that: The fracture parameter classification and fracturing optimization feedback described in S5 construct a fracturing section optimization table based on the fracture effectiveness results and the comprehensive reservoir score, taking into account geological heterogeneity, construction feasibility and economy, and outputting the section optimization ranking to guide the next round of fracturing deployment.

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