A method, system, device and medium for identifying a post-fracturing network morphology of a reservoir

Abstract

The application discloses a kind of reservoir postflowing fracture network form identification method, system, device and medium, method includes: obtaining the first apparent resistivity data of purpose reservoir before fracturing and the second apparent resistivity data after postflowing;Purpose reservoir is divided into multiple purpose small layer by small layer equal thickness, and multiple first resistivity plane distribution diagram is extracted from the first apparent resistivity data, and multiple second resistivity plane distribution diagram is extracted from the second apparent resistivity data;Multiple apparent resistivity drop amplitude value plane distribution diagram before and after postflowing is calculated;According to resistivity reduction amplitude, lithology weighting parameter, target thickness and flattening thickness, drop amplitude boundary value is calculated;If not in dominant reconstruction area, drop amplitude boundary value is first boundary value, if in, for second boundary value;The part surrounded by drop amplitude boundary value is determined as apparent resistivity drop amplitude change main body, and the part surrounded by drop amplitude boundary value and the zero value of resistivity reduction amplitude is determined as predicted fracture zone.The application can improve the accuracy of fracture network form identification.

Description

Technical Field

[0001] This invention relates to the technical field of oil and gas exploration and development, and in particular to a method, system, device and medium for identifying fracture network morphology after reservoir backflow. Background Technology

[0002] In the exploration and development of unconventional oil and gas resources, continental shale oil reservoirs suffer from complex mineral composition and pore structure, well-developed bedding, strong heterogeneity, and extremely poor physical properties. Under natural conditions, shale reservoir productivity is extremely low, and traditional methods and technologies are insufficient to achieve economical extraction. It is necessary to maximize reservoir extraction efficiency by increasing fracturing intensity to overcome the principal stress difference in the formation, guiding fractures to change direction and connect with natural fractures, and opening natural weak surfaces. Although practical applications have proven that volumetric fracturing using horizontal wells in multiple clusters to closely fragment the reservoir has some effect, engineering problems remain when dealing with continental shale oil reservoirs with widespread natural fractures. These problems include low fracture network complexity and difficulty in predicting the post-fracturing fracture network morphology. It is also difficult to accurately describe the extension patterns of multiple fractures during hydraulic fracturing and the geometric morphology of the fracture network formed by their intersection with natural fractures. Therefore, it is necessary to conduct research on new methods and technologies for characterizing the fracture network morphology of shale oil reservoirs, providing theoretical support for increasing reservoir stimulation volume in field fracturing operations and providing a basis for adjusting shale oil development plans. Summary of the Invention

[0003] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a method for identifying fracture network morphology after reservoir flowback, which can improve the accuracy of fracture network morphology identification.

[0004] The present invention also provides a reservoir flowback fracture morphology recognition system, a control device for performing the above-described reservoir flowback fracture morphology recognition method, and a computer-readable storage medium.

[0005] A method for identifying fracture network morphology after reservoir flowback according to a first aspect of the present invention, the method comprising:

[0006] Acquire the first apparent resistivity data of the target reservoir in the study area before fracturing and the second apparent resistivity data after flowback, as well as the elevation data of the target reservoir;

[0007] Based on the altitude data, the target reservoir is divided into equal-thickness sub-layers to obtain G target sub-layers with thickness values ​​of the target thickness;

[0008] Extract the corresponding data of G+1 planes corresponding to G target sub-layers from the first apparent resistivity data to obtain G+1 first resistivity plane distribution maps. Extract the corresponding data of G+1 planes corresponding to G target sub-layers from the second apparent resistivity data to obtain G+1 second resistivity plane distribution maps.

[0009] The resistivity reduction after the return is calculated based on G+1 of the first resistivity plane distribution maps and G+1 of the second resistivity plane distribution maps, so as to obtain G+1 plane distribution maps of the apparent resistivity reduction values ​​before and after the return.

[0010] The reduction boundary value corresponding to each apparent resistivity reduction value plane distribution map is calculated based on the resistivity reduction magnitude, the preset lithology weighted parameters, the target thickness, and the leveling thickness; wherein, the leveling thickness is the thickness value corresponding to the thickness of the target sublayer after removing the ineffective lithological interlayer; if the apparent resistivity reduction value plane distribution map is not located in the dominant modification zone, the reduction boundary value is the first boundary value; if it is located in the dominant modification zone, the reduction boundary value is the second boundary value; the dominant modification zone characterizes the shale oil enrichment area;

[0011] The portion enclosed by the reduction boundary value is defined as the main body of the apparent resistivity reduction change, and the portion enclosed by the reduction boundary value and the zero value of the resistivity reduction is defined as the predicted fracture zone. Based on the predicted fracture zone, the fracture network morphology identification after the target reservoir is completed.

[0012] The reservoir flowback fracture morphology identification method according to embodiments of the present invention has at least the following beneficial effects:

[0013] By dividing the target reservoir into equal-thickness sublayers and then analyzing the resistivity reduction before and after the backflow of G+1 resistivity plane distribution maps corresponding to G target sublayers, the resistivity change along the formation trend of the target reservoir can be intuitively displayed. This improves the accuracy of fracture network morphology identification in shale oil reservoirs where horizontal homogeneity is significantly stronger than vertical homogeneity. By using a reduction boundary value that conforms to the actual reservoir physical properties and is related to the actual lithological distribution of the reservoir as the boundary line for fracturing fluid distribution area delineation, the relevant parameters of the actual lithological conditions of the formation in the study area can be effectively characterized to reduce the adverse effects of rock physical properties on resistivity. This yields results that are closer to the actual situation, making fracture network morphology identification more accurate. In particular, the rock physical property parameters of the dominant and non-dominantly modified areas differ significantly, and the calculation of the reduction boundary value classifies and discusses whether it is in a dominantly modified area, which is more in line with the actual situation of the study area, resulting in higher accuracy in fracture network morphology identification.

[0014] According to some embodiments of the present invention, the advantageous modification region is determined by the following steps:

[0015] Obtain resistivity data and corresponding well logging data of multiple production wells in the study area, wherein the well logging data includes multiple logging parameters;

[0016] Correlation fitting is performed on multiple production well resistivity data and corresponding multiple production well logging data to obtain the fitting relationship between resistivity and multiple logging parameters;

[0017] Based on the fitted relationship, the critical values ​​of multiple logging parameters of the formation in the shale oil enrichment area within the study area are obtained to determine the lower limit of resistivity of the shale oil enrichment area within the study area.

[0018] The dominant modification region is determined in G+1 resistivity plane distribution maps based on the lower resistivity limit, and the dominant modification region is the area in the resistivity plane distribution map where the resistivity is greater than the lower resistivity limit.

[0019] According to some embodiments of the present invention, the plurality of logging parameters are effective porosity, water saturation, free hydrocarbon content, and organic carbon content, and the constraint formula of the fitting relationship is:

[0020] ;

[0021] in, Resistivity and These are the preset calculation parameters. e It is a mathematical natural constant. The effective porosity, The water saturation level is [value missing]. The free hydrocarbon content, TOC The organic carbon content is mentioned above. The coefficient of determination for the free hydrocarbon content is given. The coefficient of determination for the organic carbon content is denoted as . m The preset porosity index, k This is the preset saturation index.

[0022] According to some embodiments of the present invention, obtaining critical values ​​of multiple logging parameters of the formation in the shale oil-rich area within the study area based on the fitted relationship, in order to determine the lower limit of resistivity of the shale oil-rich area within the study area, includes:

[0023] The upper limit of effective porosity, upper limit of water saturation, lower limit of free hydrocarbon content, and lower limit of organic carbon content of the shale oil-rich strata in the study area were obtained.

[0024] The lower limit of resistivity is determined based on the fitted relationship, the upper limit of effective porosity, the upper limit of water saturation, the lower limit of free hydrocarbon content, and the lower limit of organic carbon content.

[0025] According to some embodiments of the present invention, the constraint formula for the first boundary value is:

[0026] ;

[0027] = ;

[0028] The constraint formula for the second boundary value is:

[0029] ;

[0030] in, The magnitude of the decrease in resistivity, n The target thickness is... For the aforementioned flattened thickness, E These are preset lithology weighted parameters. The average thickness of the interlayer in the shale. The average thickness of the mudstone interlayers. The average thickness of the sandstone interlayers. This is the sum of the average interlayer thicknesses of carbonate rocks. For shale, the weighting coefficient is... For mudstone, the weighting coefficient is... For sandstone, the weighting coefficient is... This represents the weighting coefficient for carbonate rocks.

[0031] According to some embodiments of the present invention, the constraint formula for the target thickness is:

[0032] ;

[0033] in, n The target thickness is... N The thickness of the target reservoir, For effective porosity, m The preset porosity index, k The preset saturation index, a and b All are preset lithology coefficients.

[0034] According to some embodiments of the present invention, the identification of fracture network morphology after the target reservoir flowback is completed based on the predicted fracture zone includes:

[0035] The planar distribution map of each apparent resistivity reduction value is further subdivided into grids;

[0036] The spatial location, width, and length of the predicted crack zone are determined based on the number and location of grid points in the planar distribution map of each apparent resistivity reduction value.

[0037] Based on the spatial location, width, and length of all predicted fracture zones in the G+1 apparent resistivity reduction value plane distribution map, the fracture network morphology identification after the target reservoir flowback is completed.

[0038] A reservoir flowback fracture morphology identification system according to a second aspect of the present invention, the system comprising:

[0039] The data acquisition unit is used to acquire the first apparent resistivity data of the target reservoir in the study area before fracturing and the second apparent resistivity data after flowback, as well as the altitude data of the target reservoir.

[0040] The sublayer division unit is used to divide the target reservoir into sublayers of equal thickness based on the altitude data, so as to obtain G target sublayers with thickness values ​​of the target thickness.

[0041] The small-layer resistivity extraction unit is used to extract the corresponding data of G+1 planes corresponding to G target small layers from the first apparent resistivity data to obtain G+1 first resistivity plane distribution maps, and to extract the corresponding data of G+1 planes corresponding to G target small layers from the second apparent resistivity data to obtain G+1 second resistivity plane distribution maps.

[0042] The resistivity reduction magnitude determination unit is used to calculate the resistivity reduction magnitude after the return based on G+1 first resistivity plane distribution maps and G+1 second resistivity plane distribution maps, so as to obtain G+1 apparent resistivity reduction magnitude plane distribution maps before and after the return.

[0043] The reduction boundary value calculation unit is used to calculate the reduction boundary value corresponding to each apparent resistivity reduction value plane distribution map based on the resistivity reduction magnitude, preset lithology weighting parameters, target thickness, and leveling thickness; wherein, the leveling thickness is the thickness value corresponding to the thickness of the target sublayer after removing the ineffective lithological interlayer; if the apparent resistivity reduction value plane distribution map is not located in the dominant modification zone, the reduction boundary value is the first boundary value; if it is located in the dominant modification zone, the reduction boundary value is the second boundary value; the dominant modification zone characterizes the shale oil enrichment area;

[0044] The fracture network identification unit is used to determine the part enclosed by the reduction boundary value as the main body of the apparent resistivity reduction change, and to determine the part enclosed by the reduction boundary value and the zero value of the resistivity reduction as the predicted fracture zone, and to complete the fracture network morphology identification after the target reservoir is flowed back based on the predicted fracture zone.

[0045] The reservoir flowback fracture morphology recognition system according to embodiments of the present invention has at least the following beneficial effects:

[0046] By dividing the target reservoir into equal-thickness sublayers and then analyzing the resistivity reduction before and after the backflow of G+1 resistivity plane distribution maps corresponding to G target sublayers, the resistivity change along the formation trend of the target reservoir can be intuitively displayed. This improves the accuracy of fracture network morphology identification in shale oil reservoirs where horizontal homogeneity is significantly stronger than vertical homogeneity. By using a reduction boundary value that conforms to the actual reservoir physical properties and is related to the actual lithological distribution of the reservoir as the boundary line for fracturing fluid distribution area delineation, the relevant parameters of the actual lithological conditions of the formation in the study area can be effectively characterized to reduce the adverse effects of rock physical properties on resistivity. This yields results that are closer to the actual situation, making fracture network morphology identification more accurate. In particular, the rock physical property parameters of the dominant and non-dominantly modified areas differ significantly, and the calculation of the reduction boundary value classifies and discusses whether it is in a dominantly modified area, which is more in line with the actual situation of the study area, resulting in higher accuracy in fracture network morphology identification.

[0047] A control device according to a third aspect embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the reservoir flowback fracture network morphology identification method as described in the first aspect embodiment above. Since the control device employs all the technical solutions of the reservoir flowback fracture network morphology identification method of the above embodiments, it possesses at least all the beneficial effects brought about by the technical solutions of the above embodiments.

[0048] A computer-readable storage medium according to a fourth aspect embodiment of the present invention stores computer-executable instructions for performing the reservoir flowback fracture network morphology identification method as described in the first aspect embodiment above. Since the computer-readable storage medium employs all the technical solutions of the reservoir flowback fracture network morphology identification method of the above embodiments, it possesses at least all the beneficial effects brought about by the technical solutions of the above embodiments.

[0049] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. Attached Figure Description

[0050] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0051] Figure 1 This is a flowchart of a reservoir flowback fracture morphology identification method according to an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of a resistivity profile according to an embodiment of the present invention;

[0053] Figure 3 This is a schematic diagram of the resistivity plane diagram according to an embodiment of the present invention;

[0054] Figure 4 This is a schematic diagram of the fitting curve of resistivity with the power parameters of effective porosity and water saturation in the fitting relationship of an embodiment of the present invention.

[0055] Figure 5 This is a schematic diagram of the advantageous modification area according to an embodiment of the present invention;

[0056] Figure 6 This is a schematic diagram of mesh recognition according to an embodiment of the present invention. Detailed Implementation

[0057] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0058] In the description of this invention, the use of terms such as "first," "second," etc., is for the purpose of distinguishing technical features only and should not be construed as indicating or implying relative importance, or implicitly indicating the number of technical features indicated, or implicitly indicating the order of the technical features indicated.

[0059] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, etc., are based on the orientation or positional relationship shown in the drawings and are only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0060] In the description of this invention, it should be noted that, unless otherwise explicitly defined, terms such as "setting," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0061] The following will combine Figures 1 to 6 The reservoir flowback fracture morphology identification method of the present invention will be clearly and completely described in the embodiments of the present invention. Obviously, the embodiments described below are some embodiments of the present invention, not all embodiments.

[0062] refer to Figures 1 to 6 , Figure 1This is a flowchart of a reservoir flowback fracture morphology identification method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a resistivity profile according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the resistivity plane diagram according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the fitting curve of resistivity with the power parameters of effective porosity and water saturation in the fitting relationship of an embodiment of the present invention. Figure 5 This is a schematic diagram of the advantageous modification area according to an embodiment of the present invention; Figure 6 This is a schematic diagram of mesh recognition according to an embodiment of the present invention.

[0063] A method for identifying the fracture network morphology after reservoir flowback according to a first aspect of the present invention includes:

[0064] Acquire the first apparent resistivity data of the target reservoir in the study area before fracturing and the second apparent resistivity data after flowback, as well as the elevation data of the target reservoir;

[0065] Based on altitude data, the target reservoir is divided into equal-thickness sub-layers to obtain G target sub-layers with thickness values ​​of the target thickness;

[0066] Extract the corresponding data of G+1 planes corresponding to G target sublayers from the first apparent resistivity data to obtain G+1 first resistivity plane distribution maps. Extract the corresponding data of G+1 planes corresponding to G target sublayers from the second apparent resistivity data to obtain G+1 second resistivity plane distribution maps.

[0067] The resistivity reduction after the return is calculated based on the G+1 first resistivity plane distribution map and the G+1 second resistivity plane distribution map, so as to obtain the G+1 apparent resistivity reduction value plane distribution map before and after the return.

[0068] The reduction boundary value corresponding to each apparent resistivity reduction value plane distribution map is calculated based on the resistivity reduction magnitude, preset lithology weighted parameters, target thickness, and leveling thickness. Among them, the leveling thickness is the thickness value of the corresponding target sublayer after removing the ineffective lithological interlayer. If the apparent resistivity reduction value plane distribution map is not in the dominant modification zone, the reduction boundary value is the first boundary value; if it is in the dominant modification zone, the reduction boundary value is the second boundary value. The dominant modification zone characterizes the shale oil enrichment area.

[0069] The area enclosed by the reduction boundary value is defined as the main body of the apparent resistivity reduction change, and the area enclosed by the reduction boundary value and the zero value of the resistivity reduction is defined as the predicted fracture zone. Based on the predicted fracture zone, the fracture network morphology identification after the target reservoir is completed.

[0070] Apparent resistivity data is obtained through the following steps:

[0071] To acquire electromagnetic field information, seismic data, and geological data for the study area;

[0072] An initial geoelectric model is established based on electromagnetic field information, seismic data, and geological data. A high-precision wide-area electromagnetic inversion is then performed on the initial geoelectric model to obtain the inverted apparent resistivity distribution profile of the study area, thereby acquiring the apparent resistivity data of the target reservoir in the study area.

[0073] Altitude data is obtained through the following steps:

[0074] Acquire seismic data and well logging data for the study area;

[0075] The elevation range of the target reservoir is determined based on seismic data and well logging data, and this elevation range is recorded as elevation data.

[0076] It should be noted that the elevation range refers to the elevation data that can describe the top and bottom interfaces of the target reservoir at any location within the study area. The elevation of the top and bottom interfaces of the target reservoir changes continuously with geographical location and stratigraphic trend, rather than remaining constant. The elevation data of the target reservoir can be represented by coordinate points (x, y, z), where x and y are the x and y values ​​of the geodetic coordinate system at that location, and z is the corresponding elevation depth.

[0077] The constraint formula for the target thickness is:

[0078] ;Formula (1)

[0079] in, n For the target thickness, N For the thickness of the target reservoir, For effective porosity, m The preset porosity index, k The preset saturation index, a and b All are preset lithology coefficients. The thickness of the target reservoir is obtained based on elevation data. Effective porosity, porosity index, saturation index, and lithology coefficients can all be obtained from existing geological and seismic data of the study area. G equals N / n .

[0080] It is understandable that the target sublayer is a virtual sublayer obtained by dividing the target reservoir into equal parts according to a certain thickness, and it does not exist in the actual formation environment. The empirical formula for dividing virtual layers shows that the thickness of a virtual layer is only related to the actual reservoir thickness, porosity, and lithology. There are two main reasons for this treatment: First, anisotropy is one of the main characteristics of shale oil reservoirs. Due to the influence of the reservoir system, natural fractures, and both organic and inorganic origins, the degree of porosity and fracture development in shale reservoirs varies greatly in the vertical and horizontal directions. In the horizontal direction, highly developed foliation fractures can even form high-porosity and high-permeability zones in shallower strata (the depth of which "shallower strata" depends on the actual geological conditions; for example, a pure shale-type shale oil reservoir in a certain basin in China typically represents an area shallower than -2000m). Horizontal porosity and permeability can be hundreds of times greater than vertical porosity, resulting in extremely strong vertical heterogeneity in shale reservoirs. A difference of several meters in altitude can lead to significant differences in physical properties. Therefore, when analyzing the lithological and physical properties of a shale oil reservoir, the thickness of a virtual layer (tens of meters or even hundreds of meters) is considered. It is unreasonable to study reservoirs as a whole. Instead, dividing and classifying reservoirs into smaller layers based on their actual lithology and physical properties is beneficial for refining the evaluation of their hydrocarbon potential. Secondly, as the development and research of unconventional oil and gas resources in China deepens, the level of refinement in reservoir hydrocarbon potential evaluation is also increasing. Refining the vertical resolution of reservoir information and improving its accuracy is one of the main research directions for Chinese scholars. Virtual layers do not exist in reality. The significance of dividing virtual layers is to combine the microscopic analysis results of the layers with the macroscopic analysis results of the entire reservoir, thereby achieving a more scientific and reasonable evaluation of reservoir hydrocarbon potential. This can be understood as a classification analysis method. By studying each virtual layer individually and then combining them into a whole, a comprehensive understanding of the reservoir can be formed. This method is particularly suitable for shale oil reservoirs where horizontal homogeneity is significantly stronger than vertical homogeneity.

[0081] For understanding the G target sublayers and G+1 resistivity plane distribution maps, refer to... Figure 3 , Figure 3 This is a schematic diagram of a resistivity plane diagram according to an embodiment of the present invention. If the number of target sub-layers is 2, divided into upper and lower sub-layers, then there are three resistivity plane distribution diagrams, namely the top boundary of the upper sub-layer, the boundary between the upper and lower sub-layers, and the bottom boundary of the lower sub-layer.

[0082] By dividing the target reservoir into equal-thickness sub-layers and then analyzing the resistivity reduction before and after the return of the G+1 resistivity plane distribution maps corresponding to the G target sub-layers, the change of resistivity of the target reservoir along the formation trend can be intuitively displayed. In shale oil reservoirs where horizontal homogeneity is significantly stronger than vertical homogeneity, the accuracy of fracture network morphology identification can be improved.

[0083] As we can understand, flowback refers to the process where, after fracturing fluid is injected underground to achieve the fracturing objective, the formation pressure drops and some fractures close after fracturing operations cease. A large amount of formation water, drill cuttings, and remaining fracturing fluid are then flowed back to the surface; this is collectively known as fracturing flowback fluid. The flowback process typically lasts for several months. Furthermore, flowback efficiency is an important parameter for evaluating fracturing effectiveness. A higher flowback rate means less fracturing fluid remains in the formation, resulting in less damage to the formation and greater oil and gas production. Generally, engineers can control the flowback flow rate to ensure better proppant placement within the fractures. In essence, the goals of fracturing fluid flowback and fracturing operations are the same: to obtain fractures with high conductivity.

[0084] In some embodiments, one or more production wells with logging data in the study area are selected for fracturing operations. The resistivity reduction after flowback is calculated based on G+1 first resistivity plane distribution maps and G+1 second resistivity plane distribution maps to obtain G+1 apparent resistivity reduction value plane distribution maps before and after flowback.

[0085] The constraint formula for the magnitude of resistivity reduction is:

[0086] ;Formula (2)

[0087] in, The magnitude of the decrease in resistivity, The resistivity before fracturing. The resistivity is the value after the material is returned to its original state.

[0088] In some embodiments of the present invention, reference is made to Figure 2 , Figure 4 and Figure 5 The advantageous redevelopment areas are determined through the following steps:

[0089] Obtain resistivity data and corresponding well logging data of multiple production wells in the study area. The well logging data includes multiple logging parameters.

[0090] Correlation fitting was performed on resistivity data from multiple production wells and corresponding logging data from multiple production wells to obtain the fitting relationship between resistivity and multiple logging parameters.

[0091] The critical values ​​of multiple logging parameters of the formation in the shale oil enrichment area within the study area are obtained based on the fitting relationship, so as to determine the lower limit of resistivity of the shale oil enrichment area within the study area.

[0092] Based on the lower limit of resistivity, the dominant modification areas are determined in the G+1 resistivity plane distribution maps. The dominant modification areas are the regions in the resistivity plane distribution maps where the resistivity is greater than the lower limit of resistivity.

[0093] It should be noted that the reference Figure 2 The resistivity data of multiple production wells are extracted by selecting several apparent resistivity profiles. All apparent resistivity profiles must pass through one or more production wells in the study area that have logging data.

[0094] In some embodiments of the present invention, multiple logging parameters are effective porosity, water saturation, free hydrocarbon content, and organic carbon content, and the constraint formula for the fitting relationship is:

[0095] ;Formula (3)

[0096] in, Resistivity and These are the preset calculation parameters. e It is a mathematical natural constant. For effective porosity, Water saturation This refers to the free hydrocarbon content. TOC Organic carbon content, The coefficient of determination for free hydrocarbon content, The coefficient of determination for organic carbon content. m The preset porosity index, k This is the preset saturation index.

[0097] Correlation fitting was performed using resistivity data from multiple production wells and corresponding logging data from multiple production wells. The coefficient of determination of the fitted curve was then obtained based on the fitting results. The coefficient of determination is That is, the correlation coefficient. R The square of.

[0098] The power parameter of effective porosity and water saturation Recorded as x . refer to Figure 4 The resistivity is calculated and fitted according to formula (3) until the calculation is completed. Power parameter of effective porosity and water saturation x coefficient of determination between Reaching above 0.9. It is worth noting that when the free hydrocarbon content... Organic carbon content TOC When the coefficient of determination of each factor is less than 0.5, they will be judged as having no correlation. In this case, the formula... , TOC It was assigned the value 0.

[0099] In some embodiments of the present invention, critical values ​​of multiple logging parameters of shale oil-rich formations within the study area are obtained based on fitting relationships to determine the lower limit of resistivity of shale oil-rich areas within the study area, including:

[0100] To obtain the upper limit of effective porosity, upper limit of water saturation, lower limit of free hydrocarbon content, and lower limit of organic carbon content of strata in shale oil-rich areas within the study area;

[0101] The lower limit of resistivity is determined based on the fitted relationship, the upper limit of effective porosity, the upper limit of water saturation, the lower limit of free hydrocarbon content, and the lower limit of organic carbon content.

[0102] Understandably, the higher the effective porosity and upper limit of water saturation in the shale oil-rich areas of the study region, the lower the lower limits of free hydrocarbon content and organic carbon content, resulting in a larger delineation range for the dominant stimulation zone and potentially higher oil content in the shale reservoir. From the fitting equations, once the upper limits of effective porosity, water saturation, free hydrocarbon content, and organic carbon content are determined, and the coefficients of determination are known, the lower limit of resistivity can be determined. Then, based on the lower limit of resistivity, the dominant stimulation zone can be determined in G+1 resistivity plane distribution maps. The dominant stimulation zone is the area with resistivity greater than the lower limit of resistivity in the resistivity plane distribution maps. The upper limits of effective porosity, water saturation, free hydrocarbon content, and organic carbon content can be obtained from geological data, well logging data, drilling data, and well testing data of the study region.

[0103] It should be noted that the "advantageous redevelopment zone" refers to the potential shale oil enrichment area inferred from the resistivity distribution. The reason for defining this zone is that the oil-bearing state and oil content of shale reservoirs are inextricably linked to lithological distribution. Locations dominated by mudstone and shale, with highly developed foliation fractures and abundant clay minerals typically have larger shale oil reserves. However, locations with well-developed brittle lithologies such as felsic grains, dolomite laminae, and shell-like calcareous laminae have lower oil spillover rates. These locations have dense carbonate cementation, underdeveloped reservoir space, and lower oil content. Different lithologies also lead to different rock properties. Formations with a higher brittleness index may exhibit better compressibility during fracturing operations (the brittleness index is an important parameter characterizing the brittleness of rocks, a comprehensive parameter quantifying the rock's ability to resist plastic deformation and undergo sudden fracture under external forces), resulting in more fracturing fractures. Furthermore, according to the fitted relationship, the lower limit of resistivity in shale oil-rich areas is related to logging parameters such as free hydrocarbon content and organic carbon content in the reservoir of the actual study area. In fact, these two parameters are inseparable from the lithology contained in the crude oil reservoir environment. Therefore, this invention delineates "dominant stimulation zones" based on the obtained planar map and the lower limit of resistivity in shale oil-rich areas. The purpose is to conduct classified research and analysis to achieve a more detailed and accurate evaluation of the fracture network morphology after reservoir fracturing. The lower limit of resistivity indicates the difference in rock properties inside and outside the dominant stimulation zone. In subsequent fracturing operations, the artificial fracture morphology obtained in different lithological areas will also be different. If only a uniform resistivity reduction standard is used to delineate the fracture range in subsequent steps, it will lead to results that deviate from the actual formation conditions of the work area and have low reliability. Overall, the purpose of delineating dominant stimulation zones is to achieve classified research on areas with significant differences in reservoir lithology.

[0104] In some embodiments of the present invention, the constraint formula for the first boundary value is:

[0105] ;Formula (4)

[0106] = ;Formula (5)

[0107] The constraint formula for the second boundary value is:

[0108] ;Formula (6)

[0109] in, The magnitude of the decrease in resistivity, n For the target thickness, To flatten the thickness, E These are preset lithology weighted parameters. The average thickness of the interlayer in the shale. The average thickness of the mudstone interlayers. The average thickness of the sandstone interlayers. This is the sum of the average interlayer thicknesses of carbonate rocks. For shale, the weighting coefficient is... For mudstone, the weighting coefficient is... For sandstone, the weighting coefficient is... This represents the weighting coefficient for carbonate rocks.

[0110] The average thickness of the flattened layer, the average interlayer thickness of the shale, the average interlayer thickness of the mudstone, the average interlayer thickness of the sandstone, and the sum of the average interlayer thickness of the carbonate rocks can all be obtained from the well logging data of the study area; the weighting coefficients of the shale, mudstone, sandstone, and carbonate rocks can be obtained from the oil testing data of the study area and from the laboratory physical property tests of the target reservoir cores of multiple production wells in the study area.

[0111] The flattened thickness is the thickness value of the corresponding target layer after removing the thickness of ineffective lithological interlayers. It refers to the total superimposed thickness of mudstone, shale, and sandstone in the target layer. Ineffective lithological interlayers include carbonate rocks. The first and second boundary values ​​deduct the influence of ineffective reservoirs, which better reflects the actual situation in the study area.

[0112] It should be noted that regarding the correspondence between the relevant parameters of the target sublayer (flattening thickness, average interlayer thickness of shale, average interlayer thickness of mudstone, average interlayer thickness of sandstone, sum of average interlayer thicknesses of carbonate rocks, weight coefficient of shale, weight coefficient of mudstone, weight coefficient of sandstone, and weight coefficient of carbonate rocks) and the apparent resistivity reduction value plane distribution map, taking two target sublayers as an example, divided into upper and lower sublayers, corresponding to three apparent resistivity reduction value plane distribution maps: the top boundary of the upper sublayer, the boundary between the upper and lower sublayers, and the bottom boundary of the lower sublayer. The apparent resistivity reduction value plane distribution map corresponding to the top boundary of the upper sublayer can correspond to the relevant parameters of the upper sublayer; the apparent resistivity reduction value plane distribution map corresponding to the boundary between the upper and lower sublayers can correspond to the relevant parameters of the upper sublayer or the relevant parameters of the lower sublayer; the apparent resistivity reduction value plane distribution map corresponding to the bottom boundary of the lower sublayer can correspond to the relevant parameters of the lower sublayer.

[0113] refer to Figure 6 This allows for intuitive observation of closed, irregular polygons, thus reducing boundary values. The boundary line is used to define the area within which the apparent resistivity decreases as the main body, and the area outside the boundary line as the predicted crack zone. Figure 6The area enclosed by the dashed line and the coordinate axis is the advantageous modification zone. The area enclosed by the solid line further in is the main body of the apparent resistivity reduction. The solid line further out represents the zero value of the resistivity reduction, indicating that the formation here is almost unaffected by fracturing fluid, and the apparent resistivity change is basically zero. The part enclosed by the solid line further in and the solid line further out is the predicted fracture zone. The part outside the solid line further out is the area unaffected by fracturing operations.

[0114] It should be noted that, Figure 5 and Figure 6 All are resistivity planar diagrams, with the horizontal and vertical axes representing the geodetic coordinate axes.

[0115] It is understandable that the main component of the apparent resistivity reduction is the fracturing fluid currently remaining in the formation, as inferred from the resistivity distribution in this invention. It is worth noting that the planar distribution map of the apparent resistivity reduction value in this invention is obtained based on the difference in apparent resistivity between the formation before fracturing and after flowback. The apparent resistivity distribution of the formation before fracturing represents the original apparent resistivity distribution of the formation before any modification or construction. It is important to understand that the smaller the difference between the apparent resistivity distribution of the formation after flowback and the original apparent resistivity distribution, the closer the apparent resistivity distribution of the formation after flowback is to the original state, indicating a lower retention rate of fracturing fluid and a higher flowback rate. Conversely, when there is a significant difference between the apparent resistivity distribution of the formation after flowback and the original apparent resistivity distribution, it means that the fracturing fluid retention rate in the formation is high. Simultaneously, the presence of a large amount of water and metal ions leads to a significantly low resistivity in the fracturing fluid. Therefore, this significant difference will result in a significant decrease in the apparent resistivity at the same formation location after flowback.

[0116] The main dividing line for the change in apparent resistivity is the boundary value of the decrease. It is important to understand that shale reservoirs typically contain clay minerals. Due to the hydrophilicity of clay minerals, even if the fracturing fluid is completely discharged from a certain formation fracture after flowback, the resistivity of that formation will decrease to some extent. This means that the fracturing fluid will reduce the resistivity of the shale reservoir regardless of whether the flowback is complete or not. It also indicates that the distribution of fracturing fluid in the formation cannot be determined solely by whether the resistivity changes. Therefore, this invention proposes to divide the fracturing fluid distribution area using a reduction boundary value that conforms to the actual physical properties of the reservoir and is related to the actual lithological distribution of the reservoir as the boundary line (as can be seen from formulas (4), (5), and (6)). EClosely related to the actual reservoir properties and lithology of the study area, by combining relevant parameters that can effectively characterize the actual lithology of the formation in the study area to reduce the adverse effects of rock properties on resistivity, results closer to the actual situation can be obtained. When the resistivity anomaly is greater than the drop boundary value, the area inside the boundary line is judged to still have some fracturing fluid remaining, resulting in a significant abnormal change in resistivity in this area; when the resistivity anomaly is less than the drop boundary value, the area outside the boundary line is judged to be a fracture distribution area with low fracturing fluid retention or where fracturing fluid was previously filled but has now been drained.

[0117] In some embodiments of the present invention, reference is made to Figure 6 Based on the predicted fracture zones, the fracture network morphology identification after the target reservoir is flowed back includes:

[0118] Subdivide the grid into the planar distribution map for each apparent resistivity decrease value;

[0119] The spatial location, width, and length of the predicted crack zone are determined based on the number and location of grid points in the planar distribution map of each apparent resistivity reduction value.

[0120] Based on the spatial location, width, and length of all predicted fracture zones in the G+1 apparent resistivity reduction plane distribution map, the fracture network morphology identification after the target reservoir flowback is completed.

[0121] The reservoir flowback fracture network morphology identification method proposed in this invention innovatively combines seismic, electromagnetic, and logging technologies to compensate for some of the shortcomings of using the three methods individually. The method proposed in this invention is based on the concept of integrating seismic, logging, and electromagnetic exploration methods. When combining the three technologies, it does not simply perform a series formula. When combining the three types of data, it makes full use of the advantages of each data: (1) Combining seismic and electromagnetic methods. The geological framework and boundary conditions provided by the seismic model are used to accurately identify the reservoir structure in the study area. Electromagnetic data is used to fill the gaps, and high-precision wide-area electromagnetic inversion is performed to obtain the wide-area apparent resistivity, which is more sensitive to fluids. This combines the characteristics of conventional seismic parameters, which are accurate in identifying reservoir structure but inaccurate in identifying fluids, and electromagnetic methods, which are not accurate in identifying formation structure but sensitive to fluids; (2) Achieving the organic combination of electromagnetic and logging technologies. In proposing the discriminant relationship between the advantageous transformation zone of the study area, this invention fully considers the complex actual situation of the study area. Firstly, it mainly selects the well resistivity profile for correlation analysis, so that the data is more consistent with the actual situation of the formation and has higher credibility. Secondly, it mainly selects several logging parameters that are closely related to the oil and gas enrichment and fits them. Based on the fitting, it establishes an empirical formula. When establishing the empirical formula, it is not a simple linear analysis, but fully considers the actual situation of the reservoir and substitutes the correlation between various logging parameters and resistivity, so that the formula parameters are dynamic and have stronger applicability. (3) This invention innovatively divides the target reservoir into several virtual small layers according to equal thickness and draws the apparent resistivity plane map along the formation trend. Compared with the conventional two-dimensional profile results, the data is more refined and richer, and the multidimensional characteristics are significant. (4) When identifying the fracture network morphology, this invention establishes different formulas according to whether it is in an advantageous transformation zone or not. The formula uses the flattened thickness to deduct the influence of the ineffective reservoir, which is more in line with the actual situation of the work area.

[0122] The fracture network morphology identification method after reservoir flowback according to embodiments of the present invention, by dividing the target reservoir into equal-thickness sub-layers and then analyzing the resistivity reduction before and after flowback for each of the G+1 resistivity plane distribution maps corresponding to G target sub-layers, can intuitively display the change in resistivity of the target reservoir along the formation trend. This method improves the accuracy of fracture network morphology identification in shale oil reservoirs where horizontal homogeneity is significantly stronger than vertical homogeneity. By using a reduction boundary value that conforms to the actual reservoir physical properties and is related to the actual lithological distribution state of the reservoir as the boundary line for dividing the fracturing fluid distribution area, the relevant parameters of the actual lithological conditions of the formation in the study area can be effectively characterized to reduce the adverse effects of rock physical properties on resistivity. This yields results that are closer to the actual situation, making fracture network morphology identification more accurate. In particular, the rock physical property parameters of the dominant and non-dominantly modified areas differ greatly, and the calculation of the reduction boundary value classifies and discusses whether it is in a dominantly modified area, which is more in line with the actual situation of the study area, resulting in higher accuracy in the final fracture network morphology identification.

[0123] According to a second aspect of the present invention, a reservoir flowback post-fracturing mesh morphology identification system includes a data acquisition unit, a sub-layer division unit, a sub-layer resistivity extraction unit, a resistivity reduction magnitude determination unit, a reduction magnitude boundary value calculation unit, and a mesh identification unit.

[0124] The data acquisition unit is used to acquire the first apparent resistivity data of the target reservoir in the study area before fracturing and the second apparent resistivity data after flowback, as well as the elevation data of the target reservoir.

[0125] The small-layer division unit is used to divide the target reservoir into small layers of equal thickness based on elevation data, so as to obtain G target small layers with thickness values ​​of the target thickness;

[0126] The small-layer resistivity extraction unit is used to extract the corresponding data of G+1 planes corresponding to G target small layers from the first apparent resistivity data to obtain G+1 first resistivity plane distribution maps, and to extract the corresponding data of G+1 planes corresponding to G target small layers from the second apparent resistivity data to obtain G+1 second resistivity plane distribution maps.

[0127] The resistivity reduction magnitude determination unit is used to calculate the resistivity reduction magnitude after the return based on G+1 first resistivity plane distribution maps and G+1 second resistivity plane distribution maps, so as to obtain G+1 apparent resistivity reduction magnitude plane distribution maps before and after the return.

[0128] The reduction boundary value calculation unit is used to calculate the reduction boundary value corresponding to each apparent resistivity reduction value plane distribution map based on the resistivity reduction magnitude, preset lithology weighting parameters, target thickness, and leveling thickness. Among them, the leveling thickness is the thickness value of the corresponding target sublayer after removing the thickness of ineffective lithological interlayers. If the apparent resistivity reduction value plane distribution map is not in the dominant modification zone, the reduction boundary value is the first boundary value; if it is in the dominant modification zone, the reduction boundary value is the second boundary value. The dominant modification zone characterizes the shale oil enrichment area.

[0129] The fracture network identification unit is used to identify the part enclosed by the reduction boundary value as the main body of the apparent resistivity reduction change, and to identify the part enclosed by the reduction boundary value and the zero value of the resistivity reduction as the predicted fracture zone. Based on the predicted fracture zone, the fracture network morphology identification is completed after the target reservoir is flowed back.

[0130] Apparent resistivity data is obtained through the following steps:

[0131] To acquire electromagnetic field information, seismic data, and geological data for the study area;

[0132] An initial geoelectric model is established based on electromagnetic field information, seismic data, and geological data. A high-precision wide-area electromagnetic inversion is then performed on the initial geoelectric model to obtain the inverted apparent resistivity distribution profile of the study area, thereby acquiring the apparent resistivity data of the target reservoir in the study area.

[0133] Altitude data is obtained through the following steps:

[0134] Acquire seismic data and well logging data for the study area;

[0135] The elevation range of the target reservoir is determined based on seismic data and well logging data, and this elevation range is recorded as elevation data.

[0136] It should be noted that the elevation range refers to the elevation data that can describe the top and bottom interfaces of the target reservoir at any location within the study area. The elevation of the top and bottom interfaces of the target reservoir changes continuously with geographical location and stratigraphic trend, rather than remaining constant. The elevation data of the target reservoir can be represented by coordinate points (x, y, z), where x and y are the x and y values ​​of the geodetic coordinate system at that location, and z is the corresponding elevation depth.

[0137] The constraint formula for the target thickness is:

[0138] ;Formula (1)

[0139] in, n For the target thickness, N For the thickness of the target reservoir, For effective porosity, m The preset porosity index, kThe preset saturation index, a and b All are preset lithology coefficients. The thickness of the target reservoir is obtained based on elevation data. Effective porosity, porosity index, saturation index, and lithology coefficients can all be obtained from existing geological and seismic data of the study area. G equals N / n .

[0140] For understanding the G target sublayers and G+1 resistivity plane distribution maps, refer to... Figure 3 , Figure 3 This is a schematic diagram of a resistivity plane diagram according to an embodiment of the present invention. If the number of target sub-layers is 2, divided into upper and lower sub-layers, then there are three resistivity plane distribution diagrams, namely the top boundary of the upper sub-layer, the boundary between the upper and lower sub-layers, and the bottom boundary of the lower sub-layer.

[0141] By dividing the target reservoir into equal-thickness sub-layers and then analyzing the resistivity reduction before and after the return of the G+1 resistivity plane distribution maps corresponding to the G target sub-layers, the change of resistivity of the target reservoir along the formation trend can be intuitively displayed. In shale oil reservoirs where horizontal homogeneity is significantly stronger than vertical homogeneity, the accuracy of fracture network morphology identification can be improved.

[0142] In some embodiments, one or more production wells with logging data in the study area are selected for fracturing operations. The resistivity reduction after flowback is calculated based on G+1 first resistivity plane distribution maps and G+1 second resistivity plane distribution maps to obtain G+1 apparent resistivity reduction value plane distribution maps before and after flowback.

[0143] The constraint formula for the magnitude of resistivity reduction is:

[0144] ;Formula (2)

[0145] in, The magnitude of the decrease in resistivity, The resistivity before fracturing. The resistivity is the value after the material is returned to its original state.

[0146] In some embodiments of the present invention, reference is made to Figure 2 , Figure 4 and Figure 5 The advantageous redevelopment areas are determined through the following steps:

[0147] Obtain resistivity data and corresponding well logging data of multiple production wells in the study area. The well logging data includes multiple logging parameters.

[0148] Correlation fitting was performed on resistivity data from multiple production wells and corresponding logging data from multiple production wells to obtain the fitting relationship between resistivity and multiple logging parameters.

[0149] The critical values ​​of multiple logging parameters of the formation in the shale oil enrichment area within the study area are obtained based on the fitting relationship, so as to determine the lower limit of resistivity of the shale oil enrichment area within the study area.

[0150] Based on the lower limit of resistivity, the dominant modification areas are determined in the G+1 resistivity plane distribution maps. The dominant modification areas are the regions in the resistivity plane distribution maps where the resistivity is greater than the lower limit of resistivity.

[0151] It should be noted that the reference Figure 2 The resistivity data of multiple production wells are extracted by selecting several apparent resistivity profiles. All apparent resistivity profiles must pass through one or more production wells in the study area that have logging data.

[0152] In some embodiments of the present invention, multiple logging parameters are effective porosity, water saturation, free hydrocarbon content, and organic carbon content, and the constraint formula for the fitting relationship is:

[0153] ;Formula (3)

[0154] in, Resistivity and These are the preset calculation parameters. e It is a mathematical natural constant. For effective porosity, Water saturation This refers to the free hydrocarbon content. TOC Organic carbon content, The coefficient of determination for free hydrocarbon content, The coefficient of determination for organic carbon content. m The preset porosity index, k This is the preset saturation index.

[0155] Correlation fitting was performed using resistivity data from multiple production wells and corresponding logging data from multiple production wells. The coefficient of determination of the fitted curve was then obtained based on the fitting results. The coefficient of determination is That is, the correlation coefficient. R The square of.

[0156] The power parameter of effective porosity and water saturation Recorded as x . refer to Figure 4 The resistivity is calculated and fitted according to formula (3) until the calculation is completed. Power parameter of effective porosity and water saturation x coefficient of determination between Reaching above 0.9. It is worth noting that when the free hydrocarbon content... Organic carbon content TOC When the coefficient of determination of each factor is less than 0.5, they will be judged as having no correlation. In this case, the formula... , TOC It was assigned the value 0.

[0157] In some embodiments of the present invention, critical values ​​of multiple logging parameters of shale oil-rich formations within the study area are obtained based on fitting relationships to determine the lower limit of resistivity of shale oil-rich areas within the study area, including:

[0158] To obtain the upper limit of effective porosity, upper limit of water saturation, lower limit of free hydrocarbon content, and lower limit of organic carbon content of strata in shale oil-rich areas within the study area;

[0159] The lower limit of resistivity is determined based on the fitted relationship, the upper limit of effective porosity, the upper limit of water saturation, the lower limit of free hydrocarbon content, and the lower limit of organic carbon content.

[0160] Understandably, higher effective porosity and water saturation lead to lower free hydrocarbon and organic carbon content, potentially resulting in higher oil content in shale reservoirs and thus greater potential for stimulation. From the fitted relationship, once the upper limits of effective porosity, water saturation, free hydrocarbon content, and organic carbon content are determined, and the coefficients of determination are known, the lower limit of resistivity can be determined. Then, based on this lower limit, advantageous stimulation zones can be identified in G+1 resistivity plane distribution maps. These advantageous zones are areas with resistivity greater than the lower limit on the resistivity plane distribution maps. The upper limits of effective porosity, water saturation, free hydrocarbon content, and organic carbon content can be obtained from geological, logging, drilling, and well testing data for the study area.

[0161] In some embodiments of the present invention, the constraint formula for the first boundary value is:

[0162] ;Formula (4)

[0163] = ;Formula (5)

[0164] The constraint formula for the second boundary value is:

[0165] ;Formula (6)

[0166] in, The magnitude of the decrease in resistivity, n For the target thickness, To flatten the thickness, E These are preset lithology weighted parameters. The average thickness of the interlayer in the shale. The average thickness of the mudstone interlayers. The average thickness of the sandstone interlayers. This is the sum of the average interlayer thicknesses of carbonate rocks. For shale, the weighting coefficient is... For mudstone, the weighting coefficient is... For sandstone, the weighting coefficient is... This represents the weighting coefficient for carbonate rocks.

[0167] The average thickness of the flattened layer, the average interlayer thickness of the shale, the average interlayer thickness of the mudstone, the average interlayer thickness of the sandstone, and the sum of the average interlayer thickness of the carbonate rocks can all be obtained from the well logging data of the study area; the weighting coefficients of the shale, mudstone, sandstone, and carbonate rocks can be obtained from the oil testing data of the study area and from the laboratory physical property tests of the target reservoir cores of multiple production wells in the study area.

[0168] The flattened thickness is the thickness value of the corresponding target layer after removing the thickness of ineffective lithological interlayers. It refers to the total superimposed thickness of mudstone, shale, and sandstone in the target layer. Ineffective lithological interlayers include carbonate rocks. The first and second boundary values ​​deduct the influence of ineffective reservoirs, which better reflects the actual situation in the study area.

[0169] refer to Figure 6 This allows for intuitive observation of closed, irregular polygons, thus reducing boundary values. The boundary line is used to define the area within which the apparent resistivity decreases as the main body, and the area outside the boundary line as the predicted crack zone. Figure 6 The area enclosed by the dashed line and the coordinate axis is the advantageous modification zone. The area enclosed by the solid line further in is the main body of the apparent resistivity reduction. The solid line further out represents the zero value of the resistivity reduction, indicating that the formation here is almost unaffected by fracturing fluid, and the apparent resistivity change is basically zero. The part enclosed by the solid line further in and the solid line further out is the predicted fracture zone. The part outside the solid line further out is the area unaffected by fracturing operations.

[0170] It should be noted that, Figure 5 and Figure 6 All are resistivity planar diagrams, with the horizontal and vertical axes representing the geodetic coordinate axes.

[0171] In some embodiments of the present invention, reference is made to Figure 6 Based on the predicted fracture zones, the fracture network morphology identification after the target reservoir is flowed back includes:

[0172] Subdivide the grid into the planar distribution map for each apparent resistivity decrease value;

[0173] The spatial location, width, and length of the predicted crack zone are determined based on the number and location of grid points in the planar distribution map of each apparent resistivity reduction value.

[0174] Based on the spatial location, width, and length of all predicted fracture zones in the G+1 apparent resistivity reduction plane distribution map, the fracture network morphology identification after the target reservoir flowback is completed.

[0175] The reservoir flowback fracture network morphology identification system proposed in this invention innovatively proposes a method that organically combines seismic, electromagnetic, and logging technologies, making up for some of the shortcomings of the three methods when used alone. The system proposed in this invention is based on the concept of integrating seismic, logging, and electromagnetic exploration methods. When combining the three technologies, it does not simply perform a series formula. When combining the three types of data, it makes full use of the advantages of each data: (1) Combining seismic and electromagnetic methods. The geological framework and boundary conditions provided by the seismic model are used to accurately identify the reservoir structure in the study area. Electromagnetic data is used to fill the gaps, and high-precision wide-area electromagnetic inversion is performed to obtain the wide-area apparent resistivity that is more sensitive to fluids. This combines the characteristics of conventional seismic parameters for accurate reservoir structure identification but inaccurate fluid identification, and electromagnetic methods for inaccurate formation structure identification but sensitive to fluids; (2) Achieving the organic combination of electromagnetic and logging technologies. In proposing the discriminant relationship between the advantageous transformation zone of the study area, this invention fully considers the complex actual situation of the study area. Firstly, it mainly selects the well resistivity profile for correlation analysis, so that the data is more consistent with the actual situation of the formation and has higher credibility. Secondly, it mainly selects several logging parameters that are closely related to the oil and gas enrichment and fits them. Based on the fitting, it establishes an empirical formula. When establishing the empirical formula, it is not a simple linear analysis, but fully considers the actual situation of the reservoir and substitutes the correlation between various logging parameters and resistivity, so that the formula parameters are dynamic and have stronger applicability. (3) This invention innovatively divides the target reservoir into several virtual small layers according to equal thickness and draws the apparent resistivity plane map along the formation trend. Compared with the conventional two-dimensional profile results, the data is more refined and richer, and the multidimensional characteristics are significant. (4) When identifying the fracture network morphology, this invention establishes different formulas according to whether it is in an advantageous transformation zone or not. The formula uses the flattened thickness to deduct the influence of the ineffective reservoir, which is more in line with the actual situation of the work area.

[0176] The reservoir flowback fracture network morphology identification system according to embodiments of the present invention, by dividing the target reservoir into equal-thickness sub-layers and then analyzing the resistivity reduction before and after flowback for each of the G+1 resistivity plane distribution maps corresponding to G target sub-layers, can intuitively display the change in resistivity of the target reservoir along the formation trend. This improves the accuracy of fracture network morphology identification in shale oil reservoirs where horizontal homogeneity is significantly stronger than vertical homogeneity. By using a reduction boundary value that conforms to the actual reservoir physical properties and is related to the actual lithological distribution state of the reservoir as the boundary line for dividing the fracturing fluid distribution area, the relevant parameters of the actual lithological conditions of the formation in the study area can be effectively characterized to reduce the adverse effects of rock physical properties on resistivity, resulting in results closer to the actual situation and more accurate fracture network morphology identification. In particular, the rock physical property parameters of the dominant and non-dominantly modified areas differ greatly, and the calculation of the reduction boundary value classifies and discusses whether it is in a dominantly modified area, which is more in line with the actual situation of the study area, resulting in higher accuracy of fracture network morphology identification.

[0177] In addition, one embodiment of the present invention provides a control device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor and the memory can be connected via a bus or other means.

[0178] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0179] The non-transient software program and instructions required to implement the reservoir flowback segregation morphology recognition method of the above embodiments are stored in the memory. When executed by the processor, the reservoir flowback segregation morphology recognition method of the above embodiments is executed.

[0180] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0181] Furthermore, one embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions that are executed by a processor or controller, for example, by the processor of the above embodiment, causing the processor to perform the reservoir flowback selvage morphology identification method in the above embodiment.

[0182] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0183] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for identifying the network of fractures after post-flood reservoir, characterized in that, The method comprises: obtaining first apparent resistivity data and second apparent resistivity data of a target reservoir in a study area before fracturing and after flowback, and elevation data of the target reservoir; based on the elevation data, the target reservoir is divided into sublayers with equal thickness to obtain G target sublayers with a target thickness; from the first apparent resistivity data, corresponding data of G+1 planes corresponding to the G target sublayers is extracted to obtain G+1 first resistivity plane distribution maps, and from the second apparent resistivity data, corresponding data of G+1 planes corresponding to the G target sublayers is extracted to obtain G+1 second resistivity plane distribution maps; according to the G+1 first resistivity plane distribution maps and the G+1 second resistivity plane distribution maps, a resistivity reduction amplitude after flowback is calculated to obtain G+1 apparent resistivity reduction amplitude value plane distribution maps before and after flowback; according to the resistivity reduction amplitude, a preset lithology weighting parameter, the target thickness and a flattened thickness, a reduction amplitude boundary value corresponding to each of the apparent resistivity reduction amplitude value plane distribution maps is calculated, wherein the flattened thickness is a thickness value corresponding to the target sublayer after removing the thickness of invalid lithologic interlayer, if the apparent resistivity reduction amplitude value plane distribution map is not in a dominant reconstruction area, the reduction amplitude boundary value is a first boundary value, if the apparent resistivity reduction amplitude value plane distribution map is in the dominant reconstruction area, the reduction amplitude boundary value is a second boundary value, and the dominant reconstruction area represents a shale oil enrichment area; part surrounded by the reduction amplitude boundary value is determined as an apparent resistivity reduction amplitude change main body, and part surrounded by the reduction amplitude boundary value and the zero value of the resistivity reduction amplitude is determined as a predicted fracture zone, and based on the predicted fracture zone, a fracture network morphology after flowback of the target reservoir is identified.

2. The method of claim 1, wherein, The dominant reconstruction area is determined by the following steps: obtaining resistivity data of a plurality of production wells in the study area and corresponding well logging data of the plurality of production wells, wherein the well logging data comprises a plurality of logging parameters; correlation fitting is performed on the plurality of resistivity data of the production wells and the corresponding plurality of well logging data to obtain a fitting relationship between resistivity and the plurality of logging parameters; based on the fitting relationship, a critical value of the plurality of logging parameters of the formation in the shale oil enrichment area in the study area is obtained to determine a lower limit of resistivity of the shale oil enrichment area in the study area; based on the lower limit of resistivity, the dominant reconstruction area is determined in the G+1 resistivity plane distribution maps, and the dominant reconstruction area is a region in the resistivity plane distribution map with resistivity greater than the lower limit of resistivity.

3. The method of claim 2, wherein, The plurality of logging parameters are effective porosity, water saturation, free hydrocarbon content and organic carbon content, and the constraint formula of the fitting relationship is: ； wherein, is the resistivity, and is a preset calculation parameter, e is the mathematical natural constant, is the effective porosity, is the water saturation, is the free hydrocarbon content, TOC is the organic carbon content, is the determination coefficient of the free hydrocarbon content, is the determination coefficient of the organic carbon content, m is a preset porosity index, k is a preset saturation index.

4. The method of claim 3, wherein, The method for obtaining the critical value of the plurality of logging parameters of the formation in the shale oil enrichment area in the study area based on the fitting relationship to determine the lower limit of resistivity of the shale oil enrichment area in the study area comprises: obtaining an upper limit of effective porosity, an upper limit of water saturation, a lower limit of free hydrocarbon content and a lower limit of organic carbon content of the formation in the shale oil enrichment area in the study area; determining the lower limit of the resistivity according to the fitting formula, the upper limit of the effective porosity, the upper limit of the water saturation, the lower limit of the free hydrocarbon content and the lower limit of the organic carbon content.

5. The method of claim 1, wherein, The constraint formula of the first boundary value is: ； = ； The constraint formula of the second boundary value is: ； wherein, is the resistivity reduction amplitude, n is the target thickness, is the flattening thickness, E is a preset lithology weighting parameter, is the average interlayer thickness of shale, is the average interlayer thickness of mudstone, is the average interlayer thickness of sandstone, is the sum of the average interlayer thickness of carbonate rocks, is the weight coefficient of shale, is the weight coefficient of mudstone, is the weight coefficient of sandstone, is the weight coefficient of carbonate rocks.

6. The post-frac fracture network identification method of claim 1, wherein, The constraint formula of the target thickness is: ； wherein, n is the target thickness, N is the thickness of the target reservoir, is the effective porosity, m is a preset porosity index, k is a preset saturation index, a and b are both preset lithology coefficients.

7. The method of claim 1, wherein, The fracture network morphology identification after flowback of the target reservoir based on the predicted fracture zone includes: grid subdivision is performed on each of the apparent resistivity drop value plane distribution maps; the spatial position, fracture width and fracture length of the predicted fracture zone are determined according to the grid point quantity and grid point position of the predicted fracture zone in each of the apparent resistivity drop value plane distribution maps; fracture network morphology identification after flowback of the target reservoir is completed according to the spatial position, fracture width and fracture length of all the predicted fracture zones in G+1 apparent resistivity drop value plane distribution maps.

8. A reservoir flowback fracture morphology recognition system, characterized in that, The system comprises: a data acquisition unit configured to acquire first apparent resistivity data and second apparent resistivity data of a target reservoir in a research area before fracturing and after flowback, and elevation data of the target reservoir; a sublayer division unit configured to perform sublayer equal-thickness division on the target reservoir based on the elevation data to obtain G target sublayers with a target thickness; a sublayer resistivity extraction unit configured to extract corresponding data of G+1 planes corresponding to the G target sublayers from the first apparent resistivity data to obtain G+1 first resistivity plane distribution maps, and extract corresponding data of G+1 planes corresponding to the G target sublayers from the second apparent resistivity data to obtain G+1 second resistivity plane distribution maps; a resistivity drop amplitude determination unit configured to calculate resistivity drop amplitudes after flowback according to G+1 first resistivity plane distribution maps and G+1 second resistivity plane distribution maps to obtain G+1 apparent resistivity drop value plane distribution maps before and after flowback; a drop amplitude boundary value calculation unit configured to calculate a drop amplitude boundary value corresponding to each of the apparent resistivity drop value plane distribution maps according to the resistivity drop amplitude, a preset lithology weighting parameter, the target thickness and a flattened thickness, wherein the flattened thickness is a thickness value corresponding to the target sublayer after removing invalid lithologic interlayer thickness, the drop amplitude boundary value is a first boundary value if the apparent resistivity drop value plane distribution map is not in a dominant reconstruction area, and the drop amplitude boundary value is a second boundary value if the apparent resistivity drop value plane distribution map is in the dominant reconstruction area, and the dominant reconstruction area represents a shale oil enrichment area; a fracture network identification unit configured to determine a part surrounded by the drop amplitude boundary value as an apparent resistivity drop amplitude change main body, and determine a part surrounded by the drop amplitude boundary value and a zero value of the resistivity drop amplitude as a predicted fracture zone, and complete fracture network morphology identification after flowback of the target reservoir based on the predicted fracture zone.

9. A control device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the fracture network morphology identification method after flowback of the target reservoir.

10. A computer-readable storage medium storing computer-executable instructions, the computer-executable instructions comprising: The computer executable instructions are used to execute the fracture network morphology identification method after flowback of the target reservoir.

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