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

Abstract

The application discloses a method, system, device and medium for identifying the position of a water invasion front in a high-resistance formation. The method comprises: establishing a geoelectric model; obtaining multiple wide-area electromagnetic integrated data bodies of a target layer before, during and after fracturing, target resistivity variation data, target porosity variation data and target permeability data of the target layer, and a physical property and fluid co-identification template; sequentially performing constrained inversion on the multiple wide-area electromagnetic integrated data bodies in the geoelectric model to obtain multiple wide-area apparent resistivity profiles; determining the initial position of the water invasion front in the target layer according to the low-resistance fluid dynamic variation characteristics of the multiple wide-area apparent resistivity profiles; and performing interference characteristic region exclusion processing on the initial position of the water invasion front according to the target resistivity variation data, the target porosity variation data, the target permeability data and the physical property and fluid co-identification template to obtain the target position of the water invasion front. The application can accurately identify the position of the water invasion front in the high-resistance formation.

Description

Technical Field

[0001] This application relates to the technical fields of oil and gas exploration and development, and in particular to methods, systems, devices and media for identifying the location of water fronts in high resistivity formations. Background Technology

[0002] Identifying fluids in the target formation is a cutting-edge technology in oil and gas exploration and development. Typically, fluids in the target formation include crude oil, natural gas, and water. The water includes formation water originally present in the strata, as well as water-based fracturing fluid injected later during construction. Water intrusion increases construction risks and may lead to anomalies such as pressure channeling and leakage, affecting reservoir development. Therefore, it is crucial to quickly predict the location of the water intrusion front and display its position in real time. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method for identifying the location of the water front of a high-resistivity stratum, which can accurately identify the location of the water front of a high-resistivity stratum.

[0004] This application also provides a water front location identification system for high resistivity formations, a control device for performing the above-described water front location identification method for high resistivity formations, and a computer-readable storage medium.

[0005] A method for identifying the location of the water front of a high-resistivity stratum according to a first aspect embodiment of this application, the method comprising:

[0006] Acquire well logging resistivity data and lithological data of the study area, perform stratigraphic division based on the well logging resistivity data and the lithological data, and establish a geoelectric model;

[0007] Based on the geoelectric model, multiple wide-area electromagnetic integrated data volumes corresponding to the target layer in the study area before, during and after fracturing are obtained. These data include target resistivity change data, target porosity change data and target permeability data of 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 feature range corresponding to different geological feature regions. The geological feature regions include low-resistivity fluid feature regions and interference feature regions. The parameter feature range includes the porosity change range, resistivity change range and permeability range.

[0008] Multiple wide-area electromagnetic integrated data volumes are sequentially constrained in the geoelectric model to obtain multiple wide-area apparent resistivity profiles in a one-to-one correspondence.

[0009] Based on the low-resistivity fluid dynamic change characteristics of multiple wide-area apparent resistivity profiles, the initial water intrusion front position of the target layer is determined.

[0010] 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, the initial water intrusion front position is processed to eliminate interference feature regions, thereby obtaining the target water intrusion front position.

[0011] The method for identifying the location of the water front of a high-resistivity stratum according to the embodiments of this application has at least the following beneficial effects:

[0012] This application abandons the massive seismic data and only uses well logging resistivity data and lithological data to build the stratigraphic framework model, reducing the burden of structural modeling and the workload of data processing. While minimizing model layering errors, it significantly improves model building efficiency. By analyzing the dynamic changes of low-resistivity fluids through multiple wide-area apparent resistivity profiles, the initial water front position of the target layer can be determined. However, in high-resistivity strata, identifying low-resistivity fluids solely through wide-area apparent resistivity is subject to interference from reservoir properties, reservoir lithology, and reservoir fluids. Under tight conditions, reservoir properties can cause an abnormal increase in wide-area apparent resistivity; differences in reservoir lithology can also cause anomalies in wide-area apparent resistivity; and the presence of high-resistivity oil and gas fluids in the reservoir can also cause such anomalies. By identifying the parameter characteristic ranges corresponding to different geological feature regions marked by the property and fluid identification template, the corresponding low-resistivity fluid feature regions can be determined based on the target resistivity change data, target porosity change data, and target permeability data, eliminating interference from other feature regions and thus achieving accurate identification of low-resistivity fluids in high-resistivity reservoirs.

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

[0014] Multiple wide-area electromagnetic integrated data volumes are sequentially constrained in the geoelectric model to obtain multiple wide-area apparent resistivity profiles in a one-to-one correspondence.

[0015] Extract the difference between the wide-area apparent resistivity data of the two most recent wide-area apparent resistivity profiles of the target layer, and record it as the initial resistivity change data.

[0016] Extract the wide-area apparent resistivity data of the wide-area apparent resistivity profile corresponding to the target layer before fracturing, and record it as the 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 this application, the target porosity change data is obtained through the following steps:

[0019] Obtain the first logging porosity data before fracturing the target formation and the current second logging porosity data after fracturing;

[0020] Calculate the difference between the first logging porosity data and the second 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 this application, the target penetration rate data is obtained through the following steps:

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

[0024] The logarithm of the well logging permeability data is used to determine the target permeability data.

[0025] According to some embodiments of this application, the physical property and fluid-related identification template is obtained through the following steps:

[0026] Acquire 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] Based on 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, parameter characteristic ranges corresponding to different geological feature regions are determined. The geological feature regions include low-resistivity fluid feature regions and interference feature regions. The parameter characteristic ranges include porosity change range, resistivity change range, and permeability range.

[0028] The physical property and fluid joint identification template is obtained based on the parameter feature range corresponding to different geological feature regions.

[0029] According to some embodiments of this application, the low-resistivity fluid characteristic region includes a flooded area and a transition zone, the interference characteristic region includes an abnormally high pressure area and a dense shielding area, the geological characteristic region also includes a undisturbed stratum 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] The step of determining the parameter characteristic range corresponding to different geological feature regions based on 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 includes:

[0031] Based on 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 corresponding first characteristic range, the transition zone and corresponding second characteristic range, the undisturbed stratum area and corresponding third characteristic range, the abnormal high pressure area and corresponding fourth characteristic range, and the dense shielding area and corresponding fifth characteristic range are determined.

[0032] According to some embodiments of this application, the plurality of wide-area electromagnetic integrated data bodies are respectively a first wide-area electromagnetic integrated data body, a second wide-area electromagnetic integrated data body, and a third wide-area electromagnetic integrated data body. The first wide-area electromagnetic integrated data body, the second wide-area electromagnetic integrated data body, and the third wide-area electromagnetic integrated data body are obtained through the following steps:

[0033] 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, and the wide-area electromagnetic wave data of the target layer is collected in a encrypted manner to calculate the second wide-area apparent resistivity data. 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, the wide-area electromagnetic wave data of the target layer is collected in encrypted form, and the third wide-area apparent resistivity data is calculated. The data volume of the target layer is deducted, and the third wide-area apparent resistivity data is filled into 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 is encrypted and collected to calculate 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 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] A water front location identification system for high resistivity formations according to a second aspect embodiment of this application, the system comprising:

[0037] The geoelectric model establishment unit is used to acquire well logging resistivity data and lithological data of the study area, perform stratigraphic division based on the well logging resistivity data and the lithological data, and establish a geoelectric model.

[0038] The data acquisition unit is used to acquire, based on the geoelectric model, multiple wide-area electromagnetic integrated data volumes corresponding to multiple periods before, during and after fracturing of the target layer in the study area, including target resistivity change data, target porosity change data and target permeability data of 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 feature range corresponding to different geological feature regions. The geological feature regions include low-resistivity fluid feature regions and interference feature regions. The parameter feature range includes the porosity change range, resistivity change range and permeability range.

[0039] The 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 a one-to-one correspondence.

[0040] The initial water intrusion front position determination unit is used to determine the initial water intrusion front position of the target layer based on the low-resistivity fluid dynamic change characteristics of multiple wide-area apparent resistivity profiles.

[0041] The target water intrusion front location determination unit is used to perform interference feature region elimination processing on the initial water intrusion front location 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, so as to obtain the target water intrusion front location.

[0042] The water front location identification system for high-resistivity formations according to the embodiments of this application has at least the following beneficial effects:

[0043] This application abandons the massive seismic data and only uses well logging resistivity data and lithological data to build the stratigraphic framework model, reducing the burden of structural modeling and the workload of data processing. While minimizing model layering errors, it significantly improves model building efficiency. By analyzing the dynamic changes of low-resistivity fluids through multiple wide-area apparent resistivity profiles, the initial water front position of the target layer can be determined. However, in high-resistivity strata, identifying low-resistivity fluids solely through wide-area apparent resistivity is subject to interference from reservoir properties, reservoir lithology, and reservoir fluids. Under tight conditions, reservoir properties can cause an abnormal increase in wide-area apparent resistivity; differences in reservoir lithology can also cause anomalies in wide-area apparent resistivity; and the presence of high-resistivity oil and gas fluids in the reservoir can also cause such anomalies. By identifying the parameter characteristic ranges corresponding to different geological feature regions marked by the property and fluid identification template, the corresponding low-resistivity fluid feature regions can be determined based on the target resistivity change data, target porosity change data, and target permeability data, eliminating interference from other feature regions and thus achieving accurate identification of low-resistivity fluids in high-resistivity reservoirs.

[0044] A control device according to a third aspect embodiment of this application 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 water front location identification method for high-resistivity formations as described in the first aspect embodiment. Since the control device employs all the technical solutions of the water front location identification method for high-resistivity formations described in the above embodiments, it possesses at least all the beneficial effects brought about by the technical solutions of the above embodiments.

[0045] According to a fourth aspect embodiment of this application, a computer-readable storage medium stores computer-executable instructions for performing the water front location identification method for high-resistivity formations as described in the first aspect embodiment. Since the computer-readable storage medium employs all the technical solutions of the water front location identification method for high-resistivity formations described in the above embodiments, it possesses at least all the beneficial effects brought about by the technical solutions of the above embodiments.

[0046] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing this application. Attached Figure Description

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

[0048] Figure 1 This is a flowchart of a method for identifying the location of the water front of a high-resistivity stratum according to an embodiment of this application;

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

[0050] Figure 3 This is a comparative schematic diagram of the first wide-area apparent resistivity profile of the same survey line according to an embodiment of this application;

[0051] Figure 4 This is a comparative schematic diagram of the second wide-area apparent resistivity profile of the same survey line according to an embodiment of this application;

[0052] Figure 5 This is a comparative schematic diagram of the third wide-area apparent resistivity profile of the same survey line according to an embodiment of this application;

[0053] Figure 6 This is a schematic diagram of a physical property and fluidity-based identification template according to an embodiment of this application. Detailed Implementation

[0054] The embodiments of this application 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 this application, and should not be construed as limiting this application.

[0055] In the description of this application, 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 the order of the technical features indicated.

[0056] In the description of this application, it should be understood that the orientation descriptions, such as 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, and do not 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 application.

[0057] In the description of this application, it should be noted that, unless otherwise explicitly defined, terms such as "setup," "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 application in conjunction with the specific content of the technical solution.

[0058] In oil and gas exploration and development, hydraulic fracturing is a common production enhancement measure in high-resistivity reservoirs (such as carbonate rocks). In this process, low-resistivity fracturing fluid seeps into the high-resistivity reservoir through pores and throats, forming a series of continuous or discontinuous low-resistivity zones within the reservoir. Generally, in tight reservoir regions (where porosity and permeability are both low to very low), the overall resistivity often appears high. Therefore, it is necessary to analyze the high-resistivity portion of the overall apparent resistivity to determine whether the high resistivity is caused by lithology, high-resistivity fluids, or reservoir properties.

[0059] Currently, no technology on the market can dynamically display the leading edge location of low-resistivity fluids in high-resistivity reservoirs, while also displaying the static distribution of high-resistivity fluids such as oil and gas that have relatively good reservoir properties. Therefore, a new identification method is needed that can identify the causes of high reservoir resistivity, clarify the static distribution areas with relatively good properties and relatively rich oil and gas fluids in high-resistivity formations, and dynamically identify the leading edge location of low-resistivity water-based fracturing fluids. This would provide a reference for improving construction measures and providing early warning of fracturing fluid leakage.

[0060] Based on this, this application proposes a method for identifying the location of the water front of a high-resistivity stratum, which can accurately identify the location of the water front of a high-resistivity stratum.

[0061] The following will combine Figures 1 to 6 The method for identifying the water front location of high resistivity strata according to the embodiments of this application is clearly and completely described. Obviously, the embodiments described below are some embodiments of this application, not all embodiments.

[0062] refer to Figures 1 to 6 , Figure 1 This is a flowchart of a method for identifying the location of the water front of a high-resistivity stratum according to an embodiment of this application; Figure 2 This is a schematic diagram of a wide-area apparent resistivity profile according to an embodiment of this application; Figure 3 This is a comparative schematic diagram of the first wide-area apparent resistivity profile of the same survey line according to an embodiment of this application; Figure 4 This is a comparative schematic diagram of the second wide-area apparent resistivity profile of the same survey line according to an embodiment of this application; Figure 5 This is a comparative schematic diagram of the third wide-area apparent resistivity profile of the same survey line according to an embodiment of this application; Figure 6 This is a schematic diagram of a physical property and fluidity-based identification template according to an embodiment of this application.

[0063] A method for identifying the location of the water front of a high-resistivity stratum according to a first aspect embodiment of this application, the method comprising:

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

[0065] Based on the geoelectric model, multiple wide-area electromagnetic integrated data volumes were obtained for the target layer in the study area at multiple periods before, during and after fracturing. These data included 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 was used to characterize the parameter feature ranges corresponding to different geological feature regions. The geological feature regions included low-resistivity fluid feature regions and interference feature regions. The parameter feature ranges included the porosity change range, resistivity change range, and permeability range.

[0066] Multiple wide-area electromagnetic integrated data volumes are sequentially constrained and inverted in the geoelectric model to obtain multiple wide-area apparent resistivity profiles in a one-to-one correspondence.

[0067] Based on the dynamic characteristics of low-resistivity fluid changes in multiple wide-area apparent resistivity profiles, the initial water intrusion front position of the target layer was determined.

[0068] Based on the target resistivity change data, target porosity change data, target permeability data, and the physical property and fluid joint identification template, the initial water intrusion front position is processed to eliminate interference feature regions, thus obtaining the target water intrusion front position.

[0069] In areas with high exploration and development maturity, geological data is abundant and the stratigraphic structure is relatively well understood. Therefore, this application abandons the massive seismic data, reduces the burden of structural modeling, and reduces the workload of data processing. Only well logging lithology data and well logging resistivity are used to build the stratigraphic structure model. While minimizing the model layering error, the efficiency of model building is greatly improved, providing a feasible method for rapid processing.

[0070] In mature exploration and development areas, there are numerous and densely distributed wells with detailed logging data. By acquiring logging data from key wells in the study area, the stratigraphic structure can be understood. In this embodiment, to intuitively establish a stratigraphic structure model, lithological data and logging resistivity data need to be acquired; simultaneously, to establish a joint identification template for physical properties and fluids, logging porosity data and logging permeability data need to be acquired.

[0071] In this embodiment, the regional tectonic profile of the study area is relatively simple, with the strata being primarily horizontally layered. In areas with more complex structures, tectonic features and stratigraphic development history can be obtained from regional background data.

[0072] Lithological data includes both data that directly reflect lithology and data that indirectly reflect lithology. Data that directly reflects lithology includes core samples and well cuttings, while data that indirectly reflects lithology includes logging data such as gamma ray logging, compensated density logging, spontaneous potential logging, photoelectric effect section index logging, and sonic logging. In this embodiment, data that directly reflects lithology is selected.

[0073] Generally, data that directly reflects lithology primarily comes from well core samples, supplemented by well cuttings. By obtaining core samples from key wells, we can clearly understand the thickness, lithology, oil content, porosity and permeability of each stratum within the study area, the presence of fractures, and the contact relationships between different strata. This information plays a crucial role in establishing well-connected profiles and three-dimensional geological framework models of the work area. For example, when modeling a target layer, it is necessary to know the thickness, depth, and lithology of the target layer in each well to build a more accurate model and understand the distribution of the target layer both vertically and horizontally.

[0074] The aforementioned logging resistivity data, logging porosity data, and logging permeability data are all obtained from logging data, and all three types of data require data from the entire well section.

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

[0076] Each well should ideally cover all five types of logging information, and there should be at least five wells. These five wells should be evenly distributed throughout the work area, and should not be concentrated in a small block or on a line. This is to ensure that the stratigraphic model covers the structural features and stratigraphic development features of the work area as much as possible, so as to reduce the ambiguity of the results.

[0077] Based on well logging resistivity data and lithological data, stratigraphic division is carried out, and a geoelectric model is established. This includes: rapidly dividing the main survey line profiles of the study area according to well logging resistivity data and defining positive and negative phases, and correcting the top and bottom depths of the stratigraphic layers according to lithological data to obtain a more accurate stratigraphic division result and establish a geoelectric model.

[0078] Positive and negative phases are defined based on the logging resistivity throughout the well. A positive phase is defined as the logging resistivity increasing from shallow to deep; a negative phase is defined as the logging resistivity decreasing from shallow to deep. The accuracy of the positive and negative phase division is 0.5m.

[0079] Based on the above definition, let's take an example: If the logging resistivity at an altitude of -4232m is R1 = 50 Ω·m, the logging resistivity at a shallower altitude of -4231.5m is R2 = 48 Ω·m, and the logging resistivity at a deeper altitude of -4232.5m is R3 = 40 Ω·m, where K is a correction factor, typically 1 Ω·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 depth H1 is R1, the logging resistivity at depth H2 is R2, and H2 > H1, the formula for phase calculation can be summarized as follows:

[0086]

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

[0088] Based on the above classification results, lithological correction is needed to the phase stratification results of individual wells to eliminate situations where different strata have similar lithology, leading to minimal phase variation. The specific procedure is as follows: Based on the phase stratification results of individual wells, the stratification results of the lithological data are quickly compared. If the two are identical, the stratification is accurate; if they differ, the phase stratification is corrected according to the lithological data, thus obtaining a unified single-well stratification result. By performing rapid phase calculation and stratigraphic classification on key wells in the study area using the above method, and correcting the results based on lithological data, the stratigraphic framework of the entire study area can be obtained.

[0089] It should be noted that lithological data is obtained through core sampling or well cuttings logging. Depth localization can be achieved using lithological data obtained through well logging techniques (such as lithological scanning logging, which combines density, neutron, photoelectric effect, and energy dispersive spectroscopy data to calculate mineral content and provide detailed mineral composition (e.g., clay, quartz, calcite, dolomite), suitable for complex lithological analysis). Lithological data is displayed in segments based on burial depth or elevation; for example, 1000m-1500m is fine sandstone, 1500m-1700m is mudstone, and 1700m-2000m is limestone. The stratification results of lithological data are intuitive, highly reliable, and serve as a direct basis for stratigraphic division.

[0090] In some embodiments of this application, the multiple wide-area electromagnetic integrated data bodies are respectively a first wide-area electromagnetic integrated data body, a second wide-area electromagnetic integrated data body, and a third wide-area electromagnetic integrated data body. The first wide-area electromagnetic integrated data body, the second wide-area electromagnetic integrated data body, and the third wide-area electromagnetic integrated data body are obtained through 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, and the wide-area electromagnetic wave data of the target layer is collected in a high-density manner to calculate the second wide-area apparent resistivity data. 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 collected in a high-density manner, and the third wide-area apparent resistivity data is calculated. The data volume of the target layer is deducted, and the third wide-area apparent resistivity data is filled into 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 is encrypted and collected to calculate the fourth wide-area apparent resistivity data. The data volume of the target layer is subtracted, 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.

[0094] It is important to note that the terms "pre-fracturing acquisition," "during fracturing," and "post-fracturing" refer to specific time points in the wide-area electromagnetic acquisition. The time points in this case are merely illustrative examples of this technical solution. In practice, smaller time intervals are typically required, such as "pre-fracturing acquisition," "at the very beginning of fracturing," ..., "minute n of fracturing," ..., "minute n+m of fracturing," ..., "just after fracturing," ..., "minute k after fracturing," etc., where n, m, and k are numerical values ​​determined based on the specific circumstances. At each moment, only the target layer is subjected to encrypted acquisition.

[0095] In the embodiments of this application, when acquiring and calculating the first wide-area electromagnetic wave data of each layer in the study area, it should be done before artificial measures such as hydraulic fracturing and injection of low-resistivity fluids are carried out in the study area, that is, the first wide-area electromagnetic wave data is the original stratum information without artificial intervention.

[0096] In some implementations, based on the wide-area electromagnetic method, the apparent resistivity data of a specified area can be obtained. This can be the first wide-area electromagnetic wave data of the specified area collected using a wide-area electromagnetic device. This data is then 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. The above apparent resistivity data ρ α It can be calculated using the following formula:

[0098]

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

[0100] In this embodiment of the application, the top and bottom depths of the target layer are obtained using the stratification results of lithological data. Typically, to improve the accuracy of the results, the top and bottom depths of the target layer need to be extended outward by 5m-15m, that is, the top depth is reduced by 5m-15m and the bottom depth is increased by 5m-15m, to obtain a "variable depth range" to cover more stratigraphic information.

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

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

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

[0104] Based on 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 transmission 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 point encryption is performed 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 variable depth range is obtained by encrypted acquisition of wide-area electromagnetic equipment.

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

[0109] In this embodiment, wide-area electromagnetic spectrum encryption technology can be 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 as the detection depth increases. Therefore, when measuring strata at greater depths, the resolution of electromagnetic methods is often low, thus requiring the introduction of spectrum encryption technology to acquire more wide-area electromagnetic signals from deeper target strata.

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

[0111] The skin depth formula is shown below:

[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, representing the combined resistivity of multiple strata from the surface to a certain depth; and f is the transmitted signal frequency, in Hz.

[0114] Based on the top and bottom depths of the aforementioned variable depth range, and the first wide-area apparent resistivity data, the frequency range of 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 taken as 356, but it can be adjusted according to the actual situation. Generally, the transmission frequency range of wide-area electromagnetic devices can be from 0.0020Hz to 15728.6Hz. The transmission frequency can be adjusted according to the actual situation. Generally, the higher the frequency, the shallower the detection depth; conversely, the higher the frequency, the deeper the detection depth.

[0116] Specifically, information on the top and bottom of each layer is obtained through phase and lithological data, and the top and bottom interface of the target layer is extended outward to a certain extent to obtain the top and bottom interface with "variable depth range".

[0117] The transmission frequency range for spectrum encryption is calculated using the skin depth formula. Spectrum encryption technology is then used to encrypt frequency groups within this range, while corresponding wide-area pseudo-random signal data is collected as 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 filled into the blank portion of the first wide-area apparent resistivity data to form the first wide-area integrated electromagnetic data body.

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

[0119] Table 1. Process of obtaining wide-area electromagnetic integrated data volume

[0120]

[0121] In the embodiments of this application, the data collected subsequently, such as the third wide-area apparent resistivity data and the fourth wide-area apparent resistivity data, are all collected after artificial measures such as injecting low-resistivity fracturing fluid into the high-resistivity target layer. The measurement point location remains unchanged, the transmission frequency for the "variable depth range" detection remains unchanged, and the number of acquisitions and the interval between two acquisitions 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-resistivity 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, the third and fourth wide-area electromagnetic wave data only need to be collected within the "variable depth range," and the data is collected using spectral encryption technology. The newly collected data also needs to undergo operations such as denoising, signal-to-noise ratio enhancement, and Fourier transform to calculate the corresponding third and fourth wide-area apparent resistivity data, which are then filled into the first wide-area apparent resistivity data after deducting the "variable depth range," thereby obtaining the integrated wide-area electromagnetic data body after each collection.

[0124] Then, multiple wide-area electromagnetic integrated data volumes are sequentially constrained and inverted in the geoelectric model to obtain multiple wide-area apparent resistivity profiles.

[0125] The aforementioned constrained inversion employs conventional inversion methods, using an established geological framework model (i.e., a geoelectric model) as a constraint to optimize the model parameter solution process and improve the stability and reliability of the inversion results. In this embodiment, the geological framework model established using well logging resistivity data and lithological data serves as a hard constraint. A regularized constraint inversion method is used, with regularization terms (such as model smoothness and well data matching) limiting the complexity of the inversion results to ensure consistency with well data and minimize the residuals between electromagnetic observation data and the forward response. It should be noted that the specific inversion process is 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 This is a schematic diagram of a wide-area apparent resistivity profile according to an embodiment of this application. Figure 3 This is a comparative schematic diagram of the first wide-area apparent resistivity profile of the same survey line according to an embodiment of this application. Figure 4 This is a comparative schematic diagram of the second wide-area apparent resistivity profile of the same survey line according to an embodiment of this application. Figure 5 This is a comparative schematic diagram of the third wide-area apparent resistivity profile of the same survey line according to an embodiment of this application. Third and fourth wide-area electromagnetic wave data can be collected multiple times at the same survey point, simultaneously acquiring the latest well logging porosity data. By comparing the identification results of the fluids from multiple profiles, the dynamic change characteristics of low-resistivity fluids in high-resistivity formations can be obtained, accurately determining the direction of advance and current position of low-resistivity fluids in the high-resistivity target layer, i.e., the initial water invasion front position of the target layer.

[0127] refer to Figures 3 to 5 Regarding the leading edge position of low-resistivity fluid within the "variable depth range", it can be determined based on the advance of low-resistivity fluid in each wide-area apparent resistivity profile of each survey line.

[0128] In some embodiments of this application, the target resistivity change data is obtained through the following steps:

[0129] Multiple wide-area electromagnetic integrated data volumes are sequentially constrained and inverted in the geoelectric model to obtain multiple wide-area apparent resistivity profiles in a one-to-one correspondence.

[0130] Extract the difference between the wide-area apparent resistivity data of the "variable depth range" in the two most 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" from 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 change data to the initial wide-area apparent resistivity data is determined as the target resistivity change data for the "variable depth range".

[0133] In some embodiments of this application, the target porosity change data is obtained through the following steps:

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

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

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

[0137] In some embodiments of this application, the target penetration rate data is obtained through the following steps:

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

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

[0140] It is important to note that the porosity and permeability values ​​obtained from each well are continuously distributed vertically, with an interval of 0.125m between adjacent sets of data. This means that each measurement of porosity and permeability includes numerous data points. In some embodiments, porosity and permeability are typically measured a maximum of two times. If cost is not a concern, multiple measurements can be performed to obtain multiple sets of data.

[0141] In some embodiments of this application, identifying low-resistivity fluids in high-resistivity formations solely through wide-area apparent resistivity can be subject to interference from reservoir properties, reservoir lithology, and reservoir fluids. Under tight conditions, 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 the presence of high-resistivity oil and gas fluids in the reservoir can also cause anomalies in wide-area apparent resistivity. Therefore, these interference factors need to be eliminated when determining the location of low-resistivity fluids in high-resistivity reservoirs.

[0142] This application provides an identification template that combines physical properties and fluid dynamics. This template allows for the analysis of the causes of resistivity anomalies in the target reservoir, eliminating the possibility of reservoir physical properties interfering with resistivity. Furthermore, this template can further clarify differences in reservoir physical properties, enabling more accurate identification of the distribution of high-resistivity oil and gas flows, as well as the leading edge location of low-resistivity fluids, in reservoirs with different physical properties.

[0143] The following section will explain in detail the principles and process of establishing the joint identification template of physical properties and fluids.

[0144] First, we introduce the standard boundary equations:

[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, 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 the fracturing. 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 is used to reflect the change in reservoir space. The first logging porosity data is for 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 taken as 0.4 in this case; K is the logging permeability data for the current "variable depth range" after fracturing, which is the most recently acquired logging permeability. ref For reference penetration rate, 50mD is used in this case.

[0147] A correlation model is constructed between logging porosity and logging permeability, and the corresponding transformation function is improved based on the Kozeny-Carman equation:

[0148]

[0149] Where K is the logging permeability, used to reflect and control fluid transport efficiency; C is the rock structure coefficient, which is taken as 0.05-0.1 for carbonate rocks, and 0.05 in this case. For logging porosity; S wiWater saturation can be determined through laboratory tests or nuclear magnetic resonance logging.

[0150] Formula (9) can be simplified in the following way:

[0151]

[0152] Meanwhile, the well logging permeability was normalized to avoid the influence of different dimensions and data differences on the experimental results.

[0153]

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

[0155] In this embodiment, the effect of permeability K on fluid intrusion needs to be considered, as the magnitude of permeability directly affects fluid replacement efficiency. In the high-permeability zone (K>100mD): fluid intrusion is rapid, and resistivity response is significant; in the low-permeability zone (K<10mD): intrusion is limited, and resistivity change is delayed or weak.

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

[0157] Acquire 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] Based on 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 parameter characteristic ranges corresponding to different geological characteristic regions are determined. The geological characteristic regions include low-resistivity fluid characteristic regions and interference characteristic regions. The parameter characteristic ranges include porosity change range, resistivity change range, and permeability range.

[0159] Based on the parameter feature range corresponding to different geological feature regions, a physical property and fluid joint identification template is obtained.

[0160] In some embodiments of this application, reference is made to Figure 6 The low-resistivity fluid characteristic region includes the flooded area and the transition zone; the disturbance characteristic region includes the abnormally high pressure region and the dense shielding region; the geological characteristic region also includes the original stratum region; 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] Based on multiple resistivity change data and their corresponding preset first weights, multiple porosity change data and their corresponding preset second weights, and multiple permeability data and their corresponding preset third weights, the parameter characteristic ranges corresponding to different geological feature regions are determined, including:

[0162] Based on multiple resistivity change data and their corresponding preset first weights, multiple porosity change data and their corresponding preset second weights, and multiple permeability data and their corresponding preset third weights, the flooded area and its corresponding first characteristic range, the transition zone and its corresponding second characteristic range, the undisturbed stratum area and its corresponding third characteristic range, the abnormal high pressure area and its corresponding fourth characteristic range, and the dense shielding area and its corresponding fifth characteristic range are determined.

[0163] In some embodiments, the resistivity change data is Porosity change data is Penetration data is log 10 (K).

[0164] The resistivity change data indicates the conductivity of the fluid within the "variable depth range", with a preset first weight of 0.8; the porosity change data reflects the change in storage space, with a preset second weight of 0.6; and the permeability data controls the fluid transport efficiency, with a preset third weight of 0.5.

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

[0166] In this embodiment of the application, a Python program is used for machine learning. A Random Forest model is selected and a training set (i.e., multiple resistivity change data, multiple porosity change data, and multiple permeability data) is provided. The combination of multiple parameters is classified and optimized. The weight allocation of different parameters affects the division of parameter feature ranges, thereby obtaining the parameter feature ranges corresponding to different geological feature regions.

[0167] The flooded area represents the intrusion zone of low-resistivity fluid, the transition zone represents the leading edge of the low-resistivity fluid, and the undisturbed strata represent the area unaffected by the low-resistivity fluid.

[0168] The range of parameter characteristics reflects the features of a geological region, such as a flooded area. Significantly negative values ​​(-0.8 to -0.3) reflect the intrusion of low-resistivity fluids. 10 A relatively high K (2.8–3.5) corresponds to rapid fluid transport in high-permeability regions. Densely shielded areas... Reflecting the dominance of high-resistivity lithology, log 10 (K)<0.5, corresponding to extremely low permeability, eliminating fluid interference.

[0169] The interference mechanism between abnormally high voltage areas and densely shielded areas is as follows:

[0170] (1) Disturbance manifestations of abnormal high pressure zones: Development of fissure-cavity systems leads to (Abnormally low resistivity) can easily be misjudged as intrusion of low-resistivity fluid.

[0171] Elimination method: Combine (drastic changes in porosity) and log 10 (K)>3.5 (superpermeability) is identified as a high-pressure zone dominated by fractures, where fluid does not enter along the pores.

[0172] (2) Interference behavior in densely obscured areas: (High resistance) may be misjudged as an oil and gas enrichment area.

[0173] Exclusion method: log 10 (K) < 0.5 (extremely low permeability) indicates a tight reservoir. (Decrease in porosity) reflects cementation and eliminates fluid movement.

[0174] By using multi-stage wide-area apparent resistivity profiles after fracturing operations, the inrush path of low-resistivity fluid was traced and compared with the template classification results for verification.

[0175] The combined physical property and fluid identification template classifies resistivity anomaly areas into water-flooded areas (low-resistivity fluids), transition zones (front edges), undisturbed strata (unaffected), and interference feature areas (abnormally high-pressure areas and densely blocked areas) by using multi-parameter combination classification and interference area feature thresholds. This eliminates lithological and physical property interference and enables accurate identification of low-resistivity fluids in high-resistivity strata.

[0176] Table 2 shows the feature regions of the combined physical property and fluid identification template:

[0177] Table 2. Feature Regions of the Combined Physical Property and Fluid Identification Template

[0178]

[0179] It should be noted that the standard boundary equation serves to comprehensively assess the impact of fluid intrusion in the reservoir through multi-parameter dynamic evaluation. Although the variables in the equation are all known measured values, the equation functions in the following ways:

[0180] (1) Quantifying changes in fluid conductivity: through It directly reflects the dynamic changes in reservoir resistivity caused by fluid intrusion.

[0181] (2) Correlation between storage space and fluid transport: Introduction Using the absolute value of porosity change and K (permeability) as correction factors, reservoir properties (porosity, permeability) are correlated with fluid transport efficiency (controlled by permeability), avoiding misjudgments caused by relying solely on resistivity anomalies.

[0182] (3) Eliminating interfering factors: using the permeability influence coefficient β and the reference permeability K ref This corrects for the differences in fluid intrusion rates under different permeability conditions (e.g., significant resistivity response in high-permeability zones when K>100mD, and delayed response in low-permeability zones when K<10mD), thereby eliminating the interference of reservoir property differences on resistivity anomalies.

[0183] (4) Weight allocation optimization judgment: In the equation The weighting coefficient of K reflects the contribution of different parameters to fluid identification, and finally outputs a comprehensive index to determine the position of the leading edge of low-resistivity fluid.

[0184] The standard boundary equation does not solve for unknowns, but rather achieves quantitative analysis of reservoir fluid intrusion characteristics through dynamic combination and weight allocation of known parameters, thereby improving the accuracy of low-resistivity fluid identification.

[0185] There is a logical progression between the standard boundary equations and the physical property and fluid simultaneous identification template, as detailed below:

[0186] (1) The standard boundary equations provide a basis for the dynamic analysis of parameters for the identification template of physical properties and fluids.

[0187] The role of standard boundary equations: By introducing K is used to construct a multi-parameter joint equation to quantify the dynamic impact of fluid intrusion on reservoir resistivity and physical properties. The core of this approach is to comprehensively determine the location of the low-resistivity fluid front through weight allocation.

[0188] Input parameters for the physical property and fluid dynamics identification template: three key parameters in the standard boundary equation. It can be directly used as the input feature for the joint identification template of physical properties and fluidity.

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

[0190] The correction logic for the standard boundary equation: through the permeability influence coefficient β and the reference permeability K ref This corrects for differences in fluid intrusion rates under different permeability conditions (e.g., significant resistivity response in high-permeability zones when K>100mD, and delayed response in low-permeability zones when K<10mD), thereby eliminating the interference of physical property differences on resistivity anomalies.

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

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

[0193] Quantitative output of standard boundary equations: The standard boundary equations output a comprehensive index through multi-parameter weighting, which is used to preliminarily determine the position of the fluid leading edge.

[0194] Extension of the physical property and fluid-based identification template: By using a random forest model to perform nonlinear classification of the parameter combinations of the standard boundary equation, the comprehensive index is extended to multi-region classification (such as flooded areas, transition zones, and undisturbed stratigraphic areas).

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

[0196] The standard boundary equation is used to dynamically correct the correlation between physical property parameters and fluid intrusion, and to solve the problem of misjudgment of single resistivity anomalies (such as eliminating the high resistivity interference of tight reservoirs in high resistivity formations).

[0197] Localization 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 region division in high-dimensional feature space, thereby improving classification accuracy.

[0198] In summary, the standard boundary equations form the physical basis of the combined property and fluid identification template, responsible for dynamically correlating physical parameters with fluid responses. The combined property and fluid identification template, on the other hand, is the engineering application of the standard boundary equations, using machine learning to achieve multi-region classification under complex geological conditions. Together, they address the technical challenge of identifying low-resistivity fluid fronts in high-resistivity formations.

[0199] According to the water front location identification method of high resistivity strata in this application, this application abandons the huge amount of seismic data and only uses well logging resistivity data and lithological data to build the stratigraphic framework model, which reduces the burden of structural modeling, reduces the amount of data processing, and greatly improves the model building efficiency while minimizing the model layering error. By analyzing the dynamic changes of low-resistivity fluids through multiple wide-area apparent resistivity profiles, the initial water front position of the target layer can be determined. However, in high-resistivity formations, identifying low-resistivity fluids solely through wide-area apparent resistivity can be hampered by factors such as reservoir properties, reservoir lithology, and reservoir fluids. Under tight conditions, reservoir properties can cause an abnormal increase in wide-area apparent resistivity; differences in reservoir lithology can also cause anomalies in wide-area apparent resistivity; and the presence of high-resistivity oil and gas fluids in the reservoir can also cause anomalies in wide-area apparent resistivity. By identifying the parameter characteristic ranges corresponding to different geological feature regions marked by the joint identification template of properties and fluids, the corresponding low-resistivity fluid feature regions can be determined based on the target resistivity change data, target porosity change data, and target permeability data, eliminating interference from other feature regions and thus achieving accurate identification of low-resistivity fluids in high-resistivity reservoirs.

[0200] According to a second aspect embodiment of the present application, a water front location identification system for high resistivity strata includes a geoelectric model establishment unit, a data acquisition unit, a wide-area apparent resistivity profile determination unit, an initial water front location determination unit, and a target water front location determination unit.

[0201] The geoelectric model building unit is used to acquire well logging resistivity data and lithological data of the study area, divide the stratigraphy based on the well logging resistivity data and lithological data, and build the geoelectric model.

[0202] The data acquisition unit is used to acquire multiple wide-area electromagnetic integrated data volumes corresponding to multiple periods before, during and after fracturing of the target layer in the study area based on the geoelectric model. These data include target resistivity change data, target porosity change data and target permeability data of 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 feature range corresponding to different geological feature regions. The geological feature regions include low-resistivity fluid feature regions and interference feature regions. The parameter feature ranges include the porosity change range, resistivity change range and permeability range.

[0203] The 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 a one-to-one correspondence.

[0204] The initial water intrusion front location determination unit is used to determine the initial water intrusion front location of the target layer based on the low-resistivity fluid dynamic change characteristics of multiple wide-area apparent resistivity profiles.

[0205] The target water intrusion front location determination unit is used to perform interference feature region elimination processing on the initial water intrusion front location based on the target resistivity change data, target porosity change data, target permeability data, and physical property and fluid joint identification template, so as to obtain the target water intrusion front location.

[0206] According to the water front location identification system for high resistivity strata in this application, this application abandons the massive amount of seismic data and only uses well logging resistivity data and lithological data to build the stratigraphic framework model, which reduces the burden of structural modeling, reduces the amount of data processing, and greatly improves the model building efficiency while minimizing model layering errors. By analyzing the dynamic changes of low-resistivity fluids through multiple wide-area apparent resistivity profiles, the initial water front position of the target layer can be determined. However, in high-resistivity formations, identifying low-resistivity fluids solely through wide-area apparent resistivity can be hampered by factors such as reservoir properties, reservoir lithology, and reservoir fluids. Under tight conditions, reservoir properties can cause an abnormal increase in wide-area apparent resistivity; differences in reservoir lithology can also cause anomalies in wide-area apparent resistivity; and the presence of high-resistivity oil and gas fluids in the reservoir can also cause anomalies in wide-area apparent resistivity. By identifying the parameter characteristic ranges corresponding to different geological feature regions marked by the joint identification template of properties and fluids, the corresponding low-resistivity fluid feature regions can be determined based on the target resistivity change data, target porosity change data, and target permeability data, eliminating interference from other feature regions and thus achieving accurate identification of low-resistivity fluids in high-resistivity reservoirs.

[0207] Since the water front location identification system for high resistivity strata adopts all the technical solutions of the water front location identification method for high resistivity strata described in the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, and will not be elaborated here.

[0208] Additionally, one embodiment of this application provides a control device comprising: 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.

[0209] 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.

[0210] The non-transient software program and instructions required to implement the water front location identification method for high resistivity strata in the above embodiments are stored in the memory. When executed by the processor, the water front location identification method for high resistivity strata in the above embodiments is executed.

[0211] 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.

[0212] Furthermore, one embodiment of this application provides a computer-readable storage medium storing computer-executable instructions that are executed by a processor or controller, such as the processor in the above embodiment, to enable the processor to perform the water front location identification method for high-resistivity formations in the above embodiment.

[0213] 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.

[0214] The embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application 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 this application.

Claims

1. A method for identifying the location of the water front in a high-resistivity stratum, characterized in that, The method includes: Acquire well logging resistivity data and lithological data of the study area, perform stratigraphic division based on the well logging resistivity data and the lithological data, and establish a geoelectric model; Based on the geoelectric model, multiple wide-area electromagnetic integrated data volumes corresponding to the target layer in the study area before, during and after fracturing are obtained. These data include target resistivity change data, target porosity change data and target permeability data of 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 feature range corresponding to different geological feature regions. The geological feature regions include low-resistivity fluid feature regions and interference feature regions. The parameter feature range includes the porosity change range, resistivity change range and permeability range. Multiple wide-area electromagnetic integrated data volumes are sequentially constrained in the geoelectric model to obtain multiple wide-area apparent resistivity profiles in a one-to-one correspondence. Based on the low-resistivity fluid dynamic change characteristics of multiple wide-area apparent resistivity profiles, the initial water intrusion front position of the target layer is determined. 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, the initial water intrusion front position is processed to eliminate interference feature regions, thereby obtaining the target water intrusion front position.

2. The method for identifying the location of the water front of a high-resistivity stratum according to claim 1, characterized in that, The target resistivity change data is obtained through the following steps: Multiple wide-area electromagnetic integrated data volumes are sequentially constrained in the geoelectric model to obtain multiple wide-area apparent resistivity profiles in a one-to-one correspondence. Extract the difference between the wide-area apparent resistivity data of the two most recent wide-area apparent resistivity profiles of the target layer, and record it as the initial resistivity change data. Extract the wide-area apparent resistivity data of the wide-area apparent resistivity profile corresponding to the target layer before fracturing, and record it as the 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 location of the water front of a high-resistivity stratum according to claim 1, characterized in that, The target porosity change data is obtained through the following steps: Obtain the first logging porosity data before fracturing the target formation and the current second logging porosity data after fracturing; Calculate the difference between the first logging porosity data and the second 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 location of the water front of a high-resistivity stratum according to claim 1, characterized in that, The target penetration rate data is obtained through the following steps: Obtain the current well logging permeability data after fracturing the target layer; The logarithm of the well logging permeability data is used to determine the target permeability data.

5. The method for identifying the location of the water front of a high-resistivity stratum according to claim 1, characterized in that, The physical property and fluid-based joint identification template is obtained through the following steps: Acquire multiple resistivity change data, multiple porosity change data, and multiple permeability data of the target layer in the study area before and after fracturing; Based on 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, parameter characteristic ranges corresponding to different geological feature regions are determined. The geological feature regions include low-resistivity fluid feature regions and interference feature regions. The parameter characteristic ranges include porosity change range, resistivity change range, and permeability range. The physical property and fluid joint identification template is obtained based on the parameter feature range corresponding to different geological feature regions.

6. The method for identifying the location of the water front of a high-resistivity stratum according to claim 1 or 5, characterized in that, The low-resistivity fluid characteristic region includes a flooded area and a transition zone; the interference characteristic region includes an abnormally high pressure area and a dense shielding area; the geological characteristic region also includes a undisturbed stratum 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. The step of determining the parameter characteristic range corresponding to different geological feature regions based on 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 includes: Based on 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 corresponding first characteristic range, the transition zone and corresponding second characteristic range, the undisturbed stratum area and corresponding third characteristic range, the abnormal high pressure area and corresponding fourth characteristic range, and the dense shielding area and corresponding fifth characteristic range are determined.

7. The method for identifying the location of the water front of a high-resistivity stratum 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. 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 through the following steps: 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, and the wide-area electromagnetic wave data of the target layer is collected in a encrypted manner to calculate the second wide-area apparent resistivity data. 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, the wide-area electromagnetic wave data of the target layer is collected in encrypted form, and the third wide-area apparent resistivity data is calculated. The data volume of the target layer is deducted, and the third wide-area apparent resistivity data is filled into 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 is encrypted and collected to calculate 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 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 location of the water front in a high-resistivity stratum, characterized in that, The system includes: The geoelectric model establishment unit is used to acquire well logging resistivity data and lithological data of the study area, perform stratigraphic division based on the well logging resistivity data and the lithological data, and establish a geoelectric model. The data acquisition unit is used to acquire, based on the geoelectric model, multiple wide-area electromagnetic integrated data volumes corresponding to multiple periods before, during and after fracturing of the target layer in the study area, including target resistivity change data, target porosity change data and target permeability data of 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 feature range corresponding to different geological feature regions. The geological feature regions include low-resistivity fluid feature regions and interference feature regions. The parameter feature range includes the porosity change range, resistivity change range and permeability range. The 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 a one-to-one correspondence. The initial water intrusion front position determination unit is used to determine the initial water intrusion front position of the target layer based on the low-resistivity fluid dynamic change characteristics of multiple wide-area apparent resistivity profiles. The target water intrusion front location determination unit is used to perform interference feature region elimination processing on the initial water intrusion front location 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, so as to obtain the target water intrusion front location.

9. A control device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for identifying the location of the water front of a high-resistivity stratum as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are used to execute the water front location identification method for high-resistivity strata as described in any one of claims 1 to 7.

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