Method, system and device for identifying water invasion front edge position of high-resistance stratum and medium

Abstract

The invention discloses a water invasion front edge position identification method, system and device for a high-resistance stratum and a medium. The method comprises the following steps: establishing a geoelectric model; acquiring a plurality of wide-area electromagnetic integrated data volumes, target resistivity variation data, target porosity variation data and target permeability data of a target layer and a physical property and fluid simultaneous identification template of the target layer before fracturing, in the fracturing process and after fracturing; sequentially carrying out constrained inversion on the plurality of wide-area electromagnetic integrated data volumes in the geoelectric model to obtain a plurality of wide-area apparent resistivity profiles; the initial water invasion front position of the target layer is determined according to the low-resistance fluid dynamic change characteristics of the multiple wide-area apparent resistivity profiles; and according to the target resistivity variation data, the target porosity variation data, the target permeability data and the physical property and fluid simultaneous identification template, performing interference feature region elimination processing on the initial water invasion front position to obtain a target water invasion front position. According to the method, the water invasion front edge position of the high-resistance stratum can be accurately identified.

Description

Technical Field

[0001] The present application relates to the technical field related to oil and gas exploration and development, and in particular to a method, system, device and medium for identifying the position of a water invasion front in a high-resistance formation. Background Art

[0002] Identifying fluids in target formations is a cutting-edge technology in oil and gas exploration and development. Typically, these fluids include crude oil, natural gas, and water. Water includes both the original formation water and water-based fracturing fluids injected later during construction. Water intrusion increases construction risks and can lead to anomalies such as pressure channeling and leakage, which can impact reservoir development. Therefore, rapidly predicting the location of the water intrusion front and displaying it as close to real time as possible is crucial. Summary of the Invention

[0003] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a method for identifying the position of the water invasion front edge in a high-resistance formation, which can accurately identify the position of the water invasion front edge in the high-resistance formation.

[0004] The present application also provides a system for identifying the position of a water invasion front in a high-resistance formation, a control device for executing the above-mentioned method for identifying the position of a water invasion front in a high-resistance formation, and a computer-readable storage medium.

[0005] According to a first aspect of the present application, a method for identifying a water invasion front position in a high-resistance formation includes:

[0006] Acquiring well logging resistivity data and lithologic data of the study area, dividing the horizons according to the well logging resistivity data and the lithologic data, and establishing a geoelectric model;

[0007] Based on the geoelectric model, a plurality of wide-area electromagnetic integrated data volumes corresponding to the target layer in the study area at multiple periods before, during, and after fracturing are obtained, as well as target resistivity change data, target porosity change data, and target permeability data of the target layer, and a physical property and fluid joint identification template, the physical property and fluid joint identification template being used to characterize parameter characteristic ranges corresponding to different geological characteristic regions, the geological characteristic regions including low-resistance fluid characteristic regions and interference characteristic regions, and the parameter characteristic ranges including porosity change ranges, resistivity change ranges, and permeability ranges;

[0008] performing constrained inversion on the plurality of wide-area electromagnetic integrated data volumes in the geoelectric model in sequence, and obtaining a plurality of wide-area apparent resistivity profiles in one-to-one correspondence;

[0009] determining the position of the initial water invasion front of the target layer based on the dynamic change characteristics of the low-resistance fluid in the plurality of wide-area apparent resistivity profiles;

[0010] The initial water invasion front position is subjected to interference feature area exclusion processing according to the target resistivity change data, the target porosity change data, the target permeability data, and the physical property and fluid joint identification template to obtain a target water invasion front position.

[0011] The method for identifying the position of the water invasion front in a high-resistance formation according to an embodiment of the present application has at least the following beneficial effects:

[0012] This application abandons the huge amount of seismic data and only uses logging resistivity data and lithology data to build the formation framework model, which reduces the burden of structural modeling and the workload of data processing. While minimizing the model layering error, it greatly improves the efficiency of model building. The dynamic change characteristics of low-resistance fluids in multiple wide-area apparent resistivity profiles can determine the initial water invasion front position of the target layer. However, in high-resistance formations, identifying low-resistance fluids only through wide-area apparent resistivity will be subject to interference factors such as reservoir physical properties, reservoir lithology, and reservoir fluids. Under tight conditions, reservoir physical properties will cause an abnormal increase in wide-area apparent resistivity; differences in reservoir lithology will also cause wide-area apparent resistivity anomalies; when the reservoir contains high-resistance oil and gas fluids, it will also cause wide-area apparent resistivity anomalies. By calibrating the parameter feature ranges corresponding to different geological feature areas using the physical property and fluid joint identification template, the corresponding low-resistance fluid feature area can be determined based on the target resistivity change data, target porosity change data, and target permeability data, eliminating the interference of the interfering feature area, thereby achieving accurate identification of low-resistance fluids in high-resistance reservoirs.

[0013] According to some embodiments of the present application, the target resistivity change data is obtained by the following steps:

[0014] performing constrained inversion on the plurality of wide-area electromagnetic integrated data volumes in the geoelectric model in sequence, and obtaining a plurality of wide-area apparent resistivity profiles in one-to-one correspondence;

[0015] Extracting the difference between the wide-area apparent resistivity data of the target layer and the two recent wide-area apparent resistivity profiles, and recording it as the initial resistivity variation data;

[0016] Extracting the wide-area apparent resistivity data of the wide-area apparent resistivity profile corresponding to the target layer before fracturing, and recording it as initial wide-area apparent resistivity data;

[0017] The ratio of the initial resistivity change data to the initial wide-area apparent resistivity data is determined as the target resistivity change data.

[0018] According to some embodiments of the present application, the target porosity change data is obtained by the following steps:

[0019] Acquire first logging porosity data of the target layer before fracturing and current second logging porosity data after fracturing;

[0020] Calculating a difference between the first well logging porosity data and the second well logging porosity data to obtain porosity change data;

[0021] The ratio of the porosity change data to the first well logging porosity data is determined as the target porosity change data.

[0022] According to some embodiments of the present application, the target permeability data is obtained by the following steps:

[0023] Obtain the current logging permeability data of the target layer after fracturing;

[0024] The logarithm of the well logging permeability data is determined as the target permeability data.

[0025] According to some embodiments of the present application, the physical property and fluid joint identification template is obtained by the following steps:

[0026] Acquiring multiple resistivity change data, multiple porosity change data, and multiple permeability data of the target layer in the study area before and after fracturing;

[0027] Determining parameter characteristic ranges corresponding to different geological characteristic regions based on the plurality of resistivity change data and corresponding preset first weights, the plurality of porosity change data and corresponding preset second weights, and the plurality of permeability data and corresponding preset third weights, the geological characteristic regions including low-resistance fluid characteristic regions and interference characteristic regions, and the parameter characteristic ranges including a porosity change range, a resistivity change range, and a permeability range;

[0028] The physical property and fluid joint identification template is obtained based on the parameter characteristic ranges corresponding to different geological characteristic areas.

[0029] According to some embodiments of the present application, the low-resistance fluid characteristic area includes a flooded area and a transition zone, the interference characteristic area includes an abnormally high-pressure area and a dense shielding area, the geological characteristic area also includes an original formation area, and the parameter characteristic range includes a first characteristic range, a second characteristic range, a third characteristic range, a fourth characteristic range, and a fifth characteristic range;

[0030] Determining parameter characteristic ranges corresponding to different geological characteristic regions based on the plurality of resistivity change data and corresponding preset first weights, the plurality of porosity change data and corresponding preset second weights, and the plurality of permeability data and corresponding preset third weights includes:

[0031] According to the multiple resistivity change data and the corresponding preset first weights, the multiple porosity change data and the corresponding preset second weights, and the multiple permeability data and the corresponding preset third weights, the flooded area and the corresponding first characteristic range, the transition zone and the corresponding second characteristic range, the original formation area and the corresponding third characteristic range, the abnormally high pressure area and the corresponding fourth characteristic range, and the dense shielding area and the corresponding fifth characteristic range are determined.

[0032] According to some embodiments of the present application, the plurality of wide-area electromagnetic integrated data volumes are respectively a first wide-area electromagnetic integrated data volume, a second wide-area electromagnetic integrated data volume, and a third wide-area electromagnetic integrated data volume, and the first wide-area electromagnetic integrated data volume, the second wide-area electromagnetic integrated data volume, and the third wide-area electromagnetic integrated data volume are obtained by the following steps:

[0033] Before fracturing, first wide-area apparent resistivity data of each layer in the study area is collected and calculated, a data volume of a target layer is deducted, wide-area electromagnetic wave data of the target layer is encrypted and collected, and second wide-area apparent resistivity data is calculated. The second wide-area apparent resistivity data is filled into the data volume of the target layer to obtain the first wide-area electromagnetic integrated data volume of each layer in the study area;

[0034] During the fracturing process, wide-area electromagnetic wave data of the target layer are encrypted and collected to obtain third wide-area apparent resistivity data. The data volume of the target layer is deducted and the third wide-area apparent resistivity data is added to the data volume of the target layer to obtain the second wide-area electromagnetic integrated data volume of each layer in the study area.

[0035] After fracturing, the wide-area electromagnetic wave data of the target layer are encrypted and collected to obtain fourth wide-area apparent resistivity data. The data volume of the target layer is deducted and the fourth wide-area apparent resistivity data is filled into the data volume of the target layer to obtain the third wide-area electromagnetic integrated data volume of each layer in the study area.

[0036] According to a second aspect of the present application, a system for identifying a water invasion front position in a high-resistance formation includes:

[0037] a geoelectric model building unit, configured to obtain well logging resistivity data and lithologic data of a study area, perform layer division based on the well logging resistivity data and the lithologic data, and build a geoelectric model;

[0038] a data acquisition unit for acquiring, based on the geoelectric model, a plurality of wide-area electromagnetic integrated data volumes corresponding to each other at multiple periods before, during, and after fracturing of a target layer in the study area, target resistivity change data, target porosity change data, and target permeability data of the target layer, and a physical property and fluid joint identification template, the physical property and fluid joint identification template being used to characterize parameter characteristic ranges corresponding to different geological characteristic regions, the geological characteristic regions including low-resistance fluid characteristic regions and interference characteristic regions, and the parameter characteristic ranges including porosity change ranges, resistivity change ranges, and permeability ranges;

[0039] a wide-area apparent resistivity profile determining unit, configured to sequentially perform constrained inversion on the plurality of wide-area electromagnetic integrated data volumes in the geoelectric model to obtain a plurality of wide-area apparent resistivity profiles in a one-to-one correspondence;

[0040] an initial water invasion front position determining unit, configured to determine the initial water invasion front position of a target layer based on the dynamic change characteristics of the low-resistance fluid in the plurality of wide-area apparent resistivity profiles;

[0041] a target water invasion front position determination unit, configured to perform interference feature area elimination processing on the initial water invasion front position based on the target resistivity change data, the target porosity change data, the target permeability data, and the physical property and fluid joint identification template to obtain the target water invasion front position.

[0042] The system for identifying the position of the water invasion front in a high-resistance formation according to the embodiment of the present application has at least the following beneficial effects:

[0043] This application abandons the huge amount of seismic data and only uses logging resistivity data and lithology data to build the formation framework model, which reduces the burden of structural modeling and the workload of data processing. While minimizing the model layering error, it greatly improves the efficiency of model building. The dynamic change characteristics of low-resistance fluids in multiple wide-area apparent resistivity profiles can determine the initial water invasion front position of the target layer. However, in high-resistance formations, identifying low-resistance fluids only through wide-area apparent resistivity will be subject to interference factors such as reservoir physical properties, reservoir lithology, and reservoir fluids. Under tight conditions, reservoir physical properties will cause an abnormal increase in wide-area apparent resistivity; differences in reservoir lithology will also cause wide-area apparent resistivity anomalies; when the reservoir contains high-resistance oil and gas fluids, it will also cause wide-area apparent resistivity anomalies. By calibrating the parameter feature ranges corresponding to different geological feature areas using the physical property and fluid joint identification template, the corresponding low-resistance fluid feature area can be determined based on the target resistivity change data, target porosity change data, and target permeability data, eliminating the interference of the interfering feature area, thereby achieving accurate identification of low-resistance fluids in high-resistance reservoirs.

[0044] According to a third embodiment of the present application, a control device 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 method for identifying the position of a water invasion front in a high-resistance formation as described in the first embodiment. Because the control device utilizes all of the technical solutions of the method for identifying the position of a water invasion front in a high-resistance formation described in the above embodiment, it at least has all the beneficial effects provided by the technical solutions of the above embodiment.

[0045] According to a fourth embodiment of the present application, a computer-readable storage medium stores computer-executable instructions for executing the method for identifying the position of a water invasion front in a high-resistance formation as described in the first embodiment. Because the computer-readable storage medium employs all of the technical solutions of the method for identifying the position of a water invasion front in a high-resistance formation described in the above embodiment, it at least has all the beneficial effects provided by the technical solutions of the above embodiment.

[0046] Other features and advantages of the present application will be set forth in the following description, and in part will be apparent from the description, or may be learned by practicing the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0048] Figure 1 This is a flow chart of a method for identifying the position of a water invasion front in a high-resistance formation according to an embodiment of the present application;

[0049] Figure 2 is a schematic diagram of a wide-area apparent resistivity profile according to an embodiment of the present application;

[0050] Figure 3 This is a comparative schematic diagram of the first wide-area apparent resistivity profile of the same survey line in one embodiment of the present application;

[0051] Figure 4 1 is a comparative schematic diagram of the second wide-area apparent resistivity profile of the same survey line in one embodiment of the present application;

[0052] Figure 5 This is a comparative schematic diagram of the third wide-area apparent resistivity profile of the same survey line in one embodiment of the present application;

[0053] Figure 6 Schematic diagram of a physical property and fluid joint identification template according to an embodiment of the present application. DETAILED DESCRIPTION

[0054] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application.

[0055] In the description of this application, if there is a description of first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0056] In the description of this application, it should be understood that descriptions involving orientation, such as the orientation or positional relationship indicated by up, down, etc., are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0057] In the description of this application, it should be noted that, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technical personnel in the relevant technical field can reasonably determine the specific meaning of the above terms in this application based on the specific content of the technical solution.

[0058] In the field of oil and gas exploration and development, hydraulic fracturing in high-resistivity reservoirs (such as carbonate rocks) is a conventional development and production enhancement measure. In this case, the low-resistivity fracturing fluid will penetrate into the high-resistivity reservoir through the pores and throats, forming a series of continuous or discontinuous low-resistivity zones within the high-resistivity reservoir. Generally, in dense reservoir areas (porosity and permeability are both low to very low), the wide-area resistivity usually also shows high resistivity. Therefore, it is necessary to analyze the high-resistivity portion of the wide-area apparent resistivity to determine whether the high resistivity is caused by lithology, high-resistivity fluid, or reservoir physical properties.

[0059] Currently, there is no technical solution on the market that can dynamically display the location of the low-resistivity fluid front in high-resistivity reservoirs, while also showing the static distribution of high-resistivity fluids such as oil and gas, which are relatively rich in reservoir physical properties. Therefore, a new identification method and technology is needed that can not only identify the cause of high reservoir resistivity, clarify the static distribution areas of relatively good physical properties and relatively rich oil and gas fluids in high-resistivity formations, but also focus on dynamically identifying the location of the low-resistivity water-based fracturing fluid front, thereby providing a reference for improving construction measures and early warning of fracturing fluid leakage.

[0060] Based on this, an embodiment of the present application proposes a method for identifying the position of the water invasion front in a high-resistance formation, which can accurately identify the position of the water invasion front in a high-resistance formation.

[0061] The following will be combined Figures 1 to 6 A clear and complete description is given of the method for identifying the position of the water invasion front of a high-resistance formation in an embodiment of the present application. Obviously, the embodiment described below is only a part of the embodiments of the present application, not all of the embodiments.

[0062] refer to Figures 1 to 6 , Figure 1 This is a flow chart of a method for identifying the position of a water invasion front in a high-resistance formation according to an embodiment of the present application; Figure 2 is a schematic diagram of a wide-area apparent resistivity profile according to an embodiment of the present application; Figure 3 This is a comparative schematic diagram of the first wide-area apparent resistivity profile of the same survey line in one embodiment of the present application; Figure 4 1 is a comparative schematic diagram of the second wide-area apparent resistivity profile of the same survey line in one embodiment of the present application; Figure 5 This is a comparative schematic diagram of the third wide-area apparent resistivity profile of the same survey line in one embodiment of the present application; Figure 6 Schematic diagram of a physical property and fluid joint identification template according to an embodiment of the present application.

[0063] According to the first embodiment of the present application, a method for identifying the position of a water invasion front in a high-resistance formation includes:

[0064] Obtain logging resistivity data and lithology data of the study area, divide the horizons based on the logging resistivity data and lithology data, and establish a geoelectric model;

[0065] Based on the geoelectric model, multiple wide-area electromagnetic integrated data volumes corresponding to the target layer in the study area at multiple periods before, during, and after fracturing are obtained, along with target resistivity change data, target porosity change data, and target permeability data for the target layer, as well as a physical property and fluid joint identification template. The physical property and fluid joint identification template is used to characterize the parameter characteristic ranges corresponding to different geological characteristic areas. The geological characteristic areas include low-resistance fluid characteristic areas and interference characteristic areas, and the parameter characteristic ranges include porosity change ranges, resistivity change ranges, and permeability ranges.

[0066] Constrained inversion is performed sequentially on multiple wide-area electromagnetic integrated data volumes in the geoelectric model, and multiple wide-area apparent resistivity profiles are obtained in one-to-one correspondence.

[0067] Determine the initial water invasion front position of the target layer based on the dynamic change characteristics of low-resistivity fluids in multiple wide-area apparent resistivity profiles;

[0068] The target water invasion front position is obtained by eliminating the interference feature area of ​​the initial water invasion front position based on the target resistivity change data, target porosity change data, target permeability data and the physical property and fluid joint identification template.

[0069] In areas with high exploration and development maturity, there are abundant geological data and a relatively clear understanding of the stratigraphic structure. Therefore, this application abandons the huge amount of seismic data, reduces the burden of structural modeling, and reduces the workload of data processing. Only logging lithology data and logging resistivity are used to build the stratigraphic framework model. While minimizing the model stratification error as much as possible, the efficiency of model building is greatly improved, providing a feasible method for rapid processing.

[0070] In mature exploration and development areas, wells are numerous and densely distributed, and logging data is comprehensive. By acquiring logging data from key wells in the study area, the stratigraphic architecture can be understood. In this embodiment, lithologic data and logging resistivity data are required to intuitively establish a stratigraphic architecture model. Simultaneously, logging porosity and permeability data are required to establish a template for joint identification of physical properties and fluids.

[0071] In the examples of this application, the regional structural profile of the study area is relatively simple, and the strata are basically horizontally layered. In areas with relatively complex structures, structural characteristics and stratigraphic development history can be obtained from regional background data.

[0072] Lithologic data includes data that directly reflects lithology and data that indirectly reflects lithology. Data that directly reflects lithology includes drilling coring and logging cuttings, while data that indirectly reflects lithology includes gamma logging, compensated density logging, natural potential logging, photoelectric cross-section index logging, and sonic logging. In this embodiment, data that directly reflects lithology is selected.

[0073] Generally, data that directly reflects lithology comes primarily from drilling coring, supplemented by logging cuttings. By obtaining core samples from key wells, we can clearly understand the thickness of each stratum within the study area, the developed lithology, oil content, basic porosity and permeability, the presence of fractures, and the contact relationships between the various strata. This information is crucial in establishing well-connected profiles and three-dimensional geological framework models for the work area. For example, when modeling a target stratum, it is necessary to understand parameters such as the thickness, burial depth, and lithology of each well's target stratum in order to more accurately build the model and understand its distribution both vertically and horizontally.

[0074] The above-mentioned logging resistivity data, logging porosity data and logging permeability data are all obtained from the logging data, and all three types of data need to be obtained from the entire well section.

[0075] In the embodiment of the present application, nuclear magnetic resonance logging data is used to obtain irreducible water saturation of the corresponding formation.

[0076] Each of the above-mentioned wells should cover the above-mentioned five types of logging information as much as possible. The number of well locations must be more than five. These five wells should be evenly distributed in the work area and should not be concentrated in a small block or on a line. This ensures that when establishing the stratigraphic framework model, the structural characteristics and stratigraphic development characteristics in the work area are covered as much as possible to reduce the multi-solution nature of the results.

[0077] The horizons are divided according to the logging resistivity data and lithologic data, and a geoelectric model is established, including: rapid horizon division of the main survey line profiles in the study area based on the logging resistivity data and the definition of positive and negative phases, and correction of the horizon top and bottom depths based on the lithologic data to obtain more accurate horizon division results and establish a geoelectric model.

[0078] Positive and negative phases are defined based on the logging resistivity of the entire well section. A positive phase is defined as the resistivity going from low to high values ​​from shallow to deep; a negative phase is defined as the resistivity going from high to low values ​​from shallow to deep. The accuracy of the positive and negative phases is 0.5m.

[0079] According to the above definition, for example, if the logging resistivity at an altitude of -4232m is R1 = 50Ω·m, the logging resistivity at a shallow altitude of -4231.5m is R2 = 48Ω·m, and the logging resistivity at a deep altitude of -4232.5m is R3 = 40Ω·m. K is the correction coefficient, usually 1Ω·m. Then the positive phase at an altitude of -4231.5m is:

[0080]

[0081] Similarly, the negative phase at an altitude of -4232.5m is:

[0082]

[0083] Similarly, the phase at an altitude of -4232m is:

[0084]

[0085] Therefore, when the logging resistivity at burial depth H1 is R1, the logging resistivity at burial depth H2 is R2, and H2>H1, the phase calculation formula can be summarized as:

[0086]

[0087] According to the phase calculation results, the phase of a single well can be divided into positive and negative. A positive phase indicates that the resistivity increases from shallow to deep, entering a high-resistance formation, while a negative phase indicates entering a low-resistance formation.

[0088] Based on the above division results, the phase stratification results of a single well need to be lithologically corrected to eliminate the situation where different strata have similar lithologies, which leads to little phase change. The specific approach is as follows: Based on the results of the single-well phase division formation, the stratification results of the lithological data are quickly compared. If the two are the same, the stratification is accurate. If the two stratification results are different, the phase stratification is corrected according to the stratification results of the lithological data, thereby obtaining a unified single-well stratification result. The key wells in the study area are subjected to rapid phase calculation and stratification according to the above method, and corrected according to the lithological data to obtain the stratigraphic structure of the entire study area.

[0089] It should be noted that lithologic data is obtained through drilling coring or logging cuttings, and can be used for depth tracing using technical data from well logging techniques for determining lithologic properties (such as lithologic scanning logging, which combines density, neutron, photoelectric effect, and energy spectrum data to calculate mineral content and provide detailed mineral composition (such as clay, quartz, calcite, and dolomite), making it suitable for complex lithologic analysis). Lithologic data is displayed in sections by burial depth or altitude, such as fine sandstone at a burial depth of 1000m-1500m, mudstone at 1500m-1700m, and limestone at 1700m-2000m. The stratification results of lithologic data are intuitive and highly reliable, serving as the basis for direct division of strata.

[0090] In some embodiments of the present application, the plurality of wide-area electromagnetic integrated data volumes are respectively a first wide-area electromagnetic integrated data volume, a second wide-area electromagnetic integrated data volume, and a third wide-area electromagnetic integrated data volume, and the first wide-area electromagnetic integrated data volume, the second wide-area electromagnetic integrated data volume, and the third wide-area electromagnetic integrated data volume are obtained by the following steps:

[0091] Before fracturing, the first wide-area apparent resistivity data of each layer in the study area is collected and calculated, the data volume of the target layer is deducted, the wide-area electromagnetic wave data of the target layer is encrypted and collected, and the second wide-area apparent resistivity data is calculated. The second wide-area apparent resistivity data is filled into the data volume of the target layer to obtain the first wide-area electromagnetic integrated data volume of each layer in the study area;

[0092] During the fracturing process, the wide-area electromagnetic wave data of the target layer is encrypted and collected to obtain the third wide-area apparent resistivity data. After deducting the data volume of the target layer, the third wide-area apparent resistivity data is added to the data volume of the target layer to obtain the second wide-area electromagnetic integrated data volume of each layer in the study area.

[0093] After fracturing, the wide-area electromagnetic wave data of the target layer are encrypted and collected to obtain the fourth wide-area apparent resistivity data. The data volume of the target layer is deducted and the fourth wide-area apparent resistivity data is added to the data volume of the target layer to obtain the third wide-area electromagnetic integrated data volume of each layer in the study area.

[0094] It should be noted that the terms "pre-fracture acquisition," "during fracking," and "post-fracture" refer to the time points of wide-area electromagnetic acquisition. These time points are used only as examples to illustrate this technical solution. In practice, smaller time intervals are often required, such as "pre-fracture acquisition," "just after fracking," "nth minute after fracking," "n+mth minute after fracking," "just after fracking," "kth minute after fracking," etc., where n, m, and k represent numbers and are determined based on actual conditions. At each moment, encrypted acquisition is performed only for the target layer.

[0095] In the embodiment of the present application, when obtaining and calculating the first wide-area electromagnetic wave data of each layer in the study area, it should be before any artificial measures such as hydraulic fracturing and injection of low-resistance fluid are carried out in the study area, that is, the first wide-area electromagnetic wave data is the original formation information without human intervention.

[0096] In some embodiments, based on the wide-area electromagnetic method, the apparent resistivity data of the specified area is obtained, which can be the first wide-area electromagnetic wave data of the specified area collected by wide-area electromagnetic equipment. The data is processed by denoising, improving the signal-to-noise ratio, Fourier transform and calculation to obtain the first wide-area apparent resistivity data of each layer in the specified area.

[0097] Wide-area electromagnetic equipment includes wide-area high-power transmitters and wide-area high-precision receivers. α It can be calculated by the following formula:

[0098]

[0099] Among them, E x is the x component of the electric field of the wide-area electromagnetic device, MN is the distance between adjacent receiving points of the wide-area electromagnetic device, I is the magnitude of the harmonic current sent by the wide-area electromagnetic device, K is the device coefficient of the observation device of the wide-area electromagnetic device, F(ikr) is the electromagnetic effect coefficient, dL is the distance of the electric dipole source of the wide-area electromagnetic device, and r is the transmitting and receiving distance of the wide-area electromagnetic device; is the azimuth, k is the wave number, and i is the imaginary unit.

[0100] In the present embodiment, the top and bottom depths of the target layer are obtained using the stratification results of the lithologic data. Typically, to improve the accuracy of the results, both the top and bottom depths of the target layer need to be expanded by 5-15 meters. That is, the top depth is reduced by 5-15 meters, and the bottom depth is increased by 5-15 meters, to obtain a "variable depth range" to include more stratigraphic information.

[0101] It should be noted that the above-mentioned extended range of 5m-15m can be increased or decreased according to actual conditions, and is not limited to the range of 5m-15m, and cannot be regarded as a limitation of this application.

[0102] After obtaining the "variable vertical depth range", the wide-area apparent resistivity data within the "variable vertical depth range" in the first wide-area apparent resistivity data covering all strata are deleted according to the top and bottom depths of the variable vertical depth range, leaving a blank part to make room for the subsequent encrypted collection of wide-area apparent resistivity data.

[0103] In some embodiments, based on spectrum encryption technology and wide-area electromagnetic method, second wide-area apparent resistivity data of a variable depth range in the area to be identified is obtained, including:

[0104] According to the "variable depth range", obtain the top and bottom depths of the variable depth range;

[0105] Based on the skin depth formula, the wide-area emission frequency range of the variable depth range is obtained according to the top and bottom depths of the variable depth range;

[0106] Based on spectrum encryption technology, frequency points are encrypted within the wide-area transmission frequency range of the target layer;

[0107] Based on the wide-area electromagnetic method, second wide-area electromagnetic wave data with a variable depth range collected by the wide-area electromagnetic equipment is obtained;

[0108] Second wide-area apparent resistivity data within a variable depth range is obtained based on the second wide-area electromagnetic wave data.

[0109] In the embodiments of this application, wide-area electromagnetic spectrum encryption technology is used to achieve encrypted acquisition of wide-area electromagnetic signals at a specified depth. According to the principle of electromagnetic wave detection, the density of electromagnetic wave signals gradually decreases with increasing detection depth. Therefore, when measuring deep strata, the resolution of electromagnetic methods is often low. Therefore, spectrum encryption technology is needed to collect more wide-area electromagnetic signals from the target strata at greater burial depths.

[0110] Generally, the transmission frequency can be designed according to the thickness of the target layer, and the number of acquisition frequency points can be increased from the unencrypted 1-3 to 10-30, which can increase the data volume by 10 times, thereby effectively improving the wide-area electromagnetic longitudinal resolution of the target layer.

[0111] The above skin depth formula is as follows:

[0112]

[0113] Where k is a coefficient ranging from 355 to 357, and is generally taken as 356; D is the detection depth in meters; ρ is the apparent resistivity in Ω·m, which represents the comprehensive resistivity of multiple layers from the surface to a certain depth; f is the frequency of the transmitted signal in Hz.

[0114] According to the top and bottom depths of the variable depth range and the first wide-area apparent resistivity data, the frequency range of the wide-area electromagnetic emission can be approximately estimated.

[0115] It should be noted that the skin depth formula here is an approximation. Typically, k is set to 356, but this can be adjusted based on actual conditions. Generally, the transmission frequency of wide-area electromagnetic equipment can range from 0.0020 Hz to 15728.6 Hz, and the transmission frequency can be adjusted based on actual conditions. Generally, higher frequencies result in shallower detection depths, while higher frequencies result in deeper detection depths.

[0116] Specifically, the top and bottom information of each layer is obtained through phase and lithology data, and the top and bottom interfaces of the target layer are expanded outward to a certain range to obtain the top and bottom interfaces of the "variable depth range".

[0117] The skin depth formula is used to convert the transmission frequency range for spectrum encryption. Spectral encryption technology is used to encrypt the frequency groups within the transmission frequency range. The corresponding wide-area pseudo-random signal data is simultaneously collected to form the second wide-area electromagnetic wave data. Subsequently, through denoising, Fourier transform, and calculation, the second wide-area apparent resistivity data with a "variable depth range" is obtained. Finally, the second wide-area apparent resistivity data is inserted into the blanks in the first wide-area apparent resistivity data to form the first wide-area electromagnetic integrated data volume.

[0118] The process of obtaining multiple wide-area electromagnetic integrated data volumes is shown in Table 1:

[0119] Table 1. The process of obtaining the wide-area electromagnetic integrated data volume

[0120]

[0121] In the embodiment of the present application, the third wide-area apparent resistivity data, the fourth wide-area apparent resistivity data and other subsequently collected data are all collected after artificial measures such as injecting low-resistance fracturing fluid into the high-resistance target layer, and the positions of the collected measuring points remain unchanged. The transmission frequency for the "variable depth range" detection remains unchanged, and the number of collection times and the interval between two collections can be adjusted according to actual needs.

[0122] In some embodiments, the third wide-area apparent resistivity data is collected simultaneously with the injection of low-resistance fracturing fluid, the fourth wide-area apparent resistivity data is collected 30 minutes to 1 hour apart from the third wide-area apparent resistivity data, the fifth wide-area apparent resistivity data is collected 3 to 6 hours apart from the fourth wide-area apparent resistivity data, the sixth wide-area apparent resistivity data is collected 6 to 12 hours apart from the fifth wide-area apparent resistivity data, and the seventh wide-area apparent resistivity data is collected 12 to 48 hours apart from the sixth wide-area apparent resistivity data.

[0123] In this embodiment of the present application, the third and fourth wide-area electromagnetic wave data only need to be collected within the "variable depth range" and are encrypted and collected using spectrum encryption technology. The newly collected data also requires denoising, signal-to-noise ratio enhancement, and Fourier transform operations to calculate the corresponding third and fourth wide-area apparent resistivity data. These data are then added to the first wide-area apparent resistivity data, minus the "variable depth range," to obtain the integrated wide-area electromagnetic data volume after each acquisition.

[0124] Then, multiple wide-area electromagnetic integrated data volumes are sequentially constrained inversion performed in the geoelectric model, and multiple wide-area apparent resistivity profiles are obtained in one-to-one correspondence.

[0125] The above-mentioned constrained inversion adopts a conventional inversion method, which is restricted by introducing an established geological framework model (i.e., geoelectric model), optimizes the model parameter solution process, and improves the stability and reliability of the inversion results. In an embodiment of the present application, the geological framework model established by logging resistivity data and lithology data is a hard constraint condition, and a regularized constrained inversion method is used to limit the complexity of the inversion results through regularization terms (such as model smoothness and well data matching) to ensure that the results are consistent with the well data and minimize the residual between the electromagnetic observation data and the forward response as much as possible. It should be noted that the specific inversion process is a prior art known to those skilled in the art and will not be described in detail here.

[0126] refer to Figures 2 to 5 , Figure 2 is a schematic diagram of a wide-area apparent resistivity profile according to an embodiment of the present application. Figure 3 This is a comparative schematic diagram of the first wide-area apparent resistivity profile of the same survey line in one embodiment of the present application. Figure 4 This is a comparative schematic diagram of the second wide-area apparent resistivity profile of the same survey line in one embodiment of the present application. Figure 5 This is a schematic diagram comparing the third wide-area apparent resistivity profile taken along the same survey line in one embodiment of the present application. The third and fourth wide-area electromagnetic wave data can be collected multiple times at the same survey point, simultaneously acquiring the latest logging porosity data. By comparing the fluid identification results from these multiple profiles, the dynamic characteristics of low-resistance fluids in high-resistance formations can be determined, allowing accurate determination of the direction and current position of low-resistance fluids in the high-resistance target layer, i.e., the initial water invasion front position of the target layer.

[0127] refer to Figures 3 to 5 The front position of the low-resistance fluid within the “variable depth range” can be judged based on the advance of the low-resistance fluid in each wide-area apparent resistivity profile of each survey line.

[0128] In some embodiments of the present application, the target resistivity variation data is obtained by the following steps:

[0129] Constrained inversion is performed sequentially on multiple wide-area electromagnetic integrated data volumes in the geoelectric model, and multiple wide-area apparent resistivity profiles are obtained in one-to-one correspondence.

[0130] Extract the difference between the wide-area apparent resistivity data of the "variable depth range" in the two recent wide-area apparent resistivity profiles of the target layer and record it as the initial resistivity change data;

[0131] Extract the wide-area apparent resistivity data of the "variable depth range" in the wide-area apparent resistivity profile corresponding to the target layer before fracturing, and record it as the initial wide-area apparent resistivity data;

[0132] The ratio of the initial resistivity variation data to the initial wide-area apparent resistivity data is determined as the target resistivity variation data for the "variable depth range".

[0133] In some embodiments of the present application, the target porosity variation data is obtained by the following steps:

[0134] Obtaining the first logging porosity data of the target layer in the "variable depth range" before fracturing and the second logging porosity data of the current "variable depth range" after fracturing;

[0135] Calculating the difference between the first well logging porosity data and the second well logging porosity data to obtain porosity change data;

[0136] The ratio of the porosity change data to the first logging porosity data is determined as the target porosity change data of the "variable depth range".

[0137] In some embodiments of the present application, the target permeability data is obtained by the following steps:

[0138] Obtain the logging permeability data of the current "variable depth range" of the target layer after fracturing;

[0139] The logarithm of the well logging permeability data is determined as the target permeability data.

[0140] It's important to note that the porosity and permeability values ​​obtained for each well are distributed continuously vertically, with a 0.125m interval between adjacent data sets. This means that each porosity and permeability measurement contains a significant amount of data. In some embodiments, porosity and permeability are typically measured at most twice. If cost is not a concern, multiple measurements can be performed to obtain multiple data sets.

[0141] In some embodiments of the present application, identifying low-resistivity fluids in high-resistivity formations solely through wide-area apparent resistivity can be subject to interfering factors such as reservoir properties, reservoir lithology, and reservoir fluids. Tight reservoir properties can cause an abnormally high wide-area apparent resistivity; differences in reservoir lithology can also cause anomalies in wide-area apparent resistivity; and when a reservoir contains high-resistivity oil and gas fluids, this can also cause anomalies in wide-area apparent resistivity. These interfering factors must be eliminated when determining the location of low-resistivity fluids in high-resistivity reservoirs.

[0142] This application provides a combined physical property and fluid identification template that can be used to analyze the cause of target layer resistivity anomalies and eliminate the possibility that reservoir physical properties interfere with resistivity. Furthermore, this template can further clarify reservoir physical property differences, enabling more accurate identification of the distribution of high-resistance oil and gas fluids and the location of the low-resistance fluid front in reservoirs with different physical properties.

[0143] Next, the principles and process of establishing the physical property and fluid joint identification template are explained in detail.

[0144] First, introduce the standard boundary equation:

[0145]

[0146] Wherein, Δρ is the difference between the wide-area apparent resistivity data of the “variable depth range” in the two most recent wide-area apparent resistivity profiles, which is used to indicate the change in reservoir resistivity; ρ0 is the wide-area apparent resistivity data of the “variable depth range” in the wide-area apparent resistivity profile corresponding to before fracturing; It is the difference between the first logging porosity data of the “variable depth range” before fracturing and the second logging porosity data of the current “variable depth range” after fracturing, and is used to reflect the change of reservoir space; is the first logging porosity data of the “variable depth range” before fracturing; β is the permeability influence coefficient, usually 0.3-0.5, which needs to be calibrated by core experiments, and is 0.4 in this case; K is the logging permeability data of the current “variable depth range” after fracturing, which is the most recently obtained logging permeability, K ref As the reference permeability, 50mD is taken this time.

[0147] The correlation model between logging porosity and logging permeability is built, and the corresponding conversion function is improved according to the Kozeny-Carman equation:

[0148]

[0149] Where K is the logging permeability, which is used to reflect and control the fluid migration efficiency; C is the rock structure coefficient, which is 0.05-0.1 for carbonate rock and 0.05 in this study; is the logging porosity; S wiis the irreducible water saturation, which can be obtained from indoor tests or nuclear magnetic resonance logging.

[0150] Formula (9) can be simplified as follows:

[0151]

[0152] At the same time, the logging permeability is normalized to avoid the influence of different dimensions and data differences on the experimental results:

[0153]

[0154] Among them, K norm is the normalized logging permeability, K is the currently input logging permeability, K max is the maximum permeability, K min is the minimum permeability.

[0155] In this embodiment, the regulatory effect of permeability K on fluid intrusion needs to be considered, as the permeability value directly affects the fluid displacement efficiency. In high permeability regions (K>100mD), fluid intrusion is rapid, and the resistivity response is significant. In low permeability regions (K<10mD), intrusion is limited, and resistivity changes are delayed or weak.

[0156] In some embodiments of the present application, reference is made to Figure 6 , the physical property and fluid joint identification template is obtained through the following steps:

[0157] Obtain multiple resistivity change data, multiple porosity change data, and multiple permeability data of the target layer in the study area before and after fracturing;

[0158] Determining parameter characteristic ranges corresponding to different geological characteristic regions based on a plurality of resistivity change data and corresponding preset first weights, a plurality of porosity change data and corresponding preset second weights, and a plurality of permeability data and corresponding preset third weights, wherein the geological characteristic regions include low-resistance fluid characteristic regions and interference characteristic regions, and the parameter characteristic ranges include a porosity change range, a resistivity change range, and a permeability range;

[0159] Based on the parameter characteristic ranges corresponding to different geological characteristic areas, a joint identification template of physical properties and fluid is obtained.

[0160] In some embodiments of the present application, reference is made to Figure 6 , the low-resistance fluid characteristic area includes the flooded area and the transition zone, the interference characteristic area includes the abnormally high-pressure area and the dense shielding area, the geological characteristic area also includes the original formation area, and the parameter characteristic range includes the first characteristic range, the second characteristic range, the third characteristic range, the fourth characteristic range and the fifth characteristic range;

[0161] Determining parameter characteristic ranges corresponding to different geological characteristic regions based on a plurality of resistivity change data and corresponding preset first weights, a plurality of porosity change data and corresponding preset second weights, and a plurality of permeability data and corresponding preset third weights includes:

[0162] According to multiple resistivity change data and corresponding preset first weights, multiple porosity change data and corresponding preset second weights, and multiple permeability data and corresponding preset third weights, the flooded area and the corresponding first characteristic range, the transition zone and the corresponding second characteristic range, the original formation area and the corresponding third characteristic range, the abnormally high pressure area and the corresponding fourth characteristic range, and the dense shielding area and the corresponding fifth characteristic range are determined.

[0163] In some embodiments, the resistivity variation data is The porosity change data is Permeability data is log 10 (K).

[0164] The resistivity change data indicates the conductivity of the fluid in the "variable depth range", and the preset first weight is set to 0.8; the porosity change data reflects the change in reservoir space, and the preset second weight is set to 0.6; the permeability data controls the fluid migration efficiency, and the preset third weight is set to 0.5.

[0165] It should be noted that the specific weight distribution can be changed according to actual conditions and should not be regarded as a limitation of this application.

[0166] In an embodiment of the present application, a Python program is used for machine learning, a random forest model (RandomForest) is selected, and a training set (i.e., multiple resistivity change data, multiple porosity change data, and multiple permeability data) is given to classify and optimize the multi-parameter combination. The weight distribution of different parameters affects the division of the parameter characteristic range, thereby obtaining the parameter characteristic range corresponding to different geological characteristic areas.

[0167] The flooded area represents the low-resistance fluid invasion area, the transition zone represents the front edge of the low-resistance fluid, and the original formation represents the range not affected by the low-resistance fluid.

[0168] The parameter characteristic range reflects the characteristics of the geological characteristic area, such as: flooded area Significantly negative values ​​(-0.8 to -0.3) reflect the intrusion of low-resistance fluids, log 10 (K) is relatively high (2.8-3.5), corresponding to rapid fluid migration in high permeability areas. Reflecting the dominance of high-resistivity lithology, log 10 (K)<0.5, corresponding to extremely low permeability, excluding fluid interference.

[0169] The interference mechanism between abnormally high pressure areas and dense obscured areas is:

[0170] (1) Interference manifestations in abnormally high-pressure areas: the development of fracture-cavern system leads to (Abnormally low resistivity) can be easily misjudged as low-resistance fluid intrusion.

[0171] Elimination method: Combine (dramatic changes in porosity) and log 10 (K)>3.5 (super permeability) identifies the high-pressure zone dominated by fractures, and fluid enters non-through the pores.

[0172] (2) Interference performance in dense occlusion areas: (high resistance), which may be misjudged as an oil and gas enrichment area.

[0173] Troubleshooting method: log 10 (K) < 0.5 (very low permeability) indicates that the reservoir is tight. (Decrease in porosity) reflects cementation and excludes fluid activity.

[0174] Through multi-stage wide-area apparent resistivity profiles after fracturing construction, the low-resistivity fluid advance path was tracked and compared with the template classification results.

[0175] The physical property and fluid joint identification template divides the resistivity anomaly area into the flooded area (low-resistance fluid), transition zone (front edge), original formation area (unaffected) and interference feature area (abnormally high-pressure area, dense obstruction area) through multi-parameter combination classification and interference area characteristic threshold, thereby eliminating the interference of lithology and physical properties and realizing the accurate identification of low-resistance fluid in high-resistance formation.

[0176] Table 2 is the characteristic region table of the physical property and fluid joint recognition template:

[0177] Table 2 Feature area table of physical property and fluid joint recognition template

[0178]

[0179] It should be noted that the purpose of the standard boundary equation is to dynamically evaluate the impact of fluid invasion in a reservoir using multiple parameters. Although the variables in the equation are all known measured values, the equation works in the following way:

[0180] (1) Quantify the change in fluid conductivity: Directly reflects the dynamic changes in reservoir resistivity caused by fluid invasion.

[0181] (2) Correlating reservoir space and fluid migration: Introduction (the absolute value of the porosity change) and K (permeability) are used as correction factors to correlate reservoir properties (porosity, permeability) with fluid migration efficiency (regulated by permeability), avoiding misjudgment caused by relying solely on resistivity anomalies.

[0182] (3) Eliminate interference factors: through the permeability influence coefficient β and reference permeability K ref , correct the differences in fluid invasion velocities under different permeability conditions (e.g., the resistivity response is significant when K>100mD in high permeability areas, and the response is delayed when K<10mD in low permeability areas), thereby eliminating the interference of reservoir physical property differences on resistivity anomalies.

[0183] (4) Weight allocation optimization judgment: In Eq. The weight coefficient of K reflects the contribution of different parameters to fluid identification, and ultimately outputs a comprehensive index for determining the position of the low-resistance fluid leading edge.

[0184] The standard boundary equation does not solve unknown variables, but achieves quantitative analysis of reservoir fluid invasion characteristics through dynamic combination and weight distribution of known parameters, thereby improving the accuracy of low-resistance fluid identification.

[0185] There is a technically logical progressive relationship between the standard boundary equation and the physical property and fluid joint identification template. The specific relationship is as follows:

[0186] (1) The standard boundary equation provides a parameter dynamic analysis basis for the joint identification template of physical properties and fluid

[0187] The role of the standard boundary equation: by introducing K, constructing a multi-parameter joint equation to quantify the dynamic impact of fluid invasion on reservoir resistivity and physical properties. The core of this equation is to comprehensively determine the location of the low-resistivity fluid front through weight distribution.

[0188] Input parameters of the physical property and fluid joint identification template: three key parameters in the standard boundary equation Directly used as input features for the joint recognition template of physical properties and fluid.

[0189] (2) The standard boundary equation provides a physical property-fluid correlation model for the joint identification template of physical properties and fluids

[0190] Correction logic of the standard boundary equation: through the permeability influence coefficient β and reference permeability K ref , correct the differences in fluid invasion velocities under different permeability conditions (e.g., the resistivity response is significant when K>100mD in the high permeability zone, and the response is delayed when K<10mD in the low permeability zone), thereby eliminating the interference of physical property differences on resistivity anomalies.

[0191] The classification basis of the joint identification template of physical properties and fluids: the modified results of the standard boundary equation (such as the response difference between high permeability areas and low permeability areas) are used to define the parameter characteristic range corresponding to different geological characteristic areas in the discrimination template.

[0192] (3) The physical property and fluid joint identification template is a multi-parameter modeling application of the standard boundary equation

[0193] Quantitative output of the standard boundary equation: The standard boundary equation outputs a comprehensive index through multi-parameter weight distribution, which is used to preliminarily determine the position of the fluid front.

[0194] Extension of the joint identification template for physical properties and fluids: The parameter combinations of the standard boundary equations are nonlinearly classified using a random forest model, and the comprehensive index is expanded to multi-region classification (such as flooded areas, transition zones, and undisturbed stratigraphic areas).

[0195] (4) Technical division of labor and collaboration

[0196] Positioning of standard boundary equations: used to dynamically correct the correlation between physical parameters and fluid invasion, and solve the problem of misjudgment of single resistivity anomalies (such as eliminating high-resistance interference of tight reservoirs in high-resistance formations).

[0197] Positioning of the joint identification template of physical properties and fluids: Based on the multi-parameter analysis results of the joint identification template of physical properties and fluids, machine learning is used to achieve regional division in high-dimensional feature space to improve classification accuracy.

[0198] In summary: The standard boundary equations (SBEs) provide the physical foundation for the simultaneous identification template for physical properties and fluids, dynamically correlating physical parameters with fluid responses. The SBEs (SPEs) are an engineering application of the SBEs, enabling multi-region classification under complex geological conditions through machine learning. Together, they address the technical challenge of identifying low-resistivity fluid fronts in high-resistivity formations.

[0199] According to the method for identifying the position of the water invasion front of a high-resistance formation in an embodiment of the present application, the present application abandons the huge amount of seismic data and only uses logging resistivity data and lithology data to build the formation framework model, which reduces the burden of structural modeling and the workload of data processing. While minimizing the model stratification error as much as possible, it greatly improves the efficiency of model building. The initial water invasion front position of the target layer can be determined by the dynamic change characteristics of low-resistance fluids in multiple wide-area apparent resistivity profiles. However, in high-resistance formations, identifying low-resistance fluids only through wide-area apparent resistivity will be subject to interference factors such as reservoir physical properties, reservoir lithology, and reservoir fluids. Under tight conditions, reservoir physical properties will cause an abnormal increase in wide-area apparent resistivity; differences in reservoir lithology will also cause wide-area apparent resistivity anomalies; when the reservoir contains high-resistance oil and gas fluids, it will also cause wide-area apparent resistivity anomalies. By calibrating the parameter characteristic ranges corresponding to different geological characteristic areas using the physical property and fluid joint identification template, the corresponding low-resistance fluid characteristic area can be determined based on the target resistivity change data, target porosity change data, and target permeability data, eliminating the interference of the interfering characteristic areas, thereby achieving accurate identification of low-resistance fluids in high-resistance reservoirs.

[0200] According to the second aspect of the present application, a system for identifying the position of a water invasion front in a high-resistance formation includes a geoelectric model establishment unit, a data acquisition unit, a wide-area apparent resistivity profile determination unit, an initial water invasion front position determination unit, and a target water invasion front position determination unit.

[0201] The geoelectric model building unit is used to obtain the well logging resistivity data and lithology data of the study area, divide the layers according to the well logging resistivity data and lithology data, and build the geoelectric model;

[0202] A data acquisition unit is used to acquire, based on a geoelectric model, multiple wide-area electromagnetic integrated data volumes corresponding to multiple periods before, during, and after fracturing for a target layer in a study area, target resistivity change data, target porosity change data, and target permeability data for the target layer, as well as a physical property and fluid joint identification template. The physical property and fluid joint identification template is used to characterize parameter characteristic ranges corresponding to different geological characteristic regions. The geological characteristic regions include low-resistance fluid characteristic regions and interference characteristic regions. The parameter characteristic ranges include porosity change ranges, resistivity change ranges, and permeability ranges.

[0203] A wide-area apparent resistivity profile determination unit is used to sequentially perform constrained inversion on multiple wide-area electromagnetic integrated data volumes in the geoelectric model to obtain multiple wide-area apparent resistivity profiles in one-to-one correspondence;

[0204] An initial water invasion front position determination unit is used to determine the initial water invasion front position of the target layer based on the dynamic change characteristics of the low-resistance fluid in multiple wide-area apparent resistivity profiles;

[0205] The target water invasion front position determination unit is used to eliminate the interference feature area of ​​the initial water invasion front position based on the target resistivity change data, the target porosity change data, the target permeability data and the physical property and fluid joint identification template to obtain the target water invasion front position.

[0206] According to the water invasion front position identification system for high-resistance formations in the embodiment of the present application, the present application abandons the huge amount of seismic data and only uses logging resistivity data and lithology data to build the formation framework model, which reduces the burden of structural modeling and the workload of data processing. While minimizing the model stratification error as much as possible, it greatly improves the efficiency of model building. The initial water invasion front position of the target layer can be determined by the dynamic change characteristics of low-resistance fluids in multiple wide-area apparent resistivity profiles. However, in high-resistance formations, identifying low-resistance fluids only through wide-area apparent resistivity will be subject to interference factors such as reservoir physical properties, reservoir lithology, and reservoir fluids. Under tight conditions, reservoir physical properties will cause an abnormal increase in wide-area apparent resistivity; differences in reservoir lithology will also cause wide-area apparent resistivity anomalies; when the reservoir contains high-resistance oil and gas fluids, it will also cause wide-area apparent resistivity anomalies. By calibrating the parameter characteristic ranges corresponding to different geological characteristic areas using the physical property and fluid joint identification template, the corresponding low-resistance fluid characteristic area can be determined based on the target resistivity change data, target porosity change data, and target permeability data, eliminating the interference of the interfering characteristic areas, thereby achieving accurate identification of low-resistance fluids in high-resistance reservoirs.

[0207] Since the system for identifying the position of the water invasion front edge in high-resistance formations adopts all the technical solutions of the method for identifying the position of the water invasion front edge in high-resistance formations in the above embodiment, it has at least all the beneficial effects brought by the technical solutions of the above embodiment, which will not be described in detail here.

[0208] In addition, an embodiment of the present application further 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 may be connected via a bus or other means.

[0209] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0210] The non-transient software program and instructions required to implement the method for identifying the water invasion front position of a high-resistance formation in the above embodiment are stored in the memory. When executed by the processor, the method for identifying the water invasion front position of a high-resistance formation in the above embodiment is executed.

[0211] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0212] In addition, an embodiment of the present application also provides a computer-readable storage medium, which stores computer-executable instructions. The computer-executable instructions are executed by a processor or controller, for example, by the processor of the above embodiment, so that the above processor can execute the method for identifying the water invasion front position of the high-resistance formation in the above embodiment.

[0213] Those skilled in the art will appreciate that all or some of the steps and systems in the method disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those skilled in the art, the term computer storage media is included in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data) and is volatile and non-volatile, removable, and non-removable. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disks (DVD), or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage, or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0214] The embodiments of the present application are described in detail above in conjunction with the accompanying drawings, but the present application is not limited to the above embodiments. Various changes can be made within the scope of knowledge possessed by ordinary technicians in the relevant technical field without departing from the purpose of the present application.

Claims

1. A method for identifying the position of the water invasion front in a high-resistance formation, characterized in that: The method comprises: Acquiring well logging resistivity data and lithologic data of the study area, dividing the horizons according to the well logging resistivity data and the lithologic data, and establishing a geoelectric model; Based on the geoelectric model, a plurality of wide-area electromagnetic integrated data volumes corresponding to the target layer in the study area at multiple periods before, during, and after fracturing are obtained, as well as target resistivity change data, target porosity change data, and target permeability data of the target layer, and a physical property and fluid joint identification template, the physical property and fluid joint identification template being used to characterize parameter characteristic ranges corresponding to different geological characteristic regions, the geological characteristic regions including low-resistance fluid characteristic regions and interference characteristic regions, and the parameter characteristic ranges including porosity change ranges, resistivity change ranges, and permeability ranges; performing constrained inversion on the plurality of wide-area electromagnetic integrated data volumes in the geoelectric model in sequence, and obtaining a plurality of wide-area apparent resistivity profiles in one-to-one correspondence; determining the position of the initial water invasion front of the target layer based on the dynamic change characteristics of the low-resistance fluid in the plurality of wide-area apparent resistivity profiles; The initial water invasion front position is subjected to interference feature area exclusion processing according to the target resistivity change data, the target porosity change data, the target permeability data, and the physical property and fluid joint identification template to obtain a target water invasion front position.

2. The method for identifying the water invasion front position in a high-resistance formation according to claim 1, characterized in that: The target resistivity change data is obtained by the following steps: performing constrained inversion on the plurality of wide-area electromagnetic integrated data volumes in the geoelectric model in sequence, and obtaining a plurality of wide-area apparent resistivity profiles in one-to-one correspondence; Extracting the difference between the wide-area apparent resistivity data of the target layer and the two recent wide-area apparent resistivity profiles, and recording it as the initial resistivity variation data; Extracting the wide-area apparent resistivity data of the wide-area apparent resistivity profile corresponding to the target layer before fracturing, and recording it as initial wide-area apparent resistivity data; The ratio of the initial resistivity change data to the initial wide-area apparent resistivity data is determined as the target resistivity change data.

3. The method for identifying the water invasion front position in a high-resistance formation according to claim 1, characterized in that: The target porosity variation data is obtained by the following steps: Acquire first logging porosity data of the target layer before fracturing and current second logging porosity data after fracturing; Calculating a difference between the first well logging porosity data and the second well logging porosity data to obtain porosity change data; The ratio of the porosity change data to the first well logging porosity data is determined as the target porosity change data.

4. The method for identifying the water invasion front position in a high-resistance formation according to claim 1, characterized in that: The target permeability data is obtained by the following steps: Obtain the current logging permeability data of the target layer after fracturing; The logarithm of the well logging permeability data is determined as the target permeability data.

5. The method for identifying the water invasion front position in a high-resistance formation according to claim 1, characterized in that: The physical property and fluid joint identification template is obtained by the following steps: Acquiring multiple resistivity change data, multiple porosity change data, and multiple permeability data of the target layer in the study area before and after fracturing; Determining parameter characteristic ranges corresponding to different geological characteristic regions based on the plurality of resistivity change data and corresponding preset first weights, the plurality of porosity change data and corresponding preset second weights, and the plurality of permeability data and corresponding preset third weights, the geological characteristic regions including low-resistance fluid characteristic regions and interference characteristic regions, and the parameter characteristic ranges including a porosity change range, a resistivity change range, and a permeability range; The physical property and fluid joint identification template is obtained based on the parameter characteristic ranges corresponding to different geological characteristic areas.

6. The method for identifying the water invasion front position in a high-resistance formation according to claim 1 or 5, characterized in that: The low-resistance fluid characteristic area includes a flooded area and a transition zone, the interference characteristic area includes an abnormally high-pressure area and a dense obstruction area, the geological characteristic area also includes an original formation area, and the parameter characteristic range includes a first characteristic range, a second characteristic range, a third characteristic range, a fourth characteristic range, and a fifth characteristic range; Determining parameter characteristic ranges corresponding to different geological characteristic regions based on the plurality of resistivity change data and corresponding preset first weights, the plurality of porosity change data and corresponding preset second weights, and the plurality of permeability data and corresponding preset third weights includes: According to the multiple resistivity change data and the corresponding preset first weights, the multiple porosity change data and the corresponding preset second weights, and the multiple permeability data and the corresponding preset third weights, the flooded area and the corresponding first characteristic range, the transition zone and the corresponding second characteristic range, the original formation area and the corresponding third characteristic range, the abnormally high pressure area and the corresponding fourth characteristic range, and the dense shielding area and the corresponding fifth characteristic range are determined.

7. The method for identifying the water invasion front position in a high-resistance formation according to claim 1, characterized in that: The plurality of wide-area electromagnetic integrated data volumes are respectively a first wide-area electromagnetic integrated data volume, a second wide-area electromagnetic integrated data volume, and a third wide-area electromagnetic integrated data volume, wherein the first wide-area electromagnetic integrated data volume, the second wide-area electromagnetic integrated data volume, and the third wide-area electromagnetic integrated data volume are obtained by the following steps: Before fracturing, first wide-area apparent resistivity data of each layer in the study area is collected and calculated, a data volume of a target layer is deducted, wide-area electromagnetic wave data of the target layer is encrypted and collected, and second wide-area apparent resistivity data is calculated. The second wide-area apparent resistivity data is filled into the data volume of the target layer to obtain the first wide-area electromagnetic integrated data volume of each layer in the study area; During the fracturing process, wide-area electromagnetic wave data of the target layer are encrypted and collected to obtain third wide-area apparent resistivity data. The data volume of the target layer is deducted and the third wide-area apparent resistivity data is added to the data volume of the target layer to obtain the second wide-area electromagnetic integrated data volume of each layer in the study area. After fracturing, the wide-area electromagnetic wave data of the target layer are encrypted and collected to obtain fourth wide-area apparent resistivity data. The data volume of the target layer is deducted and the fourth wide-area apparent resistivity data is filled into the data volume of the target layer to obtain the third wide-area electromagnetic integrated data volume of each layer in the study area.

8. A system for identifying the position of water invasion front in high-resistance formations, characterized by: The system comprises: a geoelectric model building unit, configured to obtain well logging resistivity data and lithologic data of a study area, perform layer division based on the well logging resistivity data and the lithologic data, and build a geoelectric model; a data acquisition unit for acquiring, based on the geoelectric model, a plurality of wide-area electromagnetic integrated data volumes corresponding to each other at multiple periods before, during, and after fracturing of a target layer in the study area, target resistivity change data, target porosity change data, and target permeability data of the target layer, and a physical property and fluid joint identification template, the physical property and fluid joint identification template being used to characterize parameter characteristic ranges corresponding to different geological characteristic regions, the geological characteristic regions including low-resistance fluid characteristic regions and interference characteristic regions, and the parameter characteristic ranges including porosity change ranges, resistivity change ranges, and permeability ranges; a wide-area apparent resistivity profile determining unit, configured to sequentially perform constrained inversion on the plurality of wide-area electromagnetic integrated data volumes in the geoelectric model to obtain a plurality of wide-area apparent resistivity profiles in a one-to-one correspondence; an initial water invasion front position determining unit, configured to determine the initial water invasion front position of a target layer based on the dynamic change characteristics of the low-resistance fluid in the plurality of wide-area apparent resistivity profiles; a target water invasion front position determination unit, configured to perform interference feature area elimination processing on the initial water invasion front position based on the target resistivity change data, the target porosity change data, the target permeability data, and the physical property and fluid joint identification template to obtain the target water invasion front position.

9. A control device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for identifying the position of the water invasion front in a high-resistance formation according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that: The computer-executable instructions are used to execute the method for identifying the position of the water invasion front in a high-resistance formation according to any one of claims 1 to 7.

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