Method for evaluating effective fracture linear density of geological information constraint borehole logging

Abstract

The invention relates to the technical field of oil exploration, in particular to a geological information constraint borehole logging effective fracture linear density evaluation method. The method comprises the following steps: screening natural fracture development and feature information in various geological data to serve as constraint and inspection data of an electric imaging logging fracture identification result; identifying and calculating the characteristics of the natural effective crack by using an electric imaging image identification technology; and in combination with production, determining the evaluation weight of each characteristic parameter in the natural effective crack, discriminating an effective crack development section, and establishing an effective crack linear density index. According to the geological information constraint borehole logging effective crack linear density evaluation method provided by the invention, multi-parameter quantitative evaluation of effective cracks is realized, comprehensive and unified indexes are adopted, and the method has important guiding significance for oil and gas field well position deployment and development interval optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration, and particularly relates to a method for evaluating the effective fracture line density of borehole logging constrained by geological information. Background Art

[0002] Currently, in the traditional exploration and development of oil and gas reservoirs, the development of natural fractures can not only greatly improve the seepage capacity of the reservoir, but also, as the reservoir space for oil and gas, the intervals with developed natural fractures are often the key to obtaining stable and high yields in oil and gas exploration and development.

[0003] With the continuous improvement of technical methods, it is found that there are two types of natural fractures: effective and ineffective. For ineffective fractures, generally, they are filled with other minerals in their space, resulting in little or only a small amount of oil and gas contained in them, or because their dip angles are too small to form complex fracture networks by means of engineering fracturing and other technical means to obtain production capacity. For these reasons, such fractures are not conducive to the actual economic production capacity construction. Therefore, the identification of effective natural fractures is very important.

[0004] Conventional identification work mainly relies on cores obtained from underground during actual drilling as the most intuitive and highest-resolution data, which can provide a relatively accurate understanding of the fracture development and characteristics underground, and can calculate various parameters such as fracture porosity and permeability through experimental means. However, due to the complexity of underground geological conditions, it is difficult to obtain cores. Often, within a section of several hundred meters, there is a situation where only a dozen meters of cores suitable for observation can be obtained. In order to have a clear and comprehensive understanding of the natural fracture development and characteristics of the entire well section, the method of electrical imaging logging is adopted, which can reflect the geological conditions near the wellbore of the drilling into an image, and through the processing of the image, the fracture identification of the entire well section can be realized. However, in the existing electrical imaging logging fracture identification technology, there is a lack of processing and constraint on actual geological information, and less application of intuitive and high-resolution geological information such as cores and thin sections during the process of identification technology iteration, resulting in a lack of accuracy in fracture identification results. At the same time, when establishing the effective fracture identification standard, there is a lack of comprehensive consideration of standard indicators for various fracture characteristics such as fracture dip angle, filling property, aperture, fracture porosity, and fracture permeability under electrical imaging logging identification, and a unified process for effective fracture evaluation has not been realized. Summary of the Invention

[0005] (I) Technical Problems to be Solved The present invention provides a method for evaluating the effective fracture line density of borehole logging constrained by geological information to overcome the problems in the prior art that there is a lack of processing and constraint on actual geological information for natural fracture identification and the identification results lack accuracy.

[0006] (II) Technical Solutions To solve the above problems, the present invention provides a method for evaluating the effective fracture line density of borehole wall logging constrained by geological information, including: Step S1: Screen the natural fracture development and characteristic information in various geological data as the data for constraining and verifying the fracture identification results of electrical imaging logging; Step S2: Use the image recognition technology of electrical imaging to identify and calculate the characteristics of natural effective fractures; Step S3: Combine production, establish the evaluation weights of each characteristic parameter in the evaluation of natural effective fractures, judge the developed sections of effective fractures, and establish an effective fracture line density index.

[0007] Further, step S1 specifically includes: screening the information in various geological data such as cores, thin sections, drilling records that can reflect the development degree and characteristics of natural fractures, and using it as the data for constraining and verifying the fracture identification results of electrical imaging logging; Among them, the fracture characteristics are identified from core data: Screen the cores of the target interval in the study area for observation, directly identify the number of fractures developed on the cores manually, and obtain the characteristics of fracture dip angle, aperture and filling property through measurement and observation. Then, conduct experiments on the plug samples drilled from the cores to measure the fracture porosity and fracture permeability parameters, so as to determine the basic characteristics of the fractures; The fracture characteristics are identified from thin section data: Make samples of the key parts with natural fractures in the core into thin sections and observe them under a microscope to determine the development degree, filling property and filling mineral type of microfractures; The specific process of identifying fracture characteristics from drilling records data: Drilling records data including wellbore leakage, gas logging total hydrocarbon and drilling time can play a role in constraining fracture identification.

[0008] Further, step S2 specifically includes: Identifying and calculating the characteristics of natural fracture dip angle, filling property, aperture, fracture porosity and fracture permeability; The identification of natural fractures appears as a series of sinusoidal lines of conductance anomalies with variable widths and large amplitude changes and can be interrupted on the electrical imaging image, and are divided into flat fractures, low-angle fractures, high-angle fractures and vertical fractures according to the magnitude difference.

[0009] The identification of filling property includes unfilled fractures, semi-filled fractures, fully-filled fractures and network fractures composed of fractures at different angles; Unfilled fractures are the most favorable type of fractures for oil and gas exploitation, which appear as black dark bands in the form of sine waves on the imaging map; While filled fractures are divided into low-resistance material filled fractures such as shale and high-resistance material filled fractures such as calcite and silica according to the type of filling material in the fractures, and appear as bright and dark sine curves on the electrical imaging map respectively; Unfilled and low-resistance material filled fractures appear as dark thin lines or intermittent but traceable dark sine curves in the shape of approximate sine curves on the imaging map; For fractures filled with high-resistivity substances, when there are significant electrical differences, faint white sine curves can be discerned on the imaging map.

[0010] Furthermore, step S2 also includes: For the calculation of fracture aperture, it is directly read from the width of the dark or bright bands on the image.

[0011] Furthermore, step S2 also includes: Based on the correlation between the fracture porosity measured by core experiments and the porosity calculated by logging, the porosity of fractures calculated by logging is corrected; then the pore-permeability relationship of the core is established to obtain the calculation formulas for fracture porosity and permeability. The calculated fracture porosity and permeability are compared with the porosity data measured by core experiments to verify the accuracy of the calculation results.

[0012] Furthermore, step S2 also includes: For the calculation of fracture permeability, the correlation between the porosity and permeability of core experiments is established to obtain the calculation results of fracture porosity and fracture permeability based on electrical imaging logging.

[0013] Furthermore, step S3 specifically includes: Step S31: Combining the actual production capacity, determining the range values of various characteristic parameters for the discrimination of effective fractures in the actual study area and calculating the weights of each parameter, discriminating the developed sections of effective fractures, establishing an effective fracture line density index, and forming a complete set of effective fracture line density evaluation techniques and methods; For the well sections in stable production in the study area, obtain the dip angle, filling property, aperture, fracture porosity, and fracture permeability characteristics of the natural fractures, analyze the relationship between these parameters and the actual production, and thus establish a method for identifying effective fractures with multiple parameters; Step S32: For the weight calculation of the effective fracture discrimination formula, compare the correlation between the dip angle, filling property, fracture porosity, fracture permeability characteristics of fractures and the production capacity, and calculate the weight values of each parameter in the judgment of effective fractures through intelligent algorithms; Step S33: Discriminate the effective natural fractures in the entire well section, quantitatively describe the development of effective fractures in the entire well section, calculate the number of developed effective fractures by cumulative calculation per meter, and depict the line density of effective fractures in the entire well section. The greater the value of the effective fracture line density, the higher the development degree of the effective natural fractures in this section; After combining the calculation of electrical imaging fracture characteristics and the discrimination of effective fractures, comparing the effective fracture development line density with the fracture characteristics of the core observation section can verify the accuracy of the identification results again.

[0014] Further, the calculation formula for judging effective fractures in step S32 is as follows: I = W1 * I dip+ W2 * I fi + W3 * I por + W4 * I perm Where: I - effective fracture discrimination coefficient, I dip - dip discrimination index, I fi - filling discrimination index, I por - fracture porosity discrimination index, I perm - fracture permeability discrimination index, W1, W2, W3, W4 - weight values of each discrimination index; After judging the weight value of the effective fracture, when the judgment value is greater than the threshold value, this section is considered an effective fracture, and when it is less than the threshold value, this section is defined as an ineffective fracture.

[0015] (III) Beneficial effects The present invention provides a method for evaluating the effective fracture line density of borehole wall logging constrained by geological information, realizing the multi-parameter quantitative evaluation of effective fractures, adopting a comprehensive and unified index, which has important guiding significance for the well location deployment and preferred development interval of oil and gas fields. Description of the drawings

[0016] Figure 1 For various geological data information in the example of the present invention; Figure 2 For the core fractures in the example of the present invention; Figure 3 For the thin section fractures in the example of the present invention; Figure 4 For the drilling and logging information in the example of the present invention; Figure 5 For the electro-imaging flat fractures in the example of the present invention; Figure 6 For the electro-imaging low-angle fractures in the example of the present invention; Figure 7 For the electro-imaging high-angle fractures in the example of the present invention; Figure 8 For the electro-imaging vertical fractures in the example of the present invention; Figure 9 For the electro-imaging unfilled fractures in the example of the present invention; Figure 10 For the electro-imaging filled fractures in the example of the present invention; Figure 11 For the calculation of electro-imaging fracture characteristic parameters in the example of the present invention; Figure 12 For the comprehensive discrimination result in the example of the present invention; Figure 13This is the flowchart of the evaluation method for the effective fracture line density of borehole wall logging constrained by geological information in the embodiments of the present invention. Embodiment

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] As Figure 13 shown, the present invention provides a flowchart of an evaluation method for the effective fracture line density of borehole wall logging constrained by geological information. The evaluation method for the effective fracture line density of borehole wall logging constrained by geological information specifically includes: Step S1: Screen the natural fracture development and characteristic information in various geological data.

[0019] Screen the information in various geological data such as cores, thin sections, drilling records, etc. that can reflect the development degree and characteristics of natural fractures, and use it as the data for constraining and verifying the fracture identification results of electrical imaging logging; Identify fracture characteristics from core data: Screen the cores in the target interval of the study area for observation, and the number of fractures developed on the core can be directly identified manually. Moreover, through measurement and observation, the characteristics of fracture dip angle, aperture, and filling can be obtained. Then, conduct experiments on the plug samples drilled from the core to measure the fracture porosity and fracture permeability parameters, so as to determine the basic characteristics of the fractures; Analyze and identify fracture characteristics from thin section data: Make samples of the key parts with natural fractures in the core into thin sections and observe them under a microscope to determine the development degree, filling, and filling mineral types of microfractures; The specific process of identifying fracture characteristics from drilling records: Drilling records include wellbore leakage, gas logging total hydrocarbon, and drilling time, etc., which can play a certain role in constraining fracture identification. For example, when there is a well leakage interval, a high gas logging total hydrocarbon value, and an abnormal acceleration of drilling time, it indicates that the possibility of fractures in this interval is relatively high. However, due to being easily affected by underground environment and human factors, the accuracy of the results is generally average.

[0020] Step S2: Identify and calculate the characteristics of natural effective fractures Use the image recognition technology of electrical imaging to identify and calculate the characteristics of natural fractures, such as fracture dip angle, filling, aperture, fracture porosity, fracture permeability, etc.; The identification of natural fractures appears as a series of sinusoidal lines of conductance anomalies with variable widths and large amplitude changes and can be interrupted on the electrical imaging image. According to the magnitude difference, they are divided into flat fractures, low-angle fractures, high-angle fractures, and vertical fractures.

[0021] The identification of filling types includes unfilled fractures, semi-filled fractures, fully filled fractures, and reticulated fractures composed of fractures at various angles. Unfilled fractures are the most favorable type of fractures for oil and gas production, appearing as sinusoidal black dark bands on the imaging map. Filled fractures are classified into fractures filled with low-resistivity substances such as shale and fractures filled with high-resistivity substances such as calcite and silica, appearing as bright and dark sinusoidal curves on the electrical imaging map respectively. Unfilled and low-resistivity substance-filled fractures appear as dark thin lines in an approximate sinusoidal curve shape or intermittent but still traceable dark sinusoidal curves on the imaging map. For high-resistivity substance-filled fractures, when there is a large electrical difference, such as fractures filled with calcite in argillaceous limestone, faint bright white sinusoidal curves appear on the imaging map. The calculation of fracture aperture can be directly read through the width of the dark or bright bands on the image. Based on the correlation between the fracture porosity measured by core experiments and the porosity calculated by logging, the fracture porosity calculated by logging is corrected. Then, the pore-permeability relationship of the core is established to obtain the calculation formulas for fracture porosity and permeability. The calculated fracture porosity and permeability can be compared with the porosity data measured by core experiments to verify the accuracy of the calculation results. For the calculation of fracture permeability, the correlation between the porosity and permeability of core experiments is established to obtain the calculation results of fracture porosity and fracture permeability based on electrical imaging logging.

[0022] Step S3: Combine production to determine the evaluation weights of each characteristic parameter in natural effective fractures.

[0023] Combined with the actual production capacity, determine the range values of the discrimination of various characteristic parameters for effective fractures in the actual study area and calculate the weights of each parameter. Conduct the discrimination of the developed sections of effective fractures, establish the effective fracture line density index, and form a complete set of effective fracture line density evaluation techniques and methods. For the well sections in stable production in the study area, obtain the characteristics of the dip angle, filling type, aperture, fracture porosity, fracture permeability, etc. of the natural fractures, analyze the relationship between these parameters and the actual production, and thus establish a method for identifying effective fractures with multiple parameters.

[0024] Secondly, for the weight calculation of the effective fracture discrimination formula, compare the correlations between the dip angle, filling type, fracture porosity, fracture permeability characteristics of fractures and the production capacity, and preferably use intelligent algorithms to calculate the weight values of each parameter in the effective fracture judgment. The effective fracture judgment calculation formula is as follows: I = W1 * I dip + W2 * I fi + W3 * I por + W4 * I perm Where: I - effective fracture discrimination coefficient, I dip - dip angle discrimination index, Ifi - Filling discriminant index, I por - Fracture porosity discriminant index, I perm - Fracture permeability discriminant index, W1 - 4 - Weight values of each discriminant index After determining the weight value of the effective fracture, when the judgment value is greater than the threshold value, this section is considered an effective fracture; when it is less than the threshold value, this section is defined as an ineffective fracture.

[0025] Finally, the effective natural fractures in the entire well section are discriminated, and the development of effective fractures in the entire well section is quantitatively described. The number of developed effective fractures is calculated by cumulative addition per meter, and the linear density of effective fractures is depicted in the entire well section. The greater the value of the linear density of effective fractures, the higher the degree of development of effective natural fractures in this interval, and thus it is easier to obtain high production. After combining the calculation of the fracture characteristics of the electrical image logging and the discrimination of effective fractures, the linear density of the developed effective fractures is compared with the fracture characteristics of the core observation section, which can verify the accuracy of the identification results again.

[0026] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments: Among them, Figure 1 are various geological data information in the example of the present invention, among which Figure 1 a is the core, Figure 1 b is lost circulation, Figure 1 c is gas logging, Figure 1 d is imaging, Figure 1 e is conventional logging; Figure 2 are the core fractures in the example of the present invention; Figure 3 are the thin section fractures in the example of the present invention; Figure 4 are the drilling and logging information in the example of the present invention; Figure 5 are the electrical image planar fractures in the example of the present invention; Figure 6 are the electrical image low - angle fractures in the example of the present invention; Figure 7 are the electrical image high - angle fractures in the example of the present invention; Figure 8 are the electrical image vertical fractures in the example of the present invention; Figure 9 are the electrical image unfilled fractures in the example of the present invention; Figure 10 are the electrical image filled fractures in the example of the present invention; Figure 11 is the calculation of the fracture characteristic parameters of the electrical image logging in the example of the present invention; Figure 12 is the comprehensive discrimination result in the example of the present invention.

[0027] Specifically, screen the information on the development and characteristics of natural fractures in various geological data, screen the information that can reflect the development degree and characteristics of natural fractures in various geological data such as cores, thin sections, drilling and logging, and use it as the data for constraining and verifying the fracture identification results of electrical image logging (attached Figure 1 ). Specifically, it includes: Identifying fracture characteristics from core data: Screen the cores in the target interval of the study area for observation, such asFigure 2 In the high-angle effective shear fractures, the number of fractures developed on the core can be directly identified manually as 5 fractures / m. And through measurement and observation, the characteristics such as fracture dip angle of about 80°, aperture of 0.2 mm and unfilled can be obtained. Then, experiments are carried out on the plug samples drilled from the core to measure the fracture porosity and fracture permeability parameters, so as to determine the basic characteristics of the fractures (attached Figure 2 ); Analyze thin-section data to identify fracture characteristics: The part of the core with natural fractures is made into thin sections for observation under a microscope, and the development degree, filling property and filling mineral type of microfractures can be determined (attached Figure 3 ); The specific process of identifying fracture characteristics from drilling and logging data: Drilling and logging data include wellbore losses, total hydrocarbon in gas logging and drilling time, etc., which can play a certain role in restricting fracture identification. For example, when there is a well loss interval, a high total hydrocarbon value in gas logging and an abnormal acceleration of drilling time, it indicates that there is a high possibility of fractures in this interval. However, due to being easily affected by underground environment and human factors, the accuracy of the results is generally average. Use the fracture parameters of the core and thin sections to formulate the interpretation standard of the resistivity image and conduct identification. After identification, the drilling and logging information and core thin sections can also be used to verify the accuracy of the resistivity image interpretation. (attached Figure 4 ).

[0028] Furthermore, identify and calculate the characteristics of natural effective fractures. Use the image recognition technology of resistivity image to identify and calculate the characteristics such as dip angle, filling property, aperture, fracture porosity, fracture permeability, etc. of natural fractures; The identification of natural fractures is manifested as a series of sinusoidal lines of discontinuous conductivity anomalies with wide and narrow changes and large amplitude changes on the resistivity image. According to the size of the amplitude difference, they are divided into flat fractures, low-angle fractures, high-angle fractures and vertical fractures. The flat fracture with an angle less than 15° has a sinusoidal image morphology on the resistivity image but with extremely small undulations (attached Figure 5 ); The low-angle fracture with an angle between 15° and 45° has a sinusoidal image with obvious undulations on the resistivity image (attached Figure 6 ); The high-angle fracture with an angle between 45° and 75° has an even larger amplitude difference on the resistivity image (attached Figure 7 ); The vertical fracture has a more obvious amplitude difference (attached Figure 8 ).

[0029] The identification of filling property includes unfilled fractures, semi-filled fractures, fully filled fractures and network fractures composed of fractures with various angles. The unfilled fractures are the most favorable type of fractures for oil and gas exploitation, and are manifested as black dark strip bands of sinusoidal waveforms on the imaging map; The unfilled fractures appear as a dark sinusoidal line in the bright imaging section (attached Figure 9 ); On the contrary, the imaging image of high-resistance filling shows an obvious bright sinusoidal line pattern in the dark section (attached Figure 10 ).

[0030] The filled fractures can be divided into fractures filled with low-resistivity materials such as shale and fractures filled with high-resistivity materials such as calcite and silica, which are respectively shown as bright and dark sine curves on the electrical imaging log. The unfilled and low-resistivity-filled fractures appear as dark thin lines in an approximate sine curve shape or intermittent but still traceable dark sine curves on the imaging log; for fractures filled with high-resistivity materials, when there is a large electrical difference, such as fractures filled with calcite in argillaceous limestone, faint bright white sine curves appear on the imaging log. For the calculation of fracture aperture, it can be directly read from the width of the dark or bright bands on the image. (Appendix Figure 11 ).

[0031] According to the test experiments, samples are selected to conduct linear regression on porosity, permeability and conventional logging curve parameters to obtain the calculation model of porosity and permeability in the area. Based on the comprehensive characterization of the above-mentioned multiple geological information, the application accuracy of the interpretation results of the presence, dip angle, aperture, line density, etc. of fractures on electrical imaging reaches more than 80%.

[0032] Furthermore, in combination with production, the evaluation weights of each characteristic parameter in natural effective fractures are established. Combining the actual production capacity, the range values of various characteristic parameters for the discrimination of effective fractures in the actual study area are determined, the weights of each parameter are calculated, the discrimination of the developed sections of effective fractures is carried out, and an effective fracture line density index is established to form a complete set of effective fracture line density evaluation techniques and methods. For the well sections in stable production in the study area, the dip angle, filling property, aperture, fracture porosity, fracture permeability and other characteristics of its natural fractures are obtained, and the relationship between these parameters and the actual production is analyzed, so as to establish a method for identifying effective fractures with multiple parameters. First, analyze from single factors. Generally speaking, when the dip angle is greater than 30°, fractures can generally produce, and when it is greater than 60°, certain medium and high yields can be obtained. Therefore, the calculation results are assigned values according to the boundary range. For example, when the dip angle is greater than 60°, the assigned value is 1, when the dip angle is greater than 30° and less than 60°, the assigned value is 0.5, and when the dip angle is less than 30°, the assigned value is 0. The above assignment based on the dip angle range is only general, and there may be certain differences in different oil and gas development areas, and more accurate ranges need to be determined based on the dip angle values of the fractures in the actual production sections obtained; the same is true for the filling property. Unfilled represents effective fractures and is assigned a value of 1, semi-filled represents relatively effective fractures and is assigned a value of 0.5, and fully filled represents ineffective fractures and is assigned a value of 0; the aperture has been reflected in the calculation of fracture porosity, so only fracture porosity and fracture permeability parameters are considered. However, due to the huge differences in porosity and permeability under different lithologies and geological backgrounds, their assignment ranges need to be determined according to the fracture porosity and permeability in the actual production in the study area. Combining multiple geological information constraint conditions, the corresponding line density evaluation index is calculated for the wellbore, well wall and near-wellbore.

[0033] Secondly, for the weight calculation of the effective fracture discrimination formula, the correlations between the dip angle, filling property, fracture porosity, fracture permeability characteristics of fractures and production capacity are compared, and an optimized intelligent algorithm is proposed to calculate the weight values of each parameter in the determination of effective fractures. The calculation formula for judging the development degree of the effective fracture line density is as follows: I = W1 * Idip + W2 * Ifi + W3 * Ipor + W4 * Iperm, where: I - effective fracture discrimination coefficient, Idip - dip angle discrimination index, Ifi - filling property discrimination index, Ipor - fracture porosity discrimination index, Iperm - fracture permeability discrimination index, W1 - 4 - weight values of each discrimination index.

[0034] Based on the discrimination of the formula, after removing the underdeveloped sections, the number of developed sections is accumulated to obtain the finally determined line density value of the effective fracture line density. According to the reservoir fractures in the study area, the characteristics of actual production gas testing, gas logging total hydrocarbon and TOC of drilling and logging, after determining the weight value of the effective fracture, when the judgment value is greater than 1.8, this section is considered to be a section with a high development degree of effective fracture line density; when it is less than 1.8, this section is defined as a section with an underdeveloped effective fracture line density.

[0035] Using the calculation and discrimination formula, the development degree of the effective line density of natural fractures in the whole well section is discriminated, and the development of the effective fracture line density in the whole well section is quantitatively described. The number of developed fractures is calculated by accumulation per meter, and the line density of the effective fractures is depicted in the whole well section. The greater the value of the effective fracture line density, the higher the development degree of the effective natural fractures in this section, and thus it is easier to obtain high production. In the study area, a total of 50 wells were interpreted using this method. The self-verification compliance rate was 83%, and the prediction compliance rate with the actual gas logging total hydrocarbon results was 77%. After combining the calculation of the electrical imaging fracture characteristics and the effective fracture discrimination, the effective fracture development line density is compared with the fracture characteristics of the core observation section, which can verify the accuracy of the identification results again, and the interpretation and application accuracy is over 80%. (Appendix Figure 12 )

[0036] The geological information-constrained evaluation technology for the effective fracture line density of borehole wall logging provided by the present invention aims to solve the problems existing in the prior art for natural fracture identification, such as the lack of processing and constraint of actual geological information and the lack of accuracy in the identification results. An effective fracture judgment formula is established to achieve multi-parameter quantitative evaluation, and a comprehensive and unified index is adopted, thereby realizing the accurate identification of effective natural fractures. By analyzing the core fracture characteristics, data on the development degree and fracture characteristics of natural fractures with high accuracy are obtained; by observing thin sections, the development degree, filling property and filling mineral type of microfractures are determined; the development degree of effective fractures is indirectly reflected to a certain extent through drilling and logging data; with the above various types of geological information as constraint conditions and subsequent conclusion verification, the accuracy of the evaluation of effective fractures in electrical imaging logging is ensured. The various characteristics of fractures are identified and calculated, and based on the production capacity of the actual study area, the parameter range and weight value of various fracture characteristics in the discrimination of effective fractures are determined, an effective fracture judgment formula is established, and finally the development line density value of effective fractures is calculated, realizing the multi-parameter quantitative evaluation of effective fractures. The adoption of a comprehensive and unified index has important guiding significance for the well location deployment and preferred development interval of oil and gas fields.

[0037] The above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. Those of ordinary skill in the relevant art can also make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also belong to the scope of the present invention. The patent protection scope of the present invention shall be defined by the claims.

Claims

1. A method for evaluating the effective fracture line density of borehole wall logging constrained by geological information, characterized in that, it includes: Step S1: Screen the natural fracture development and characteristic information in various geological data as the data for constraining and verifying the fracture identification results of resistivity imaging logging; Step S2: Use the image recognition technology of resistivity imaging to identify and calculate the characteristics of natural effective fractures; Step S3: Combine production, establish the evaluation weights of each characteristic parameter in the evaluation of natural effective fractures, judge the developed sections of effective fractures, and establish an effective fracture line density index.

2. The method for evaluating the effective fracture line density of borehole wall logging constrained by geological information according to claim 1, characterized in that, Step S1 specifically includes: Screen the information in various geological data such as cores, thin sections, drilling records that can reflect the development degree and characteristics of natural fractures, and use it as the data for constraining and verifying the fracture identification results of resistivity imaging logging; Among them, the fracture characteristics are identified from core data: Screen the cores of the target interval in the study area for observation, directly manually identify the number of fractures developed on the core, and through measurement and observation, the characteristics of fracture dip angle, aperture and filling can be obtained. Then, conduct experiments on the plug samples drilled from the core to measure the fracture porosity and fracture permeability parameters, so as to determine the basic characteristics of the fractures; The fracture characteristics are identified from thin section data: Make samples of the key parts with natural fractures in the core into thin sections and observe them under a microscope to determine the development degree, filling and filling mineral types of microfractures; The specific process of identifying fracture characteristics from drilling records data: Drilling records data including wellbore loss, total hydrocarbon in gas logging and drilling time can play a role in constraining fracture identification.

3. The method for evaluating the effective fracture line density of borehole wall logging constrained by geological information according to claim 1, characterized in that, Step S2 specifically includes: Identify and calculate the characteristics of natural fracture dip angle, filling, aperture, fracture porosity and fracture permeability; The identification of natural fractures appears as a series of sinusoidal lines of conductance anomalies with variable widths and large amplitude changes and can be interrupted on the resistivity imaging image, and are divided into flat fractures, low-angle fractures, high-angle fractures and vertical fractures according to the magnitude of the amplitude difference.

4. The identification of filling includes unfilled fractures, semi-filled fractures, fully filled fractures and reticulated fractures composed of fractures at different angles; Unfilled fractures are the most favorable type of fractures for oil and gas production, and appear as black dark bands in the form of sine waves on the imaging map; Filled fractures are divided into fractures filled with low-resistivity substances such as shale and fractures filled with high-resistivity substances such as calcite and silica according to the type of filling substances in the fractures, and appear as bright and dark sine curves on the resistivity imaging map respectively; Unfilled and low-resistivity substance-filled fractures appear as dark thin lines or intermittent but traceable dark sine curves in an approximate sine curve shape on the imaging map; For fractures filled with high-resistivity substances, when there is a large electrical difference, a faintly distinguishable bright white sine curve appears on the imaging map.

5. The method for evaluating the effective fracture line density of borehole wall logging constrained by geological information according to claim 3, characterized in that, Step S2 further includes: For the calculation of fracture aperture, directly read the width of the dark or bright band on the image.

6. The method for evaluating the effective fracture line density of borehole wall logging constrained by geological information as described in claim 3, characterized in that, step S2 further includes: Based on the correlation between the fracture porosity measured by core experiment and the porosity calculated by logging, the fracture porosity calculated by logging is corrected; then the pore-permeability relationship of the core is established to obtain the calculation formulas for fracture porosity and permeability. After calculation, the fracture porosity and permeability are compared with the porosity data measured by core experiment to verify the accuracy of the calculation results.

7. The method for evaluating the effective fracture line density of borehole wall logging constrained by geological information as described in claim 5, characterized in that, step S2 further includes: Calculation of fracture permeability. The correlation between the porosity and permeability of core experiment is established to obtain the calculation results of fracture porosity and fracture permeability based on electrical imaging logging.

8. The method for evaluating the effective fracture line density of borehole wall logging constrained by geological information as described in claim 3, characterized in that, step S3 specifically includes: Step S31: Combining the actual production capacity, determining the range values of various characteristic parameters for the discrimination of effective fractures in the actual study area and calculating the weights of each parameter, conducting the discrimination of the effective fracture development section, establishing the effective fracture line density index, and forming a complete set of effective fracture line density evaluation techniques and methods; For the well sections in stable production in the study area, obtain the dip angle, filling property, aperture, fracture porosity, and fracture permeability characteristics of its natural fractures, analyze the relationship between these parameters and the actual production, and thus establish a multi-parameter method for identifying effective fractures; Step S32: For the weight calculation of the effective fracture discrimination formula, compare the correlation between the dip angle, filling property, fracture porosity, fracture permeability characteristics of fractures and the production capacity, and calculate the weight values of each parameter in the effective fracture judgment through intelligent algorithms; Step S33: Discriminate the effective natural fractures in the whole well section, quantitatively describe the development of effective fractures in the whole well section, calculate the number of developed effective fractures by cumulative calculation per meter, and depict the line density of effective fractures in the whole well section. The greater the value of the effective fracture line density, the higher the development degree of the effective natural fractures in this section; After combining the calculation of electrical imaging fracture characteristics and the discrimination of effective fractures, the line density of effective fracture development is compared with the fracture characteristics of the core observation section, which can verify the accuracy of the identification results again.

9. The method for evaluating the effective fracture line density of borehole wall logging constrained by geological information as described in claim 7, characterized in that, The calculation formula for effective fracture judgment in step S32 is as follows: I = W1 * I dip+ W2 * I fi + W3 * I por + W4 * I perm Where: I - effective fracture discrimination coefficient, I dip - dip discrimination index, I fi - filling discrimination index, I por - fracture porosity discrimination index, I perm - fracture permeability discrimination index, W1, W2, W3, W4 - weight values of each discrimination index; After determining the weight value of the effective fracture judgment, when the judgment value is greater than the threshold value, it is considered that this section belongs to the effective fracture; when it is less than the threshold value, this section is defined as an ineffective fracture.

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