A method for correcting permeability difference before and after water flooding of sandstone reservoirs

Abstract

The present application relates to the technical field of permeability difference determination, and particularly relates to a sandstone reservoir water-flooded permeability difference correction method. The method comprises the following steps: obtaining parameter data, formation temperature and air permeability of oil wells of different reservoirs of a pair of wells before and after water-flooding of a sandstone reservoir; obtaining a comprehensive invasion index of each reservoir according to the value of the deep lateral resistivity of the sampling point in each reservoir relative to the original formation resistivity; obtaining an abnormality degree index of each sampling point according to the difference of the parameter data of different sampling points; correcting the natural potential value, mud resistivity and formation temperature by combining the comprehensive invasion index and the abnormality degree index to obtain a target potential, a mud target resistivity and a target temperature, and determining the oil displacement efficiency of the oil well after water-flooding; and obtaining the corrected permeability change rate before and after water-flooding by using the oil displacement efficiency and the air permeability of the oil well before and after water-flooding. The present application improves the accuracy of the evaluation result of the permeability difference before and after water-flooding of the sandstone reservoir.

Description

Technical Field

[0001] This invention relates to the field of permeability difference measurement technology, specifically to a method for correcting permeability differences in sandstone reservoirs before and after water flooding. Background Technology

[0002] After years of development, most old oilfields in China have entered the high water-cut extraction stage. Long-term water erosion has significantly impacted the reservoir's mineral composition, pore structure, and permeability. The law of permeability variation has become a key research area for petroleum engineers and geologists, directly affecting the extraction efficiency and economic benefits of oil and gas. The permeability variation of water-flooded layers is influenced by multiple factors, including the chemical properties of injected water (temperature, pressure, salinity, pH, etc.) and the physical properties of the reservoir rocks (mineral composition, pore structure, permeability, etc.). The combined effect of these factors leads to changes in the pore structure and permeability of the water-flooded layer rocks.

[0003] Current research on permeability changes in water-flooded formations mainly includes laboratory simulations, numerical simulations, geophysical techniques, and microscopic experiments. While current research has yielded some results in experimental techniques and numerical simulations, these studies are conducted under ideal parameter settings and cannot realistically simulate the dynamic changes in various formation parameters. Furthermore, while various legal interpretations have provided qualitative explanations for the permeability variations under different displacement conditions, there is no quantitative interpretation standard, making them impractical for real-world applications. Therefore, accurately establishing a method for correcting permeability differences before and after waterflooding has become a crucial problem urgently needing to be solved by geological logging professionals. In oil extraction, the formation water resistivity measurement after waterflooding is a vital parameter for calculating waterflooded oil displacement efficiency and has a significant impact on accurately establishing a permeability difference correction model before and after waterflooding. Traditional methods for measuring formation water resistivity, such as those based on NaCl mineralization analysis of local water samples, do not consider the impact of water injection during waterflooding in oil well production on formation water at each well. This leads to significant errors in the measured formation water resistivity, making it difficult to accurately represent the oil displacement efficiency of the well and consequently affecting the accuracy of the evaluation results on the permeability difference before and after waterflooding. Summary of the Invention

[0004] To address the issue of low accuracy in existing methods for evaluating permeability differences in sandstone reservoirs before and after water flooding, this invention aims to provide a method for correcting permeability differences in sandstone reservoirs before and after water flooding. The specific technical solution adopted is as follows:

[0005] This invention provides a method for correcting the permeability difference in sandstone reservoirs before and after water flooding, the method comprising the following steps:

[0006] Obtain parameter data, formation temperature and air permeability of different reservoirs in a pair of well groups before and after water flooding of sandstone reservoirs. The parameter data includes spontaneous potential, deep lateral resistivity, original formation resistivity and mud resistivity.

[0007] Based on the magnitude of the deep lateral resistivity relative to the original formation resistivity at each sampling point in the reservoir, the comprehensive invasion index of each reservoir is obtained; based on the differences in parameter data at different sampling points, the anomaly index of each sampling point is obtained.

[0008] By combining the comprehensive intrusion index and the anomaly index, the spontaneous potential, mud resistivity and formation temperature are corrected to obtain the target potential, target mud resistivity and target temperature.

[0009] Based on the target potential, target resistivity of the mud, and target temperature, the oil displacement efficiency of the oil well after water flooding is determined; using the oil displacement efficiency and the air permeability of the oil well before and after water flooding, the corrected permeability change rate before and after water flooding is obtained.

[0010] Preferably, the step of obtaining the comprehensive invasion index of each reservoir based on the value of the deep lateral resistivity of each sampling point relative to the original formation resistivity includes:

[0011] The ratio of the deep lateral resistivity to the original formation resistivity at each sampling point in each reservoir is calculated and used as the invasion index for each sampling point in each reservoir.

[0012] The average invasion index of all sampling points in each reservoir is used as the comprehensive invasion index of each reservoir.

[0013] Preferably, the step of obtaining the anomaly index for each sampling point based on the differences in parameter data at different sampling points includes:

[0014] Based on the difference in natural potential values ​​between each sampling point and its neighboring sampling points, and the degree of fluctuation in the natural potential values ​​of all sampling points in the reservoir where each sampling point is located, abnormal sampling points are screened.

[0015] For any sampling point, the anomaly index of that sampling point is obtained based on the difference in parameter data between that sampling point and its neighboring sampling points and the consistency of the difference in parameter data between that sampling point and the abnormal sampling points.

[0016] Preferably, the step of screening outlier sampling points based on the difference in natural potential values ​​between each sampling point and its neighboring sampling points, and the degree of fluctuation in the natural potential values ​​of all sampling points in the reservoir where each sampling point is located, includes:

[0017] For any sampling point, the local jump index of the potential value of any sampling point is obtained based on the difference in natural potential values ​​between the sampling point and its neighboring sampling points, and the standard deviation of the natural potential values ​​of all sampling points in the reservoir where the sampling point is located. The difference in natural potential values ​​between the sampling point and its neighboring sampling points is positively correlated with the local jump index, and the standard deviation is negatively correlated with the local jump index. The standard deviation is used to reflect the degree of fluctuation of the natural potential value.

[0018] Based on the value of the local jump index of the potential value of any sampling point, determine whether any sampling point is an abnormal sampling point.

[0019] Preferably, determining whether any sampling point is an abnormal sampling point based on the value of the local jump index of the potential value of any sampling point includes: if the local jump index of the potential value of any sampling point is greater than a preset jump threshold, then the any sampling point is determined to be an abnormal sampling point.

[0020] Preferably, the step of obtaining the anomaly index of any sampling point based on the difference in parameter data between any sampling point and its neighboring sampling points and the consistency of the difference in parameter data between any sampling point and anomalous sampling points includes:

[0021] Calculate the first DTW distance between the parameter data sequence of any sampling point and the parameter data sequence of each sampling point in its neighborhood, and the second DTW distance between the parameter data sequence of any sampling point and the parameter data sequence of each abnormal sampling point in the reservoir in which it is located;

[0022] The ratio between the average of all first DTW distances corresponding to any sampling point and the average of all second DTW distances corresponding to any sampling point is used as the anomaly index of any sampling point.

[0023] The parameter data sequence is composed of parameter data.

[0024] Preferably, the step of combining the comprehensive intrusion index and the anomaly index to correct the spontaneous potential value, mud resistivity, and formation temperature to obtain the target potential, target mud resistivity, and target temperature includes:

[0025] For any sampling point, the mud intrusion effect factor of any sampling point is obtained based on the comprehensive intrusion index of the reservoir where the sampling point is located and the anomaly index of the sampling point.

[0026] Based on the mud intrusion effect factor of all sampling points, the contribution weight of each sampling point is determined respectively.

[0027] The spontaneous potential, mud resistivity, and formation temperature are corrected using the contribution weights to obtain the target potential, target mud resistivity, and target temperature.

[0028] Preferably, the determination of the contribution weight of each sampling point based on the mud intrusion effect factor of all sampling points includes:

[0029] Calculate the sum of the mud intrusion effect factors at all sampling points;

[0030] The ratio between the mud intrusion effect factor of each sampling point and the summation is used as the contribution weight of each sampling point.

[0031] Preferably, the step of using the contribution weights to correct the spontaneous potential, mud resistivity, and formation temperature to obtain the target potential, target mud resistivity, and target temperature includes:

[0032] The target potential is obtained by weighting the natural potential values ​​of all sampling points by using the contribution weight of each sampling point.

[0033] The target resistivity of the mud is obtained by weighting the mud resistivity of all sampling points by using the contribution weight of each sampling point.

[0034] By utilizing the contribution weight of each sampling point, the formation temperature of all sampling points is weighted and averaged to obtain the target temperature.

[0035] Preferably, determining the oil displacement efficiency of the well after water flooding based on the target potential, target mud resistivity, and target temperature includes:

[0036] Based on the spontaneous potential method, target potential, target resistivity of mud, and target temperature, the formation water resistivity of the oil well after water flooding is obtained.

[0037] The water saturation of the oil well after water flooding is calculated based on the formation water resistivity, original formation resistivity, and effective porosity.

[0038] The oil displacement efficiency of the water-flooded oil well is determined based on the water saturation of the well after water flooding.

[0039] Preferably, determining the oil displacement efficiency of the water-flooded oil well based on the water saturation of the water-flooded oil well includes:

[0040] Calculate the first difference between the water saturation and the bound water saturation of the oil well after water flooding, and the second difference between constant 1 and the bound water saturation;

[0041] The ratio between the first difference and the second difference is determined as the oil displacement efficiency of the oil well after water flooding.

[0042] Preferably, obtaining the corrected permeability change rate before and after waterflooding by utilizing the oil displacement efficiency and the air permeability of the oil well before and after waterflooding includes:

[0043] Calculate the initial rate of change of air permeability based on multiple air permeability values ​​of the oil wells before and after flooding;

[0044] The function relationship between the initial rate of change of air permeability and the oil displacement efficiency is fitted. The oil displacement efficiency of the well after water flooding is then substituted into the function relationship to obtain the corrected rate of change of permeability before and after water flooding.

[0045] Preferably, obtaining the air permeability of an oil well includes:

[0046] The effective porosity of oil wells is obtained based on acoustic and density data.

[0047] Based on the effective porosity of the oil well before and after water flooding, the air permeability of the oil well before and after water flooding is obtained.

[0048] The present invention has at least the following beneficial effects:

[0049] This invention constructs a comprehensive invasion index by analyzing deep lateral resistivity and original formation resistivity to quantify the degree of mud invasion; and constructs an anomaly index by comparing the differences in parameter data from different sampling points to identify abnormal data fluctuations. The comprehensive invasion index and the anomaly index reflect the overall degree of mud invasion in the reservoir and the abnormal fluctuations in data from each sampling point, respectively. By correcting spontaneous potential, mud resistivity, and formation temperature, more reliable target potential, mud target resistivity, and target temperature are obtained. This suppresses data noise caused by invasion effects and heterogeneous distribution, overcomes the influence of mud invasion and heterogeneous fluids on measurement data, and further improves the accuracy of oil displacement efficiency based on these corrected parameters. This provides a reliable data foundation for permeability difference correction and improves the calculation accuracy of formation water resistivity and oil displacement efficiency. By integrating multiple types of monitoring data, and based on effectively correcting the effects of mud invasion and heterogeneity, quantitative and accurate evaluation of permeability differences before and after water flooding is achieved, improving the accuracy and reliability of permeability change analysis in sandstone reservoirs during high water-cut periods. Attached Figure Description

[0050] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart illustrating a method for correcting permeability differences in sandstone reservoirs before and after water flooding, provided in an embodiment of the present invention;

[0052] Figure 2 This is a diagram showing the rhythmic segmentation results of an oil well before water flooding, provided in an embodiment of the present invention.

[0053] Figure 3 This is a diagram showing the rhythmic segmentation results of an oil well after water flooding, provided in an embodiment of the present invention.

[0054] Figure 4 The diagram showing the relationship between air permeability and effective porosity of an oil well provided in an embodiment of the present invention;

[0055] Figure 5 A graph showing the relationship between air permeability and water saturation in an oil well, provided in an embodiment of the present invention.

[0056] Figure 6 This is a calibration chart of the rate of change of air permeability when the initial rate of change of air permeability is greater than 1000 mD, provided in an embodiment of the present invention.

[0057] Figure 7 This is a calibration chart of the rate of change of air permeability when the initial rate of change is less than 300 mD, provided in an embodiment of the present invention.

[0058] Figure 8 This is a comprehensive result diagram illustrating the correction and interpretation of the change rate of oil well permeability before and after water flooding, provided in an embodiment of the present invention. Detailed Implementation

[0059] To further illustrate the technical means and effects of this invention in achieving its intended purpose, the following detailed description, in conjunction with the accompanying drawings and preferred embodiments, describes a method for correcting the permeability difference of sandstone reservoirs before and after water flooding, based on this invention.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0061] The following describes in detail, with reference to the accompanying drawings, a specific scheme for the method of correcting permeability differences in sandstone reservoirs before and after water flooding provided by the present invention.

[0062] Example of a method for correcting permeability differences in sandstone reservoirs before and after water flooding:

[0063] This embodiment proposes a method for correcting the permeability difference in sandstone reservoirs before and after water flooding, such as... Figure 1 As shown, the method for correcting the permeability difference in sandstone reservoirs before and after water flooding in this embodiment includes the following steps:

[0064] Step S1: Obtain parameter data, formation temperature and air permeability of different reservoirs in the pair of well groups before and after water flooding of the sandstone reservoir. The parameter data includes spontaneous potential value, deep lateral resistivity, original formation resistivity and mud resistivity.

[0065] First, the pair of well groups at different times before and after flooding were obtained. In this embodiment, the wells in the early stage of development and the wells in the later stage of development were the same well group at the same well point in the early stage of development around 1980 and the high water cut period in 2010, respectively. The developed sand bodies were braided channel sediments with stable thickness. Overall, the trend was from flooding to moderate to strong flooding. They were recorded as oil wells before flooding and oil wells after flooding, respectively.

[0066] For oil wells before water flooding, the thick layers were divided into rhythmic segments as follows: Rhythmic segment 1: 0.48 m thick, unwashed; Rhythmic segment 2: 0.58 m thick, unwashed; Rhythmic segment 3: 0.96 m thick, unwashed; Rhythmic segment 4: 0.65 m thick, upper part unwashed; Rhythmic segment 5: 0.48 m thick, unwashed. The rhythmic segment division results of the oil wells before water flooding are as follows: Figure 2 As shown.

[0067] For water-flooded oil wells, the thick layers are divided into rhythmic segments as follows: Rhythmic segment 1: 0.48 m thick, heavily washed; Rhythmic segment 2: 0.89 m thick, heavily washed; Rhythmic segment 3: 0.86 m thick, lower part moderately washed; Rhythmic segment 4: 0.48 m thick, unwashed; Rhythmic segment 5: 0.65 m thick, lower part moderately washed. The rhythmic segment division results of water-flooded oil wells are as follows: Figure 3 As shown.

[0068] For wells before and after water flooding, select reservoir sections in the same sedimentary unit and rhythmic region where the sedimentary environment is relatively stable; for wells in pairs, the reservoir sections should have undergone changes from unwashed and weakly washed to medium and high washed.

[0069] The system collects relevant data from different depths of the oil well before and after water flooding using an intelligent sensing system, and acquires acoustic and density data. The wellbore axis is submerged a preset distance to measure the relevant data at the current depth, which is equivalent to dividing the oil well into reservoirs. The thickness of each layer is a preset distance. In this embodiment, the preset distance is 0.1 meters. In specific applications, the implementer can set it according to the specific situation.

[0070] The intelligent sensing system consists of various intelligent sensors that collect data, including spontaneous potential, deep lateral resistivity, original formation resistivity, mud resistivity, and formation temperature. The acquisition frequency for spontaneous potential, deep lateral resistivity, original formation resistivity, and mud resistivity is 1kHz, while the acquisition frequency for formation temperature is 1Hz. In specific applications, implementers can set these frequencies according to their specific circumstances.

[0071] The spontaneous potential (SP) value was acquired using an SP logging sensor. The SP value is a potential response formed by factors such as the ion concentration difference between formation water and mud, reflecting the fluid properties in the formation. Deep lateral resistivity and raw formation resistivity data were acquired using an inductive resistivity logging sensor at different induction depths. The detection depth was set to [value missing]. Meters were used to obtain deep lateral resistivity at a depth of [missing information]. The original formation resistivity was obtained by measuring the meters. and The size can be set by the implementer according to the implementation scenario. In this embodiment, Empirical values ​​of 0.5 meters and 2 meters were used respectively. Deep lateral resistivity reflects the resistivity of the flushed zone (i.e., the area near the wellbore severely flushed by mud) in the formation; original formation resistivity reflects the resistivity of the unintruded rock mass within the formation. Mud resistivity data was acquired using a mud conductivity sensor, reflecting the resistivity of the mud itself at the wellbore in that formation. Formation temperature data was acquired using a logging temperature sensor, reflecting the temperature of the formation and used for temperature correction of the measured resistivity during spontaneous potential (SP) calculations. When collecting SP, mud resistivity, and formation temperature data, multiple evenly distributed sampling points were set up. SP, mud resistivity, and formation temperature were collected at each sampling point. The number of sampling points can be set by the implementer according to specific circumstances, for example, up to 100. SP, deep lateral resistivity, original formation resistivity, and mud resistivity were recorded as parameter data.

[0072] The above steps, based on an intelligent sensing system, enable the collection of relevant data, acoustic data, and density data of the oil well before and after flooding.

[0073] Then, multiple linear regression relationships were established between effective porosity and acoustic and density data for oil wells before and after water flooding. The effective porosity of the oil wells before water flooding can be expressed as: ,in, This indicates the effective porosity of the oil well before flooding. This represents the acoustic data of the oil well before it was flooded. This represents the density data of the oil well before water flooding. The effective porosity of the oil well after water flooding can be expressed as: ,in, This indicates the effective porosity of the oil well after flooding. This represents the acoustic data of the oil well after it was flooded. This represents the density data of the oil well after it was flooded.

[0074] Based on the effective porosity, a porosity-permeability regression relationship is established. That is, based on the effective porosity of the oil well before and after water flooding, the air permeability of the oil well before and after water flooding is obtained. The air permeability of the oil well before water flooding can be expressed as: The air permeability of an oil well after flooding can be expressed as: , This indicates the air permeability of the oil well after flooding. This represents the air permeability of the oil well before flooding, and e represents the natural constant. For example... Figure 4 and Figure 5 As shown, Figure 4 This is a graph showing the relationship between air permeability and effective porosity in oil wells. Figure 5 This is a graph showing the relationship between air permeability and water saturation in an oil well.

[0075] The air permeability of the oil well before and after flooding was determined using the above methods.

[0076] Step S2: Based on the value of the deep lateral resistivity of each sampling point in each reservoir relative to the original formation resistivity, obtain the comprehensive invasion index of each reservoir; based on the differences in parameter data of different sampling points, obtain the anomaly index of each sampling point.

[0077] While the method of calculating formation water resistivity based on equivalent NaCl salinity obtained from local water analysis data can quickly and accurately determine formation water resistivity, the injected water (usually low-salinity water) and the produced water after water flooding during the later stages of oilfield development will pollute the surrounding formation water, causing changes in the actual formation water resistivity. Therefore, the data obtained from past local water analysis data is unreliable. Furthermore, constructing a method to correct for differences in reservoir permeability under different periods and geological conditions requires formation water resistivity data from each reservoir, and obtaining formation water samples from each reservoir every time is too costly and time-consuming.

[0078] Traditional spontaneous potential (SP) is a conventional method for measuring formation water resistivity. However, in sandstone reservoirs during the later stages of waterflooding, the significant mud invasion effect causes irregularities in the wellbore surface due to mud coverage, resulting in anomalies in the measured SP amplitude. Furthermore, during waterflooding, variations in the pore-throat structure of the reservoir lead to fingering of injected water along high-permeability channels, invalidating the SP's assumption of reservoir homogeneity. The presence of water-oil mixtures in the reservoir causes localized abrupt changes in the measured SP curve, all of which reduce the reliability of the SP data. Traditional SP methods only correct for formation temperature and then perform a weighted average to obtain reservoir SP data, failing to consider the impact of the aforementioned factors, resulting in low data reliability and affecting the reliability of subsequent calculations of well displacement efficiency in the later stages of production.

[0079] To address the aforementioned issues, this embodiment evaluates the severity of mud invasion by analyzing the characteristics of deep lateral resistivity and original formation resistivity data at different reservoir depths, obtaining a comprehensive invasion index. Then, it analyzes the differences between the data at each sampling point and the data at adjacent sampling points to construct an anomaly index. Based on the comprehensive invasion index and the anomaly index, it constructs a contribution weight for the data at each sampling point and performs weighted calculation to obtain the spontaneous potential and mud resistivity data of the reservoir. This improves the accuracy of solving the formation water resistivity of the reservoir, thereby improving the accuracy of calculating the oil displacement efficiency of oil wells in the later stages of subsequent production.

[0080] During waterflooding of oil wells, the drilling mud carried by the injected water comes into contact with the reservoir. Sandstone reservoirs have a certain degree of permeability, and the mud filtrate gradually infiltrates the shallow area around the wellbore along the pore throat channels of the reservoir, forming a mud intrusion effect. The shallow area refers to the horizontally shallow area relative to the oil well. The electrochemical properties of mud filtrate and formation water differ significantly, resulting in a large difference in electrical properties between the shallow and deep areas of the reservoir. Furthermore, the mud-intruded area contains a mixture of mud filtrate and original formation water of different concentrations, leading to an unstable concentration gradient. The spontaneous potential method generates a potential response based on the diffusion potential difference between formation water and mud. Severe mud intrusion in the shallow area weakens the conditions for SP formation, causing the potential amplitude of the SP response to decrease or drift locally. Similarly, considering that mud intrusion results in a large amount of mud mixture in the shallow region of the reservoir, the actual measured mud resistivity does not match the mud resistivity of the entire reservoir. The more severe the mud intrusion effect, i.e., the greater the mud intrusion thickness, the more the mud resistivity is measured using a mud conductivity sensor, which measures the mud intrusion area, masking the original mud resistivity in the reservoir. Furthermore, with unstable changes in the mud concentration gradient, the mud resistivity will also exhibit dynamic changes and even distortion. Therefore, by analyzing the acquired deep lateral resistivity and original formation resistivity data, the severity of the mud intrusion effect is determined, leading to a comprehensive intrusion index.

[0081] As a specific example, the ratio of the deep lateral resistivity to the original formation resistivity at each sampling point in each reservoir is calculated, and this ratio is used as the invasion index for each sampling point in each reservoir; each sampling point has a corresponding invasion index. The closer the deep lateral resistivity is to the original formation resistivity, the smaller the influence of the mud invasion effect, meaning that the shallow region of the reservoir is less disturbed by mud filtrate and its resistivity is closer to the true formation resistivity, i.e., the invasion index is closer to 1. When the mud invasion effect is more severe, the injected water carries mud into the sandstone reservoir. In this case, the deep lateral resistivity is mainly controlled by mud filtrate, resulting in a lower resistivity value, and the invasion index is less than 1. For each reservoir, the average invasion index of all sampling points in each reservoir is calculated, and this average value is used as the comprehensive invasion index for each reservoir.

[0082] Considering that water injection flowing along the high-permeability pore-throat structure of the reservoir may prevent some oil from being carried out by water drive, the fluid medium in the reservoir exhibits heterogeneity. When the spontaneous potential method relies on the assumption of a single fluid, this heterogeneity of the fluid medium leads to abnormal amplitude variations in the obtained data sequence, causing data distortion. All of the above factors can cause abnormal interference in the measured data, resulting in low data reliability.

[0083] Therefore, based on the above characteristics, the next step is to screen out abnormal sampling points based on the difference in natural potential values ​​between each sampling point and its neighboring sampling points, and the degree of fluctuation in the natural potential values ​​of all sampling points in the reservoir where each sampling point is located. Specifically, for any sampling point, the local jump index of the potential value of the sampling point is obtained based on the difference in natural potential values ​​between the sampling point and its neighboring sampling points, and the standard deviation of the natural potential values ​​of all sampling points in the reservoir where the sampling point is located. The difference in natural potential values ​​between the sampling point and its neighboring sampling points is positively correlated with the local jump index, and the standard deviation is negatively correlated with the local jump index. The standard deviation is used to reflect the degree of fluctuation in the natural potential value. The larger the standard deviation, the greater the degree of fluctuation in the natural potential value.

[0084] As a concrete example, the specific formula for calculating the local jump index of the potential value is given. The local jump index of the potential value of the i-th sampling point of the s-th reservoir can be expressed as: ; in, This represents the local jump index of the potential value at the i-th sampling point of the s-th reservoir. This represents the potential value at the i-th sampling point of the s-th reservoir. This represents the potential value at the (i+1)th sampling point of the s-th reservoir. This represents the potential value at the (i-1)th sampling point of the s-th reservoir. Indicates the absolute value sign. This represents the standard deviation of the potential values ​​at all sampling points in the s-th reservoir. This indicates that the parameters are being adjusted.

[0085] In this embodiment, an adjustment parameter is introduced into the calculation formula to prevent the denominator from being 0. In this embodiment, the value of the adjustment parameter is 0.001. In specific applications, the implementer can set it according to the specific situation. The local jump index is the sum of the absolute values ​​of the differences between the potential value of the i-th sampling point in the s-th reservoir and the natural potential values ​​on its two adjacent sides. It is used to reflect the local jump intensity of the potential value of the i-th sampling point in the s-th reservoir. When the numerator of the local jump index calculation formula is greater than the denominator, it indicates that the local jump intensity of the potential value of the i-th sampling point in the s-th reservoir is relatively large, and this sampling point is more likely to be an anomalous sampling point.

[0086] Using the above method, the local jump index of the potential value at each sampling point can be obtained.

[0087] For any sampling point, if the local jump index of the potential value at that sampling point is greater than a preset jump threshold, then that sampling point is determined to be an abnormal sampling point. This method can filter out all abnormal sampling points for each reservoir. In this embodiment, the preset jump threshold is 1.

[0088] The following explanation uses one sampling point as an example. Other sampling points can be processed using the method provided in this embodiment.

[0089] Specifically, for any sampling point, an anomaly index is obtained based on the differences in parameter data between the sampling point and its neighboring sampling points, and the consistency of the differences in parameter data between the sampling point and anomalous sampling points. Specifically, the Dynamic Time Warping Distance (DTW) distance between the parameter data sequence of the sampling point and the parameter data sequences of each sampling point in its neighboring area is calculated, and this DTW distance is recorded as the first DTW distance. The DTW distance between the parameter data sequence of the sampling point and the parameter data sequences of each anomalous sampling point in its reservoir is also calculated, and this DTW distance is recorded as the second DTW distance. The parameter data sequence is composed of the normalized result of the parameter data; that is, the parameter data is first normalized, and then the normalized result is arranged to obtain the parameter data sequence. In this embodiment, the data normalization uses the maximum-minimum normalization method, which is an existing method and will not be described in detail here.

[0090] The ratio of the average of all first DTW distances corresponding to the sampling point to the average of all second DTW distances corresponding to the sampling point is used as the anomaly index for that sampling point. The calculation method for DTW distance is existing technology and will not be elaborated further here.

[0091] As a concrete example, the specific formula for calculating the anomaly index is given. The anomaly index of the i-th sampling point of the s-th reservoir can be expressed as: ; in, This represents the anomaly index of the i-th sampling point in the s-th reservoir. Let i represent the set of sampling points in the neighborhood of the i-th sampling point in the s-th reservoir. This represents the set of anomalous sampling points in the s-th reservoir. This represents the number of sampling points in the neighborhood of the i-th sampling point in the s-th reservoir. This represents the number of anomalous sampling points in the s-th reservoir. This represents the parameter data sequence of the i-th sampling point in the s-th reservoir. This represents the parameter data sequence of the j-th sampling point within the neighborhood of the i-th sampling point in the s-th reservoir. In the s-th reservoir, the first... The parameter data sequence of one abnormal sampling point This indicates the calculation of the DTW distance.

[0092] For any sampling point, its neighborhood is a window of a preset size centered on that sampling point. The size of the neighborhood can be set by the implementer according to the specific situation. It is necessary to ensure that the number of sampling points in the neighborhood of each sampling point is not less than 5. For example, the side length of the neighborhood can be 0.1 meters.

[0093] It should be noted that: when calculating the abnormality index, if the denominator of the calculation formula contains... If the result is 0, a zero-prevention parameter is added to the denominator before calculation. In this embodiment, the zero-prevention parameter is 0.001. In specific applications, the implementer can set it according to the specific situation. If there are no anomalous sampling points that meet the conditions in a certain reservoir, that is, the set of anomalous sampling points is empty, then the anomalousness index of all sampling points in the reservoir is directly set to 1.

[0094] This represents the first DTW distance corresponding to the i-th sampling point of the s-th reservoir. This represents the average of all first DTW distances corresponding to the i-th sampling point of the s-th reservoir; This represents the second DTW distance corresponding to the i-th sampling point of the s-th reservoir. This represents the average of all second DTW distances corresponding to the i-th sampling point of the s-th reservoir.

[0095] The anomaly index of the i-th sampling point in the s-th reservoir reflects whether the data of the i-th sampling point in the s-th reservoir maintains continuity and consistency with outlier data within its neighborhood. The larger the value, the more severe the abnormal jumps in the collected data caused by the heterogeneity in the reservoir due to mud intrusion or fingering phenomena, and the worse the reliability of the data at that sampling point. Conversely, the smaller the value, the more stable the data characteristics of that sampling point change over a period of time, and the more consistent they are with the characteristics of normal data points, and the higher their reliability.

[0096] The above methods can be used to determine the anomaly index of each sampling point.

[0097] Step S3: Combining the comprehensive intrusion index and the anomaly index, the spontaneous potential value, mud resistivity and formation temperature are corrected respectively to obtain the target potential, target mud resistivity and target temperature.

[0098] Traditional spontaneous potential methods determine the spontaneous potential value of a reservoir by weighted averaging of collected data. The previous method did not consider the impact of external interference on the actual measured values, resulting in significant data deviations. Therefore, this embodiment will consider constructing contribution weights based on the comprehensive intrusion index of the layer in which each data point is located and the anomaly index of the sampling point itself to obtain the spontaneous potential value and mud resistivity of the reservoir.

[0099] Specifically, for any given sampling point, the mud intrusion effect factor is obtained based on the comprehensive intrusion index of the reservoir where the sampling point is located and the anomaly index of the sampling point. Using this method, the mud intrusion effect factor for each sampling point can be obtained.

[0100] Calculate the sum of mud intrusion effect factors for all sampling points; use the ratio between the mud intrusion effect factor of each sampling point and the sum as the contribution weight of each sampling point.

[0101] In this embodiment, a specific formula for calculating the contribution weight is given. The contribution weight of the i-th sampling point in the s-th reservoir can be expressed as: ; in, This represents the contribution weight of the i-th sampling point in the s-th reservoir. This represents the comprehensive intrusion index of the s-th reservoir. This represents the anomaly index of the i-th sampling point in the s-th reservoir. This indicates that the parameters are being adjusted. Indicates the number of reservoirs, This indicates the number of sampling points in a reservoir.

[0102] Using the above method, the contribution weight of each sampling point in each reservoir can be obtained.

[0103] Next, the spontaneous potential, mud resistivity, and formation temperature are corrected using the contribution weight of each sampling point to obtain the target potential, target mud resistivity, and target temperature. Specifically, the spontaneous potential values ​​of all sampling points are weighted and summed using the contribution weight of each sampling point to obtain the target potential; the mud resistivity of all sampling points is weighted and summed using the contribution weight of each sampling point to obtain the target mud resistivity; and the formation temperature of all sampling points is weighted and averaged using the contribution weight of each sampling point to obtain the target temperature.

[0104] Step S4: Based on the target potential, target resistivity of the mud, and target temperature, determine the oil displacement efficiency of the oil well after water flooding; using the oil displacement efficiency and the air permeability of the oil well before and after water flooding, obtain the corrected permeability change rate before and after water flooding.

[0105] After obtaining the target potential, target mud resistivity, and target temperature, the formation water resistivity of the water-flooded well is obtained based on the spontaneous potential method and the target potential, target mud resistivity, and target temperature. The spontaneous potential method for calculating formation water resistivity is existing technology and will not be elaborated upon further here.

[0106] Furthermore, using Archie's formula, based on the formation water resistivity, original formation resistivity, and effective porosity of the water-flooded oil well, the water saturation of the well can be calculated as follows: ; in, This indicates the water saturation level of the oil well after flooding. Indicates the first coefficient. Indicates the cementation index, The saturation index The effective porosity of the oil well after flooding. This indicates the formation water resistivity of the oil well after flooding. This represents the resistivity of the original formation.

[0107] In this embodiment, the first coefficient is 1.1, the cementation index is 1, and the saturation index is 1.9.

[0108] Furthermore, the difference between the water saturation of the water-flooded oil well and the bound water saturation is recorded as the first difference, and the difference between the constant 1 and the bound water saturation is recorded as the second difference; the ratio between the first difference and the second difference is determined as the oil displacement efficiency of the water-flooded oil well.

[0109] In this embodiment, a specific formula for calculating the oil displacement efficiency of an oil well after water flooding is given. The oil displacement efficiency of an oil well after water flooding can be expressed as: ; in, This indicates the oil displacement efficiency of an oil well after it has been flooded. This indicates the water saturation level of the oil well after flooding. This indicates the degree of bound water saturation. Indicates the first difference. This represents the second difference.

[0110] After obtaining the oil displacement efficiency of the oil well after water flooding, the rate of change of air permeability is calculated based on the air permeability of the oil well before and after water flooding, and denoted as the initial rate of change. The initial rate of change of air permeability can be expressed as: ; in, This represents the initial rate of change of air permeability. This indicates the air permeability of the oil well after flooding. This indicates the air permeability of the oil well before it was flooded. This indicates the effective porosity of the oil well before flooding. This represents the effective porosity of the oil well after flooding, and e represents the natural constant.

[0111] Next, based on the initial change rate of air permeability and the oil displacement efficiency, the relationship between the oil displacement efficiency and the change rate of air permeability under different permeability levels and different displacement states is determined, and a reservoir permeability correction formula is established under different water flooding states.

[0112] For cases where the initial rate of change in air permeability is greater than 1000 mD and less than 300 mD, the relationship between oil displacement efficiency and permeability change is established. In this embodiment, when the initial rate of change in air permeability is greater than 1000 mD, a regression relationship is established using the least squares method with 23 sample points; when the initial rate of change in air permeability is less than 300 mD, a regression relationship is established using the least squares method with 15 sample points to obtain a permeability change rate correction model. The least squares method is an existing technology, and its specific process will not be described in detail. The permeability change rate correction chart is shown below. Figure 6 and Figure 7 As shown.

[0113] As a concrete example, the specific formula for calculating the corrected rate of change in permeability before and after flooding is given. The corrected rate of change in permeability before and after flooding can be expressed as: ; in, This represents the rate of change in permeability after correction before and after flooding. This indicates the air permeability after flooding. This indicates the oil displacement efficiency of an oil well after it has been flooded. This represents the primary coefficient for oil displacement efficiency. This represents the second coefficient for oil displacement efficiency. Denotes the first constant. This represents the second constant. The comprehensive results of the correction and interpretation of the rate of change in oil well permeability before and after water flooding are as follows: Figure 8 As shown.

[0114] By substituting multiple sets of permeability change rates and oil displacement efficiencies into the above formula for fitting, the functional relationship between the permeability change rate and the oil displacement efficiency can be determined, i.e., the result can be obtained. , , and In this embodiment, the value is given as the air permeability after flooding being greater than [value missing]. and less than The functional relationship in the two cases, in which: The value is , The value is , The value is , The value is For other cases, the above method can be used to determine the functional relationship between the rate of change of permeability and the oil displacement efficiency. Further details will not be elaborated here. Then, the air permeability is substituted into the functional relationship to obtain the corrected rate of change of permeability.

[0115] Thus, this embodiment compares and analyzes the permeability differences before and after water flooding based on the data from closed core sampling of sub-wells. By calculating the air permeability and oil displacement efficiency through core calibration logging, the relationship between changes in microscopic pore structure and macroscopic logging data is established, demonstrating the permeability change patterns before and after water flooding at different permeability levels, and achieving a quantitative interpretation of the permeability change effect of oil wells before and after water flooding.

[0116] This embodiment constructs a comprehensive invasion index by analyzing deep lateral resistivity and original formation resistivity to quantify the degree of mud invasion; and constructs an anomaly index by comparing the differences in parameter data from different sampling points to identify abnormal data fluctuations. The comprehensive invasion index and the anomaly index reflect the overall degree of mud invasion in the reservoir and the abnormal fluctuations in data from each sampling point, respectively. By correcting spontaneous potential, mud resistivity, and formation temperature, more reliable target potential, mud target resistivity, and target temperature are obtained. This suppresses data noise caused by invasion effects and heterogeneous distribution, overcomes the influence of mud invasion and heterogeneous fluids on measurement data, and further improves the accuracy of oil displacement efficiency based on these corrected parameters. This provides a reliable data foundation for permeability difference correction and improves the calculation accuracy of formation water resistivity and oil displacement efficiency. By integrating multiple types of monitoring data, and based on effectively correcting the effects of mud invasion and heterogeneity, a quantitative and accurate evaluation of permeability differences before and after water flooding is achieved, improving the accuracy and reliability of permeability change analysis in sandstone reservoirs during high water-cut periods.

[0117] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for correcting the permeability difference in sandstone reservoirs before and after water flooding, characterized in that, The method includes the following steps: Obtain parameter data, formation temperature and air permeability of different reservoirs in a pair of well groups before and after water flooding of sandstone reservoirs. The parameter data includes spontaneous potential, deep lateral resistivity, original formation resistivity and mud resistivity. Based on the magnitude of the deep lateral resistivity relative to the original formation resistivity at each sampling point in the reservoir, the comprehensive invasion index of each reservoir is obtained; based on the differences in parameter data at different sampling points, the anomaly index of each sampling point is obtained. By combining the comprehensive intrusion index and the anomaly index, the spontaneous potential, mud resistivity and formation temperature are corrected to obtain the target potential, target mud resistivity and target temperature. Based on the target potential, target resistivity of the mud, and target temperature, the oil displacement efficiency of the oil well after water flooding is determined; using the oil displacement efficiency and the air permeability of the oil well before and after water flooding, the corrected permeability change rate before and after water flooding is obtained. The process of obtaining the target potential, target resistivity of the mud, and target temperature includes: For any sampling point, the mud intrusion effect factor of any sampling point is obtained based on the comprehensive intrusion index of the reservoir where the sampling point is located and the anomaly index of the sampling point. Calculate the sum of the mud intrusion effect factors at all sampling points; The ratio between the mud intrusion effect factor of each sampling point and the summation is used as the contribution weight of each sampling point. The target potential is obtained by weighting the natural potential values ​​of all sampling points by using the contribution weight of each sampling point. The target resistivity of the mud is obtained by weighting the mud resistivity of all sampling points by using the contribution weight of each sampling point. By utilizing the contribution weight of each sampling point, the formation temperature of all sampling points is weighted and averaged to obtain the target temperature.

2. The method for correcting the permeability difference in sandstone reservoirs before and after water flooding according to claim 1, characterized in that, The comprehensive invasion index of each reservoir is obtained based on the value of the deep lateral resistivity of each sampling point relative to the original formation resistivity, including: The ratio of the deep lateral resistivity to the original formation resistivity at each sampling point in each reservoir is calculated and used as the invasion index for each sampling point in each reservoir. The average invasion index of all sampling points in each reservoir is used as the comprehensive invasion index of each reservoir.

3. The method for correcting the permeability difference in sandstone reservoirs before and after water flooding according to claim 1, characterized in that, The process of obtaining the anomaly index for each sampling point based on the differences in parameter data at different sampling points includes: Based on the difference in natural potential values ​​between each sampling point and its neighboring sampling points, and the degree of fluctuation in the natural potential values ​​of all sampling points in the reservoir where each sampling point is located, abnormal sampling points are screened. For any sampling point, the anomaly index of that sampling point is obtained based on the difference in parameter data between that sampling point and its neighboring sampling points and the consistency of the difference in parameter data between that sampling point and the abnormal sampling points.

4. The method for correcting the permeability difference in sandstone reservoirs before and after water flooding according to claim 3, characterized in that, The process of filtering out abnormal sampling points based on the difference in natural potential values ​​between each sampling point and its neighboring sampling points, and the fluctuation of natural potential values ​​of all sampling points in the reservoir where each sampling point is located, includes: For any sampling point, the local jump index of the potential value of any sampling point is obtained based on the difference in natural potential values ​​between the sampling point and its neighboring sampling points, and the standard deviation of the natural potential values ​​of all sampling points in the reservoir where the sampling point is located. The difference in natural potential values ​​between the sampling point and its neighboring sampling points is positively correlated with the local jump index, and the standard deviation is negatively correlated with the local jump index. The standard deviation is used to reflect the degree of fluctuation of the natural potential value. Based on the value of the local jump index of the potential value of any sampling point, determine whether any sampling point is an abnormal sampling point.

5. The method for correcting the permeability difference in sandstone reservoirs before and after water flooding according to claim 4, characterized in that, The step of determining whether any sampling point is an abnormal sampling point based on the value of the local jump index of the potential value of any sampling point includes: if the local jump index of the potential value of any sampling point is greater than a preset jump threshold, then the sampling point is determined to be an abnormal sampling point.

6. The method for correcting the permeability difference in sandstone reservoirs before and after water flooding according to claim 3, characterized in that, The process of obtaining an anomaly index for any sampling point based on the differences in parameter data between any sampling point and its neighboring sampling points, and the consistency of the differences in parameter data between any sampling point and anomalous sampling points, includes: Calculate the first DTW distance between the parameter data sequence of any sampling point and the parameter data sequence of each sampling point in its neighborhood, and the second DTW distance between the parameter data sequence of any sampling point and the parameter data sequence of each abnormal sampling point in the reservoir in which it is located; The ratio between the average of all first DTW distances corresponding to any sampling point and the average of all second DTW distances corresponding to any sampling point is used as the anomaly index of any sampling point. The parameter data sequence is composed of parameter data.

7. The method for correcting the permeability difference in sandstone reservoirs before and after water flooding according to claim 1, characterized in that, The determination of the oil displacement efficiency of a water-flooded well based on target potential, target mud resistivity, and target temperature includes: Based on the spontaneous potential method, target potential, target resistivity of mud, and target temperature, the formation water resistivity of the oil well after water flooding is obtained. The water saturation of the oil well after water flooding is calculated based on the formation water resistivity, original formation resistivity, and effective porosity. The oil displacement efficiency of the water-flooded oil well is determined based on the water saturation of the well after water flooding.

8. The method for correcting the permeability difference in sandstone reservoirs before and after water flooding according to claim 7, characterized in that, Determining the oil displacement efficiency of a water-flooded oil well based on its water saturation after water flooding includes: Calculate the first difference between the water saturation and the bound water saturation of the oil well after water flooding, and the second difference between constant 1 and the bound water saturation; The ratio between the first difference and the second difference is determined as the oil displacement efficiency of the oil well after water flooding.

9. The method for correcting the permeability difference in sandstone reservoirs before and after water flooding according to claim 1, characterized in that, The method of obtaining the corrected permeability change rate before and after water flooding by utilizing the oil displacement efficiency and the air permeability of the oil well before and after water flooding includes: Calculate the initial rate of change of air permeability based on multiple air permeability values ​​of the oil wells before and after flooding; The function relationship between the initial rate of change of air permeability and the oil displacement efficiency is fitted. The oil displacement efficiency of the well after water flooding is then substituted into the function relationship to obtain the corrected rate of change of permeability before and after water flooding.

10. The method for correcting the permeability difference in sandstone reservoirs before and after water flooding according to claim 1, characterized in that, Obtaining the air permeability of an oil well includes: The effective porosity of oil wells is obtained based on acoustic and density data. Based on the effective porosity of the oil well before and after water flooding, the air permeability of the oil well before and after water flooding is obtained.

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