Low-resistivity oil layer identification method and system based on multi-factor logging information integration

Abstract

The invention discloses a low-resistivity oil layer identification method and system based on multi-factor logging information integration, relates to the technical field of oil and gas field development, and solves the problem that the traditional low-resistivity oil layer interpretation does not comprehensively consider various information of a logging curve, resulting in contradictions in identification results. Analyzing the relationship among the four properties of the reservoir based on research area data; based on the relationship among the four properties of the reservoir, determining the cause of formation of the low-resistivity oil reservoir in the research area in combination with the logging data, the regional geological data, the core analysis data and the oil reservoir condition of the research area; based on the cause of formation of the low-resistivity oil layer in the research area, selecting an apparent resistance increase rate, a nuclear magnetic resonance measurement irreducible water saturation T2 cutoff value and a natural potential amplitude ratio as research parameters, and based on weighted fusion of the three research parameters, obtaining a comprehensive low-resistivity oil layer identification result; and low-resistivity oil layer identification is carried out based on multi-factor calculation comprehensive scores, so that the identification accuracy of the low-resistivity oil layer is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field development, and more specifically, to a method and system for identifying low-resistance oil layers based on the integration of multi-factor logging information. Background Art

[0002] Low-resistivity oil layers are one of the most promising targets for increasing reserves, and it is of great significance to identify the composition of low-resistivity oil layers. Usually, people refer to reservoirs with low resistivity, such as low-contrast oil layers, or oil layers with a resistivity ratio of oil layer to water layer less than 2 or 3 in the same oil-water system as low-resistivity oil layers (referred to as low-resistivity oil layers). Resistivity can be used to determine whether a reservoir contains oil and gas. Regardless of whether the absolute value of the resistivity of an oil layer is high or low, if its resistivity increase rate is low, it will make it difficult to identify whether the layer is an oil layer.

[0003] In the AG Basin in the Western Desert of Egypt, the Cretaceous Bahariya oil and gas layer is in a tidal flat sedimentary environment, and the oil reservoirs are developed into multiple sets of small layers in the vertical direction. However, with the continuous deepening of exploration and development, affected by factors such as reservoir characteristics and formation water salinity, the resistivity of the reservoirs is generally low, and the oil reservoirs are small in scale and dispersed in distribution, resulting in a lack of regularity in the distribution of oil reservoirs. In recent years, a number of low-resistance oil wells have been discovered, some of which have particularly low resistivity. In the reservoirs that have been tested, the oil-water relationship is complex, and the electrical and physical properties of each small layer are significantly different, which increases the difficulty of oil-water identification and leads to large differences between the test results and the logging interpretation conclusions. Therefore, finding an effective low-resistance oil layer identification method suitable for the region to provide potential layers for development and evaluation has become an important research topic for tapping the potential of shallow oil and gas reservoirs in the AG Basin.

[0004] The properties of low-resistivity oil layers are different from those of general oil and gas layers, and traditional logging technology is difficult to accurately and intuitively detect such oil and gas layers. Compared with high-resistivity oil layers, the latter have a more complete analysis model. The characteristics of low-resistivity oil and gas layers are complex and are affected by a variety of internal and external factors such as lithology, physical properties, oil content, water content, wettability, etc. Its resistance increase coefficient is sometimes even smaller than that of water layers, and it is easy to be confused with water layers in logging curve analysis. In terms of low-resistivity oil layer identification methods, although there are methods such as logging plate method, these methods often require high-cost special logging equipment and complex plates. Another method is to identify by sampling and analyzing test data, but in old oil field areas, the data that can be obtained is limited.

[0005] Low-resistivity oil layers are crucial in tapping the potential of old oil fields and are the key to increasing reserves and production in the later stage. Traditional interpretation of low-resistivity oil layers often relies on a single logging curve or comparison template for identification, and fails to comprehensively consider the various information of the logging curve, resulting in low identification accuracy.

[0006] Therefore, the present application provides a method and system for identifying low-resistivity oil layers based on the integration of multi-factor logging information to solve the above problems. Summary of the invention

[0007] The purpose of this application is to provide a method and system for identifying low-resistivity oil layers based on the integration of multi-factor logging information, so as to solve the problem that traditional low-resistivity oil layer interpretation often relies on a single logging curve or comparison template for identification, fails to comprehensively consider the various information of the logging curve, and leads to contradictions in the identification results; this application starts from the information value of the logging curve, comprehensively considers the interpretation results of the logging curve, and identifies the low-resistivity oil layer based on the comprehensive score calculated by multiple factors, so as to improve the accuracy of identifying the low-resistivity oil layer.

[0008] The present application first provides a method for identifying low-resistivity oil layers based on the integration of multi-factor logging information, including: obtaining data of a study area, including: logging data, regional geological data and core analysis data, and analyzing the relationship between reservoir lithology, physical properties, electrical properties and oil content based on the data of the study area; based on the relationship between reservoir lithology, physical properties, electrical properties and oil and gas content, combining logging data, regional geological data and core analysis data and oil reservoir conditions in the study area, determining the cause of the low-resistivity oil layer in the study area; based on the cause of the low-resistivity oil layer in the study area, selecting the apparent resistivity increase rate, the T2 cutoff value of bound water saturation measured by nuclear magnetic resonance, and the natural potential amplitude ratio as research parameters, and obtaining a comprehensive low-resistivity oil layer identification result based on the weighted fusion of the three research parameters.

[0009] By adopting the above technical scheme, the weighted fusion of three research parameters, namely, apparent resistivity increase rate, nuclear magnetic resonance measurement irreducible water saturation T2 cutoff value, and natural potential amplitude ratio, is used to identify low-resistivity oil layers, so as to fully tap the resistivity logging information, realize the complementary advantages of multiple parameters, and improve the accuracy of low-resistivity oil layer identification; the apparent resistivity increase rate and the natural potential amplitude ratio can be directly obtained from the logging curve, and the irreducible water saturation can be directly obtained based on nuclear magnetic resonance measurement, and the identification data is easy to obtain, which has broad prospects and value for promotion and application.

[0010] In one possible implementation, based on the relationship between reservoir lithology, physical properties, electrical properties and oil and gas content, combined with logging data, regional geological data and core analysis data and oil reservoir conditions in the study area, the cause of the low resistivity oil layer in the study area is determined; wherein the cause of the low resistivity oil layer in the study area includes: formation water mineralization, conductivity and wetting effect of clay minerals, pore content, bound water saturation and mineral type.

[0011] In a possible implementation, based on the genesis of the low-resistivity oil layer in the study area, the apparent resistivity increase rate, the nuclear magnetic resonance measurement of bound water saturation T2 cutoff value, and the natural potential amplitude ratio are selected as research parameters, and a comprehensive low-resistivity oil layer identification result is obtained based on the weighted fusion of the three research parameters; including: calculating the three research parameters of the study area, including: the apparent resistivity increase rate, the nuclear magnetic resonance measurement of bound water saturation T2 cutoff value, and the natural potential amplitude ratio; selecting the corresponding BP neural network model based on the genesis of the low-resistivity oil layer in the study area, bringing the three research parameters of the study area into the BP neural network model, obtaining the correlation index between the three research parameters and the low-resistivity oil layer, and determining the weight according to the correlation index; determining the scores of the three research parameters based on the values ​​of the three research parameters in the study area; solving the comprehensive score based on the weights and scores of the three research parameters, and determining whether the reservoir in the study area belongs to an oil layer, a low-resistivity oil layer, or a water layer based on the range of the comprehensive score.

[0012] In a possible implementation manner, the apparent resistance increase rate is obtained by the following formula:

[0013] RI=(Rt-Rw) / Rw

[0014] Where: RI is the apparent resistivity increase rate; Rt is the formation resistivity, unit is Ω·m; Rw is the resistivity of the typical water layer, unit is Ω·m.

[0015] In a possible implementation, the irreducible water saturation is calculated using the following formula:

[0016]

[0017] Where: S WIRR (T 2i ) is the capillary bound water saturation of rock; τ 2i is the i-th transverse relaxation time component in the T2 spectrum; T 2CIRR is the transverse relaxation time corresponding to the capillary force in the bound water state.

[0018] In a possible implementation, the natural potential amplitude ratio is obtained by the following formula:

[0019] Spi=(SP'–SPw) / SPw

[0020] Where: Spi is the natural potential amplitude ratio, SP' is the natural potential of the low-resistance oil layer, and SPw is the natural potential of the water layer.

[0021] In one possible implementation, a corresponding BP neural network model is selected based on the genesis of the low-resistivity oil layer in the study area, and the three research parameters of the study area are brought into the BP neural network model to obtain correlation indexes between the three research parameters and the low-resistivity oil layer as weights; wherein the BP neural network model is trained using data on low-resistivity oil layers of the same genesis.

[0022] In a possible implementation, the scores of the three research parameters are determined based on the values ​​of the three research parameters of the research area; including: determining the scores of the three research parameters according to the intervals into which the values ​​of the three research parameters fall.

[0023] In a possible implementation, a comprehensive score is solved based on the weights and scores of the three research parameters, and it is determined whether the reservoir in the study area belongs to an oil layer, a low-resistivity oil layer or a water layer based on the range of the comprehensive score; wherein the comprehensive score is obtained by the following formula:

[0024] Index=wr×score(RI)+wi×score(S wirr )+ws×score(Spi)

[0025] Where: Index is the comprehensive score; wr, wi, and ws are the apparent resistance increase rate RI, the bound water saturation T2 cutoff value S measured by nuclear magnetic resonance, respectively. wirr , the weight of the natural potential amplitude ratio Spi, wr+wi+ws=1.

[0026] The present application also provides a low-resistivity oil layer identification system based on multi-factor logging information integration, which is used to execute the low-resistivity oil layer identification method based on multi-factor logging information integration as mentioned above, and the system includes: a four-property analysis module, which is used to obtain the data of the study area, including: logging data, regional geological data and core analysis data, and analyze the relationship between the reservoir lithology, physical properties, electrical properties and oil content based on the data of the study area; a cause analysis module, which is used to determine the cause of the low-resistivity oil layer in the study area based on the relationship between the reservoir lithology, physical properties, electrical properties and oil and gas content, combined with logging data, regional geological data and core analysis data and the oil reservoir conditions in the study area; a comprehensive identification module, which is used to select the apparent resistivity increase rate, the nuclear magnetic resonance measurement bound water saturation T2 cutoff value, and the natural potential amplitude ratio as research parameters based on the cause of the low-resistivity oil layer in the study area, and obtain a comprehensive low-resistivity oil layer identification result based on the weighted fusion of the three research parameters.

[0027] Compared with the prior art, the present application has the following beneficial effects: fully exploiting resistivity logging information, taking into account the influence of oil and gas on resistivity, natural potential, and irreducible water saturation, avoiding the problem that the conventional resistivity method is difficult to explain the low-resistivity oil layer, achieving complementary advantages, and making the low-resistivity oil layer identification result more accurate; the apparent resistivity increase rate and the natural potential amplitude ratio can be directly obtained from the logging curve, and the irreducible water saturation can be directly obtained based on nuclear magnetic resonance measurement, and the identification data is easy to obtain, which has broad prospects for promotion and application and value. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0029] Figure 1 It is a flowchart of the low-resistivity oil layer identification method based on the integration of multi-factor logging information;

[0030] Figure 2 It is a schematic diagram of the relationship between reservoir lithology and physical properties in the study area;

[0031] Figure 3 This is a schematic diagram of the relationship between reservoir physical properties and oil content in the study area;

[0032] Figure 4 This is a schematic diagram of the relationship between electrical properties and oil content in the study area;

[0033] Figure 5 It is a structural diagram of the BP neural network model;

[0034] Figure 6 This is a schematic diagram of the well logging interpretation conclusion for a reservoir;

[0035] Figure 7 It is a structural diagram of the low-resistivity oil layer identification system based on the integration of multi-factor logging information. DETAILED DESCRIPTION

[0036] Hereinafter, the term "include" or "may include" used in various embodiments of the present application indicates the presence of the function, operation or element applied for, and does not limit the increase of one or more functions, operations or elements. In addition, as used in various embodiments of the present application, the terms "include", "have" and their cognates are intended only to indicate specific features, numbers, steps, operations, elements, components or a combination of the foregoing items, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components or a combination of the foregoing items or the possibility of increasing one or more features, numbers, steps, operations, elements, components or a combination of the foregoing items.

[0037] In various embodiments of the present application, the expression "or" or "at least one of B or / and C" includes any combination or all combinations of the words listed at the same time. For example, the expression "B or C" or "at least one of B or / and C" may include B, may include C, or may include both B and C.

[0038] The terms used in the various embodiments of the application are only used to describe the purpose of specific embodiments and are not intended to limit the various embodiments of the application. As used herein, the singular form is intended to also include the plural form, unless the context clearly indicates otherwise. Unless otherwise limited, all terms used here (including technical terms and scientific terms) have the same meaning as the meanings commonly understood by ordinary technicians in the field of the various embodiments of the application. Terms (such as terms defined in generally used dictionaries) will be interpreted as having the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having idealized meanings or too formal meanings, unless clearly defined in the various embodiments of the application.

[0039] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with examples and drawings. The illustrative implementation scheme of the present application and its description are only used to explain the present application and are not intended to limit the present application.

[0040] See also Figure 1 As shown, Figure 1 The flowchart of the low-resistivity oil layer identification method based on multi-factor logging information integration is as follows: S1. Obtaining data of the study area, including logging data, regional geological data and core analysis data, and analyzing the relationship between reservoir lithology, physical properties, electrical properties and oil content based on the data of the study area; S2. Based on the relationship between reservoir lithology, physical properties, electrical properties and oil and gas content, combined with logging data, regional geological data and core analysis data and the oil reservoir conditions in the study area, determine the cause of the low-resistivity oil layer in the study area; S3. Based on the cause of the low-resistivity oil layer in the study area, select the apparent resistivity increase rate, the T2 cutoff value of bound water saturation measured by nuclear magnetic resonance, and the natural potential amplitude ratio as research parameters, and obtain the comprehensive low-resistivity oil layer identification result based on the weighted fusion of the three research parameters.

[0041] Specifically, the relationship between reservoir lithology, physical properties, electrical properties and oil content is analyzed with the help of logging data, and the corresponding change rules between the "four properties" are found, which paves the way for the subsequent analysis of the causes of low-resistivity oil layers and the identification of low-resistivity oil layers; through the comprehensive analysis of the relationship between the reservoir characteristics and the "four properties" of the target layer in the study area, combined with the data of logging, well logging and oil testing, the causes of low-resistivity oil layers in the study area are found; based on the previous rock physics experimental data, the geological, logging, analytical and test data are used in combination to identify low-resistivity oil layers in the study area. Specifically, the weighted fusion of the three research parameters of apparent resistivity increase rate, nuclear magnetic resonance measurement bound water saturation T2 cutoff value, and natural potential amplitude ratio is used to identify low-resistivity oil layers.

[0042] The improvement of the present application is that the weighted fusion of three research parameters, namely, the apparent resistivity increase rate, the T2 cutoff value of bound water saturation measured by nuclear magnetic resonance, and the natural potential amplitude ratio, is used to identify low-resistivity oil layers, fully tap the resistivity logging information, realize the complementary advantages of multiple parameters, and improve the accuracy of low-resistivity oil layer identification; the apparent resistivity increase rate and the natural potential amplitude ratio can be directly obtained from the logging curve, and the bound water saturation can be directly obtained based on nuclear magnetic resonance measurement, and the identification data is easy to obtain, which has broad prospects for promotion and application and value.

[0043] Step S1 is to obtain the data of the study area, including: well logging data, regional geological data and core analysis data, and analyze the relationship between reservoir lithology, physical properties, electrical properties and oil content based on the data of the study area.

[0044] The "four-property relationship" refers to the relationship between lithology, physical properties, electrical properties and oil content, and is the basis for logging interpretation of oil and gas layers. Research and analysis have found that lithology plays a dominant role in the "four-property relationship", and electrical properties are a comprehensive reflection of lithology, physical properties and oil content; among them, lithology characteristics play a decisive role in electrical characteristics, while other characteristics, such as physical properties and oil content characteristics, also affect electrical characteristics to a certain extent. In practical applications, the oil content of the sand body can be comprehensively determined through the well logging data, regional geological data and core analysis data of the study area, as well as the relationship between the lithology, physical properties, electrical properties and oil content of the reservoir in the study area, to judge whether the reservoir contains oil, and to help evaluate the potential and development value of the reservoir.

[0045] Specifically, the single well logging curves of the study area and the influencing factors data of reservoir oil content and physical properties, lithology, and electrical properties are collected; the influencing factors data include reservoir lithology, permeability, porosity, remaining oil saturation, test conclusions, etc. Figure 2-4 As shown, Figure 2 Schematic diagram of the relationship between reservoir lithology and physical properties in the study area. Figure 3 This is a schematic diagram of the relationship between reservoir physical properties and oil content in the study area. Figure 4This is a schematic diagram of the relationship between electrical properties and oil content in the study area. It can be seen that the physical properties of reservoirs with different lithologies will also change accordingly, and the fluid saturation in the reservoir is also different, which is very important for studying the relationship between oil content and lithology. By collecting reservoir physical property data and logging curve data, it can be observed that the conductivity of water is higher than that of the oil layer, so the resistivity value of the reservoir when it contains water is lower than that when it contains oil. The low-resistance reservoirs in the study area usually do not show obvious oil-water boundaries, resulting in uncertainty in the reflection of resistivity on oil content in the logging electrical characteristics. The characteristics of low-resistance oil layers include low electrical contrast between oil layers and water layers. The analysis of the four-property relationship of the reservoir shows that it has low porosity and low permeability characteristics, and also has unique characteristics, especially the low-resistance characteristics of the oil layer.

[0046] Step S2 is based on the relationship between reservoir lithology, physical properties, electrical properties and hydrocarbon content, combined with well logging data, regional geological data and core analysis data and oil reservoir conditions in the study area, to determine the cause of the low resistivity oil layer in the study area. In a possible implementation, the cause of the low resistivity oil layer in the study area includes: formation water salinity, clay mineral conductivity and wetting effect, pore content, bound water saturation and mineral type.

[0047] Specifically, based on the analysis of the reservoir characteristics and the relationship between the "four properties" in the study area, combined with the well logging data, regional geological data, core analysis data and the reservoir conditions in the study area, the formation causes of low resistivity oil layers in the study area were studied. The analysis found that the main reasons for the low resistivity of the target reservoir can be found from the aspects of formation water salinity, conductivity and wetting effect of clay minerals, pore content, bound water saturation, mineral type, etc. See Table 1.

[0048] Table 1 Formation water water analysis table

[0049]

[0050] As can be seen from Table 1, the mineralization of formation water has a significant effect on the resistivity of oil and gas layers. Highly mineralized formation water contains more dissolved salts, which dissociate into positive and negative ions in water, increasing the conductivity of water and thus reducing the resistivity of formation water. This highly conductive formation water will lead to a decrease in the resistivity of oil and gas layers, reducing the contrast between oil and water resistivity. Therefore, the mineralization of formation water can be used as one of the causes of low-resistivity oil layers in the study area.

[0051] The presence of clay minerals may lead to the formation of microporous structures in the reservoir, increase the saturation of bound water, and thus reduce the resistivity. In addition, clay minerals themselves may have additional conductivity, further reducing the formation resistivity. Therefore, the type, conductivity and wettability of clay minerals can be regarded as one of the causes of low resistivity oil layers in the study area.

[0052] The saturation of irreducible water also has a significant effect on the resistivity of the reservoir. By analyzing the test data such as capillary pressure test and oil-water two-phase seepage experiment, various pore structure characterization parameters can be extracted. Combined with the saturation of irreducible water, an intersection chart can be made to better understand the relationship between the complexity of the reservoir pore structure and the irreducible water saturation. The more complex the pore structure of the reservoir, the higher the corresponding irreducible water saturation, which may lead to a further decrease in resistivity. Therefore, pore content and irreducible water saturation can be used as one of the causes of low-resistivity oil layers in the study area.

[0053] In summary, factors such as formation water salinity, conductivity and wetting effect of clay minerals, pore content, bound water saturation and mineral type work together to affect the formation of low-resistivity oil layers.

[0054] Step S3 is based on the genesis of the low resistivity oil layer in the study area, and selects the apparent resistivity increase rate, the bound water saturation T2 cutoff value measured by nuclear magnetic resonance, and the natural potential amplitude ratio as the research parameters, and obtains the comprehensive low resistivity oil layer identification result based on the weighted fusion of the three research parameters. In a possible implementation, S3 includes: S31, calculating the three research parameters of the study area, including: the apparent resistivity increase rate, the bound water saturation T2 cutoff value measured by nuclear magnetic resonance, and the natural potential amplitude ratio; S32, selecting the corresponding BP neural network model based on the genesis of the low resistivity oil layer in the study area, bringing the three research parameters of the study area into the BP neural network model, obtaining the correlation index between the three research parameters and the low resistivity oil layer, and determining the weight according to the correlation index; S33, determining the scores of the three research parameters based on the values ​​of the three research parameters in the study area; S34, solving the comprehensive score based on the weights and scores of the three research parameters, and determining whether the reservoir in the study area belongs to an oil layer, a low resistivity oil layer or a water layer based on the range of the comprehensive score.

[0055] Step S31 is to calculate three research parameters of the study area, including: apparent resistivity increase rate, nuclear magnetic resonance measurement of bound water saturation T2 cutoff value, and natural potential amplitude ratio. Specifically, in order to better utilize the logging curve reflecting the information of the oil and water layer and apply it to the logging identification of the low-resistivity oil layer, based on the natural potential and resistivity logging curves, the equation change derivation and data processing methods are used to extract the formation bound water saturation T2 cutoff value, natural potential amplitude ratio, and apparent resistivity increase rate. These three research parameters are used to comprehensively identify the low-resistivity oil layer. The three research parameters are derived from different data. Fusion of this information can effectively improve the ability to identify oil and water layers, rather than relying solely on a single data. In addition, the formation bound water saturation T2 cutoff value, one of the research parameters, is obtained based on the non-destructive, sensitive and fast nuclear magnetic resonance principle, which can assist in identifying the water saturation of the reservoir and further improve the accuracy of the low-resistivity oil layer determination.

[0056] Furthermore, the apparent resistivity increase rate RI (also known as the oil and gas layer resistivity index) refers to the ratio between the resistivity (Rt) of the formation containing oil and gas and the resistivity (Rw) of the formation containing 100% water. Similarly, the apparent resistivity increase rate in the formation can be defined as the ratio of the formation resistivity to the typical water layer resistivity, expressed as:

[0057] RI=(Rt-Rw) / Rw

[0058] Where: RI is the apparent resistivity increase rate; Rt is the formation resistivity, unit is Ω·m; Rw is the resistivity of the typical water layer, unit is Ω·m.

[0059] Furthermore, Nuclear Magnetic Resonance Spectroscopy (NMR) measures the transition of hydrogen nuclei in the fluid from a low energy state to a high energy state under the action of an external static magnetic field after the rock sample is saturated with water or oil. At this time, if a certain magnetic field is added, a nuclear magnetic resonance phenomenon will occur, in which the magnetization vector recovers in the direction of the static magnetic field and is decomposed into a longitudinal vector and a transverse vector. T1 is the longitudinal relaxation time, and T2 is the transverse relaxation time. In reservoir analysis, the T2 cutoff value is a key parameter that can be used to determine the bound water saturation. The presence of bound water has an important impact on the production capacity of the reservoir because the bound water occupies a part of the pore space and reduces the effective pore volume available for oil and gas flow. Formation bound water saturation S wirr for:

[0060]

[0061] Where: S WIRR (T 2i ) is the capillary bound water saturation of rock; T 2i is the i-th transverse relaxation time component in the T2 spectrum; T 2CIRR is the transverse relaxation time corresponding to the capillary force in the bound water state. Based on this, the bound water saturation T2 cutoff value S can be calculated. wirr .

[0062] Furthermore, low-resistivity oil layers are often accompanied by higher mud content and bound water saturation. The bound water mineralization is higher than that of ordinary formation water, which can cause the natural potential amplitude difference to be further reduced. In addition, under the same conditions, the reduction in positive amplitude difference of oil layers in saline water slurry environment is more significant than that of water layers, and the higher the oil saturation, the more obvious the reduction effect of positive amplitude difference. The natural potential amplitude difference of low-resistivity oil layers is significantly lower than that of water layers. According to this principle, in the identification of low-resistivity oil layers, on the one hand, low-resistivity oil layers are identified based on the numerical difference in the natural potential amplitude difference between oil layers and water layers. The natural potential amplitude of low-resistivity oil layers in different oil fields is 0.7-0.5 times that of water layers on average; on the other hand, low-resistivity oil layers are identified based on the numerical relationship between the natural potential amplitude difference of known low-resistivity oil layers and the natural potential amplitude difference of other adjacent reservoirs. The natural potential amplitude ratio is:

[0063] Spi=(SP'–SPw) / SPw

[0064] Where: Spi is the natural potential amplitude ratio, SP' is the natural potential of the low-resistance oil layer, and SPw is the natural potential of the water layer.

[0065] Step S32 is to select the corresponding BP neural network model based on the genesis of the low resistivity oil layer in the study area, bring the three research parameters of the study area into the BP neural network model, and obtain the correlation index between the three research parameters and the low resistivity oil layer as the weight. Further, the BP neural network model is trained by the data of the low resistivity oil layer of the same genesis.

[0066] Specifically, see Figure 5 As shown, Figure 5 The schematic diagram of the BP neural network model is shown in Figure 2. The idea of ​​cluster analysis can better deal with low-resistivity oil layers with various causes. In the study, the apparent resistivity increase rate RI and the bound water saturation T2 cutoff value S of the nuclear magnetic resonance measurement were selected. wirr , natural potential amplitude ratio Spi and other research parameters are used to distinguish between oil layers and water layers according to the characteristics of research parameter aggregation of the same fluid type, see Table 2.

[0067] Table 2 Relationship between research parameters and fluid types

[0068]

[0069] Construct a BP neural network model to learn the correlation between research parameters and test conclusions (water layer, low-resistivity oil layer, oil layer) under different genetic conditions. The learning process of the BP neural network model is divided into two parts: forward propagation and back propagation. In the multi-layer BP neural network model for identifying low-resistivity oil and gas layers, Figure 5Among them, the network has a three-layer structure: an input layer, a hidden layer, and an output layer. The input layer is the first layer. Since the low-resistivity oil layer is related to the research parameters, this layer consists of neurons corresponding to three research parameters (apparent resistivity increase rate RI, nuclear magnetic resonance measured irreducible water saturation T2 cut-off value S wirr , spontaneous potential amplitude ratio Spi), and at the same time, the test conclusion LRR (oil layer, low-resistivity oil layer, or water layer) is input; the second layer is the hidden layer. Although the neurons in the hidden layer are not directly connected to the outside world, their state changes can affect the relationship between the input and the output; the third layer is the output layer, including the correlation parameters between the three research parameters and the test conclusion LRR, a total of three neurons. The weights of the three research parameters are determined according to the three correlation parameters output by the BP neural network model. The larger the correlation parameter, the larger the corresponding allocated weight. Among them, the weight wr of the apparent resistivity increase rate RI, the weight wi of the nuclear magnetic resonance measured irreducible water saturation T2 cut-off value S wirr , and the weight ws of the spontaneous potential amplitude ratio Spi satisfy wr + wi + ws = 1.

[0070] It should be noted that in order to ensure that the BP neural model can generate accurate outputs for specific inputs, it is necessary to perform multiple trainings in combination with known samples, continuously adjust and optimize the connection weights and the bias values of the neurons until the expected output results are achieved. Finally, the connection weights and neuron bias values of each layer obtained during the training of multiple known samples are stored to facilitate the prediction and analysis of unseen samples.

[0071] Furthermore, in step S33, based on the values of the three research parameters in the study area, the scores of the three research parameters are determined; including: according to the intervals into which the values of the three research parameters fall, the scores of the three research parameters are determined.

[0072] Specifically, the ranges of the values of the three research parameters are comprehensively used to distinguish low-resistivity oil layers, low-resistivity oil layers, and water layers. Since the value ranges of the three research parameters are different, for unified measurement, the values of the research parameters are normalized to scores. For example, if RI > 2.8, then the score of the apparent resistivity increase rate RI, score(RI) = 1; if 0.2 < RI ≤ 2.8, then score(RI) = 0.5; if RI ≤ 0.2, then score(RI) = 0; if S wirr > 3.3, then the score of the nuclear magnetic resonance measured irreducible water saturation T2 cut-off value S wirr , score(S wirr ) = 1; if 1.6 < S wirr ≤ 3.3, then score(S wirr ) = 0.5; if S wirr ≤ 1.6, then score(S wirr) = 0; if Spi < -5.8, the score of the spontaneous potential amplitude ratio Spi, score(Spi) = 1; if -5.8 ≤ Spi < -1.3, score(Spi) = 0.5; if -1.3 ≤ Spi, score(Spi) = 0.

[0073] Further, in step S34, the comprehensive score is solved based on the weights and scores of the three research parameters, and the reservoir in the study area is determined to be an oil layer, a low-resistivity oil layer, or a water layer based on the range of the comprehensive score; wherein, the comprehensive score is obtained through the following formula:

[0074] Index = wr × score(RI) + wi × score(S wirr ) + ws × score(Spi)

[0075] In the formula: Index is the comprehensive score; wr, wi, and ws are the weights of the apparent resistivity increase rate RI, the nuclear magnetic resonance measured irreducible water saturation T2 cut-off value S wirr , and the spontaneous potential amplitude ratio Spi, respectively, and wr + wi + ws = 1.

[0076] Specifically, the three research parameters RI, Swirr, and Spi are input into the BP neural network model to obtain the weights wr, wi, and ws of the research parameters; the scores score(RI), score(S wirr ) and score(Spi) of the research parameters are obtained based on the three research parameters RI, Swirr, and Spi. The comprehensive score Index is solved by combining the scores and weights of the three research parameters, and the reservoir type is judged as: oil layer, low-resistivity oil layer, water layer according to the comprehensive score Index.

[0077] Research shows that when wr = 0.4, wi = 0.3, and ws = 0.3, for the oil layer Index > 0.75, for the low-resistivity oil layer 0.25 < Index < 0.75, and for the water layer Index < 0.25. For example, when RI = 2.4, S wirr = 3.2, and Spi = -3.5, score(RI) = 0.5, score(S wirr ) = 0.5, score(Spi) = 0.5; Index = 0.3 × 0.5 + 0.3 × 0.5 + 0.3 × 0.5 = 0.45, then the reservoir is a low-resistivity oil layer.

[0078] See Figure 6 as shown, Figure 6 is a schematic diagram of the logging interpretation conclusion of a certain reservoir. In Figure 6The results of identifying the fluid type of low-resistivity oil layers in well sections 1# and 2# (3824-3826ft, 3824-3826ft) are shown in the figure. According to the characteristics of the logging curve, it can be seen that the natural potential shows a negative anomaly, the compensation density is a medium-low value, the acoustic time difference is a medium-high value, and the resistivity is obviously low at 1-3Ω, which is a typical low-resistance feature, but it is difficult to judge the oil and water layers from the electrical properties. At this time, it can be seen from the formation water analysis data that the formation water salinity is 29380mg / L, and the formation water salinity is high, so it may be a low-resistivity oil layer caused by the formation water salinity. Furthermore, the three research parameters of the formation are solved in combination with the method of this application, and the three research parameters are brought into the BP neural network model of the formation water salinity that has been trained in advance to obtain the research parameter weights, and finally the weighted solution is used to obtain the comprehensive score, and the fluid type of the formation is judged to be a low-resistivity oil layer based on the comprehensive score. Finally, it was verified that the well indeed belonged to a low-resistivity oil layer, and a daily oil production of 89-123 bbls / d was obtained, confirming the reliability of the method of the present application.

[0079] It can be seen that the low-resistivity oil layer identification method based on the comprehensive multi-factor logging information provided by the present application fully exploits the resistivity logging information, while taking into account the influence of oil and gas on resistivity, natural potential, and irreducible water saturation, thus avoiding the problem that the conventional resistivity method is difficult to explain the low-resistivity oil layer, and realizing complementary advantages; all wells in the work area have comprehensive logging curves, and the apparent resistance increase rate and natural potential amplitude ratio can be directly obtained from the logging curves, and the irreducible water saturation can be directly obtained based on nuclear magnetic resonance measurement. Therefore, the identification method data of the present application is easy to obtain, and has broad prospects and value for promotion and application. In the identification of some low-resistivity oil layers with oil test data in the work area, the identification accuracy rate is as high as 90.5%.

[0080] In addition, the low-resistivity oil layer identification method based on multi-factor logging information provided by this application is convenient for data collection and can be collected according to actual conditions. When conditions are limited, it is only necessary to obtain basic influencing factor data; a prediction model is established through mathematical methods, and low-resistivity oil layers are identified using easily available data, and then the effect of repeated fracturing of a single well can be predicted and evaluated, thereby achieving low-cost development and showing broad application prospects. Compared with the prior art, the method of this application is simple and intuitive to calculate, and there is no need to consider the complex interactions between oil and gas layer geology and fracturing engineering properties, nor is there any need to consider their coupled effects on the effect of repeated fracturing of a single well.

[0081] See also Figure 7 As shown, Figure 7The structure diagram of the low-resistivity oil layer identification system based on multi-factor logging information integration is shown in FIG. The system is used to execute the above-mentioned low-resistivity oil layer identification method based on multi-factor logging information integration. The system includes: a four-property analysis module, which is used to obtain the data of the study area, including: logging data, regional geological data and core analysis data, and analyze the relationship between reservoir lithology, physical properties, electrical properties and oil content based on the data of the study area; a genesis analysis module, which is used to determine the genesis of the low-resistivity oil layer in the study area based on the relationship between reservoir lithology, physical properties, electrical properties and oil and gas content, combined with logging data, regional geological data and core analysis data and the oil reservoir conditions in the study area; a comprehensive identification module, which is used to select the apparent resistivity increase rate, the nuclear magnetic resonance measurement bound water saturation T2 cutoff value, and the natural potential amplitude ratio as research parameters based on the genesis of the low-resistivity oil layer in the study area, and obtain the comprehensive low-resistivity oil layer identification result based on the weighted fusion of the three research parameters.

[0082] It should be noted that the low-resistivity oil layer identification system based on multi-factor logging information integration of the present application is used to implement the above-mentioned low-resistivity oil layer identification method based on multi-factor logging information integration, which corresponds one-to-one with the above-mentioned method and has corresponding effects, so it will not be elaborated on here.

[0083] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying low-resistivity oil layers based on comprehensive multi-factor logging information, characterized in that: include: Obtain data from the study area, including well logging data, regional geological data and core analysis data, and analyze the relationship between reservoir lithology, physical properties, electrical properties and oil content based on the data from the study area; Based on the relationship between reservoir lithology, physical properties, electrical properties and oil and gas properties, combined with well logging data, regional geological data and core analysis data as well as the reservoir conditions in the study area, the origin of the low resistivity oil layer in the study area was determined; Based on the genesis of low-resistivity oil layers in the study area, the apparent resistivity increase rate, the T2 cutoff value of bound water saturation measured by nuclear magnetic resonance and the natural potential amplitude ratio were selected as research parameters. The comprehensive low-resistivity oil layer identification result was obtained based on the weighted fusion of the three research parameters.

2. The method for identifying low-resistivity oil layers based on multi-factor logging information integration according to claim 1 is characterized in that: Based on the relationship between reservoir lithology, physical properties, electrical properties and oil and gas content, combined with logging data, regional geological data and core analysis data as well as the reservoir conditions in the study area, the causes of the low resistivity oil layers in the study area are determined; wherein, the causes of the low resistivity oil layers in the study area include: formation water mineralization, conductivity and wetting effect of clay minerals, pore content, bound water saturation and mineral type.

3. The method for identifying low-resistivity oil layers based on multi-factor logging information integration according to claim 1 is characterized in that: Based on the genesis of low-resistivity oil layers in the study area, the apparent resistivity increase rate, the T2 cutoff value of bound water saturation measured by nuclear magnetic resonance, and the natural potential amplitude ratio were selected as research parameters. The comprehensive low-resistivity oil layer identification results were obtained based on the weighted fusion of the three research parameters; including: Three research parameters of the study area were calculated, including: apparent resistivity increase rate, bound water saturation T2 cutoff value measured by nuclear magnetic resonance, and natural potential amplitude ratio; Based on the genesis of the low resistivity oil layer in the study area, the corresponding BP neural network model is selected, the three research parameters of the study area are brought into the BP neural network model, the correlation index between the three research parameters and the low resistivity oil layer is obtained, and the weight is determined according to the correlation index; Based on the values ​​of the three research parameters in the study area, the scores of the three research parameters were determined; The comprehensive score is solved based on the weights and scores of the three research parameters, and the reservoir in the study area is determined to be an oil layer, a low-resistivity oil layer or a water layer based on the range of the comprehensive score.

4. The method for identifying low-resistivity oil layers based on multi-factor logging information integration according to claim 3 is characterized in that: The apparent resistance increase rate is obtained by the following formula: RI=(Rt-Rw) / Rw Where: RI is the apparent resistivity increase rate; Rt is the formation resistivity, unit is Ω·m; Rw is the resistivity of the typical water layer, unit is Ω·m.

5. The method for identifying low-resistivity oil layers based on multi-factor logging information integration according to claim 3 is characterized in that: Irreducible water saturation is calculated by the following formula: Where: S WTRR (T 2i ) is the capillary bound water saturation of rock; T 2i is the i-th transverse relaxation time component in the T2 spectrum; T 2CIRR is the transverse relaxation time corresponding to the capillary force in the bound water state.

6. The method for identifying low-resistivity oil layers based on multi-factor logging information integration according to claim 3 is characterized in that: The natural potential amplitude ratio is obtained by the following formula: Spi=(SP'-SPw) / SPw Where: Spi is the natural potential amplitude ratio, SP' is the natural potential of the low-resistance oil layer, and SPw is the natural potential of the water layer.

7. The method for identifying low-resistivity oil layers based on multi-factor logging information integration according to claim 3 is characterized in that: Based on the genesis of the low-resistivity oil layer in the study area, the corresponding BP neural network model is selected, and the three research parameters of the study area are brought into the BP neural network model to obtain the correlation index between the three research parameters and the low-resistivity oil layer as the weight; wherein, the BP neural network model is trained through the low-resistivity oil layer data of the same genesis.

8. The method for identifying low-resistivity oil layers based on multi-factor logging information integration according to claim 3 is characterized in that: Based on the values ​​of the three study parameters in the study area, the scores of the three study parameters are determined; including: The scores of the three research parameters are determined according to the intervals into which the values ​​of the three research parameters fall.

9. The method for identifying low-resistivity oil layers based on multi-factor logging information integration according to claim 3 is characterized in that: The comprehensive score is solved based on the weights and scores of the three research parameters, and the reservoir in the study area is determined to be an oil layer, a low-resistivity oil layer or a water layer based on the range of the comprehensive score; the comprehensive score is obtained by the following formula: Index=wr×score(RI)+wi×score(S wirr )+ws×score(Spi) Where: Index is the comprehensive score; wr, wi, and ws are the apparent resistance increase rate RI, the bound water saturation T2 cutoff value S measured by nuclear magnetic resonance, respectively. wirr , the weight of the natural potential amplitude ratio Spi, wr+wi+ws=1.

10. A low-resistivity oil layer identification system based on multi-factor logging information integration, characterized in that: A system for executing the low-resistivity oil layer identification method based on multi-factor logging information integration as described in any one of claims 1 to 9, comprising: Four-property analysis module, used to obtain data of the study area, including well logging data, regional geological data and core analysis data, and analyze the relationship between reservoir lithology, physical properties, electrical properties and oil content based on the data of the study area; Genesis analysis module, which is used to determine the genesis of low resistivity oil layers in the study area based on the relationship between reservoir lithology, physical properties, electrical properties and oil and gas properties, combined with well logging data, regional geological data and core analysis data and the oil reservoir conditions in the study area; The comprehensive identification module is used to select the apparent resistivity increase rate, the T2 cutoff value of bound water saturation measured by nuclear magnetic resonance, and the natural potential amplitude ratio as research parameters based on the genesis of the low-resistivity oil layer in the study area, and obtain the comprehensive low-resistivity oil layer identification result based on the weighted fusion of the three research parameters.

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