Method for improving evaluation accuracy of heterogeneity of three types of oil layers

Abstract

The invention relates to the technical field of oil field data analysis, in particular to a method for improving the accuracy of heterogeneity evaluation of three types of oil reservoirs, and the method comprises the steps: obtaining various logging interpretation data of each reservoir in the three types of oil reservoirs under each oil well at the current moment and logging curves of various sedimentary facies under each reservoir; the deviation index of the well logging interpretation data is quantified by comprehensively analyzing the isolation degree of any kind of well logging interpretation data of each reservoir under each oil well in space and the difference degree of sedimentary facies; based on the deviation index, well logging interpretation data of each reservoir under each oil well is cleaned so as to be used for evaluating the heterogeneity of each oil well at the current moment. According to the method, the logging error and the real geological mutation are distinguished, the problem that traditional evaluation is prone to distortion is solved, and the accuracy of oil layer heterogeneity evaluation is improved.

Description

Technical Field

[0001] This application relates to the field of oilfield data analysis technology, specifically to a method for improving the accuracy of heterogeneity evaluation of three types of oil reservoirs. Background Technology

[0002] Type III oil-bearing reservoirs are crucial for increasing oilfield reserves and tapping potential. Their numerous layers and thin thickness result in extremely high heterogeneity, making accurate evaluation a prerequisite for efficient development. However, conventional evaluation methods directly use all logging interpretation parameters, making them highly susceptible to interference from a few data points with high deviations. These anomalies, caused by geological mutations or interpretation errors, though few in number, can drastically amplify evaluation indicators such as the coefficient of variation, leading to the misclassification of reservoirs with "moderate" heterogeneity as "extremely strong." This misleads development decisions, resulting in unnecessary investment or missed opportunities for enhancement.

[0003] Due to the influence of geological structure and spatial relationships, well logging interpretation data of the three types of oil reservoirs should ideally exhibit a continuous and gradual trend. However, well logging errors or abrupt changes in sedimentary facies can produce high-deviation data points that disrupt this geological continuity. If these outliers are removed solely based on numerical distribution, it is highly likely that valid data reflecting true geological changes will be mistakenly deleted as well, leading to distorted heterogeneity assessments and reducing the accuracy of oil reservoir heterogeneity evaluations. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a method for improving the accuracy of heterogeneity evaluation in three types of oil reservoirs, thereby resolving the existing issues.

[0005] The method for improving the accuracy of heterogeneity evaluation of Class III oil reservoirs in this application adopts the following technical solution:

[0006] One embodiment of this application provides a method for improving the accuracy of heterogeneity evaluation for three types of oil reservoirs, the method comprising the following steps:

[0007] Obtain multiple logging interpretation data and logging curves of multiple sedimentary facies in each of the three types of oil layers in each well at the current time.

[0008] Based on the distances from each oil well and its reservoir to all other oil wells and all reservoirs within them, identify all the reference reservoirs under each oil well; based on the distances from each reservoir under each oil well to its reference reservoirs, and any logging interpretation data of each reference reservoir, determine the estimated value of any logging interpretation data of each reservoir under each oil well; based on the difference between any logging interpretation data of each reservoir under each oil well and its estimated value, determine the spatial residual characteristic value of any logging interpretation data of each reservoir under each oil well.

[0009] Based on the differences between each reservoir under each oil well and its corresponding comparison reservoirs on various sedimentary facies logging curves, a first sedimentary characteristic value is determined for each reservoir under each oil well. Based on the differences between all comparison reservoirs under each oil well on various sedimentary facies logging curves, a second sedimentary characteristic value is determined for each reservoir under each oil well. Combined with the first sedimentary characteristic value, a sedimentary facies difference value for each reservoir under each oil well is determined. Based on the spatial residual characteristic value and the sedimentary facies difference value, a deviation index for any type of logging interpretation data for each reservoir under each oil well is determined.

[0010] Based on the deviation index, the logging interpretation data of each reservoir under each oil well are cleaned to evaluate the heterogeneity of each oil well at the current moment.

[0011] Preferably, the various well logging interpretation data include: sandstone thickness, effective thickness, permeability, porosity, and oil saturation.

[0012] Preferably, the method for determining all comparative reservoirs under each oil well is as follows:

[0013] Among the distances from each oil well to all other oil wells, oil wells with distance values ​​less than a preset distance threshold are considered as neighboring oil wells of each oil well.

[0014] Among all reservoirs in each neighboring well of each oil well, the reservoirs whose vertical depth difference from each reservoir under each oil well does not exceed a preset depth are used as the comparison reservoirs of each reservoir. By traversing all neighboring wells of each oil well, all comparison reservoirs under each oil well are obtained.

[0015] Preferably, the estimated value of any logging interpretation data for each reservoir under each oil well is negatively correlated with the distance from each reservoir under each oil well to its respective comparison reservoir, and positively correlated with any logging interpretation data of the comparison reservoir.

[0016] Preferably, the spatial residual characteristic value of any logging interpretation data for each reservoir under each oil well is the normalized result of the absolute difference between any logging interpretation data for each reservoir under each oil well and its estimated value.

[0017] Preferably, the method for determining the first sedimentary characteristic value of each reservoir under each oil well is as follows:

[0018] The differences between each reservoir under each oil well and its corresponding comparative reservoir on various sedimentary facies logging curves are recorded as the difference values ​​between each reservoir under each oil well and its corresponding comparative reservoir on various sedimentary facies.

[0019] Calculate the sum of the differences between each reservoir under each oil well and its corresponding reference reservoir across all sedimentary facies, and record this as the sedimentary difference between each reservoir under each oil well and its corresponding reference reservoir.

[0020] The average of the sedimentary differences between each reservoir under each oil well and all its correlated reservoirs is used as the first sedimentary characteristic value of each reservoir under each oil well.

[0021] Preferably, the method for determining the second sedimentary characteristic value of each reservoir under each oil well is as follows:

[0022] Calculate the sum of the differences of all comparative reservoirs under each oil well on various sedimentary facies logging curves, and record it as the sedimentary difference sum of each reservoir under each oil well on various sedimentary facies;

[0023] The average of the depositional differences and values ​​of each reservoir under each oil well across all sedimentary facies is used as the second depositional characteristic value of each reservoir under each oil well.

[0024] Preferably, the sedimentary facies difference value of each reservoir under each oil well is positively correlated with the first sedimentary characteristic value of each reservoir under each oil well and negatively correlated with the second sedimentary characteristic value.

[0025] Preferably, the deviation index of any logging interpretation data for each reservoir under each oil well is the result of the normalized value of the spatial residual characteristic value of any logging interpretation data for each reservoir under each oil well being divided by the normalized value of the upper sedimentary facies difference value.

[0026] Preferably, the step of cleaning the logging interpretation data of each reservoir under each oil well includes:

[0027] At the current moment, based on the average distribution and dispersion of the deviation index of any logging interpretation data of each reservoir under all oil wells, the threshold of the deviation index of any logging interpretation data of each reservoir at the current moment is determined;

[0028] If, at the current moment, the deviation index of the qth logging interpretation data of reservoir p under well h is greater than the deviation index threshold, then the qth logging interpretation data of reservoir p under well h is discarded. Then, all wells are traversed to obtain the logging interpretation data of the remaining wells for well heterogeneity evaluation.

[0029] This application has at least the following beneficial effects:

[0030] This application first constructs an estimate reflecting the geological continuity trend for each reservoir using spatial distance weighting. Then, it quantifies the spatial residual characteristic value by utilizing the deviation between actual data and this estimate. This method can effectively identify isolated anomalous data that disrupts geological continuity within a local neighborhood, laying the foundation for subsequent intelligent differentiation between well logging interpretation errors and genuine geological mutations, and accurate evaluation of reservoir heterogeneity. Furthermore, this application introduces sedimentary facies difference values ​​as a criterion for geological genesis, constructing a deviation index that integrates spatial isolation and geological rationality. This enables the identification of "suspicious errors" and "credible mutations" in well logging interpretation data, effectively overcoming the shortcomings of traditional methods that easily misjudge real geological information as interference, providing a reliable data foundation for accurate evaluation of reservoir heterogeneity. Finally, this application sets dynamic thresholds and performs intelligent cleaning of well logging data based on the deviation index, effectively eliminating outliers that interfere with heterogeneity evaluation and improving the accuracy of reservoir heterogeneity evaluation. Attached Figure Description

[0031] To more clearly illustrate the technical solutions and advantages in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 A flowchart illustrating the steps of a method for improving the accuracy of heterogeneity evaluation of three types of oil reservoirs, provided in one embodiment of this application;

[0033] Figure 2 This is a schematic diagram of the deviation index extraction process provided in one embodiment of this application. Detailed Implementation

[0034] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive objective, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for improving the accuracy of heterogeneity evaluation of three types of oil reservoirs proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0035] 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 application pertains.

[0036] The following description, in conjunction with the accompanying drawings, details a specific scheme for a method to improve the accuracy of heterogeneity evaluation of three types of oil reservoirs provided in this application.

[0037] This application provides a method for improving the accuracy of heterogeneity assessment in three types of oil reservoirs, specifically, the method described below. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps:

[0038] Step S1: Obtain multiple logging interpretation data of each reservoir in the three types of oil layers under each oil well at the current time, as well as logging curves of multiple sedimentary facies under each reservoir.

[0039] The process involves acquiring logging data, geological interpretation results, and comprehensive data from all wells in the target oilfield test area. All logging data, geological interpretation results, and comprehensive data are analytical data generated during the oilfield development process. The logging data includes information reflecting formation lithology and pore structure, such as conventional logging data like density, neutron porosity, sonic transit time, resistivity, and well diameter. The geological interpretation results include lithology identification, sandstone-mudstone interface analysis, and sedimentary facies analysis results, enabling the division of different strata into individual layers.

[0040] Furthermore, based on the geological interpretation data, the three target oil layers are finely divided into layers, which are then subdivided into single layers. Multiple logging interpretation data are extracted for each reservoir. These multiple logging interpretation data include, but are not limited to, sandstone thickness, effective thickness, permeability, porosity, and oil saturation. The method for extracting logging interpretation data from oil well reservoirs is a well-known technology in the oilfield field, and the specific extraction process will not be described in detail.

[0041] The types of sedimentary facies are distinguished by well logging curves in well logging data, such as sonic transit time, spontaneous potential, and natural gamma ray. These well logging data can reflect the data characteristics of sedimentary facies and thus determine the differences between different sedimentary facies types. Therefore, well logging curves of multiple sedimentary facies in each reservoir under each oil well are obtained through well logging data. In this embodiment, the well logging curves of multiple sedimentary facies include sonic transit time, spontaneous potential, and natural gamma ray.

[0042] This involves ensuring that the same logging curves for different reservoirs are synchronized in time and consistent in length.

[0043] Step S2: Quantify the deviation index of well logging interpretation data by comprehensively analyzing the degree of spatial isolation and sedimentary facies differences of any type of well logging interpretation data in each reservoir under each oil well.

[0044] Due to the large number of layers, thin thickness, and significant spatial differences in the three types of oil reservoirs, their well logging interpretation data, such as permeability and porosity, inherently possess heterogeneity. However, traditional outlier identification methods fail to fully consider the spatial correlation of well logging interpretation data and the geological differences between layers, resulting in low identification accuracy. Data points that can truly reflect reservoir geological characteristics, such as abrupt changes in sedimentary facies, are often misjudged as high deviation values ​​and rejected. This misjudgment undermines the authenticity of the data and seriously affects the accuracy of heterogeneity assessment.

[0045] Therefore, based on the above analysis, this embodiment quantifies the deviation index of well logging interpretation data by comprehensively analyzing the spatial isolation and sedimentary facies differences of any type of well logging interpretation data in each reservoir under each oil well. This reduces the impact of interfering well logging interpretation data on the evaluation of oil well heterogeneity, thereby improving the accuracy of the evaluation of heterogeneity of the three types of oil reservoirs. The specific process is as follows:

[0046] S2.1: Based on the distances from each oil well and its reservoir to all other oil wells and all reservoirs within them, determine all the reference reservoirs under each oil well; based on the distances from each reservoir under each oil well to its reference reservoirs, and any logging interpretation data of each reference reservoir, determine the estimated value of any logging interpretation data of each reservoir under each oil well; based on the difference between any logging interpretation data of each reservoir under each oil well and its estimated value, determine the spatial residual characteristic value of any logging interpretation data of each reservoir under each oil well.

[0047] In the three types of oil reservoirs, well logging interpretation data should ideally exhibit a continuous and gradual trend in geological space. However, some well logging interpretation data, while not the highest value globally, deviate significantly from the distribution pattern of surrounding data in their local neighborhoods, disrupting the continuity of geological space. These spatially isolated local anomalies may be due to well logging interpretation errors or may reflect actual small-scale geological abrupt changes. Therefore, to quantify the degree of this spatial isolation, this embodiment determines all reference reservoirs under each well based on the distances from each well and its reservoir to all other wells and all reservoirs within them; based on the distances from each reservoir under each well to its reference reservoirs, and any well logging interpretation data from each reference reservoir, it determines the estimated value of any well logging interpretation data for each reservoir under each well; based on the difference between any well logging interpretation data for each reservoir under each well and its estimated value, it determines the spatial residual characteristic value of any well logging interpretation data for each reservoir under each well, specifically:

[0048] In this embodiment, firstly, based on the distances from each oil well and its reservoir to all other oil wells and all reservoirs within them, all corresponding reservoirs beneath each oil well are determined. Specifically:

[0049] Among the distances from each oil well to all other oil wells, oil wells with distance values ​​less than a preset distance threshold are considered as neighboring oil wells of each oil well.

[0050] Among all reservoirs in each neighboring well of each oil well, the reservoirs whose vertical depth difference from each reservoir under each oil well does not exceed a preset depth are used as the comparison reservoirs of each reservoir. By traversing all neighboring wells of each oil well, all comparison reservoirs under each oil well are obtained.

[0051] It should be noted that the preset distance threshold and preset depth values ​​are set manually, and the unit for both the preset distance threshold and preset depth is meters (m). The range of the preset distance threshold is usually within... The preset depth value is usually in the range of [3, 10]. In this embodiment, the preset distance threshold is 1000m and the preset depth is 6m. In actual application, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0052] Furthermore, this embodiment determines the estimated value of any logging interpretation data for each reservoir under each oil well based on the distance from each reservoir to its corresponding comparison reservoir and any logging interpretation data for each comparison reservoir. Specifically:

[0053] In this embodiment, the estimated value of any logging interpretation data for each reservoir under each oil well is negatively correlated with the distance from each reservoir under each oil well to its corresponding comparison reservoir, and positively correlated with any logging interpretation data for each comparison reservoir.

[0054] It should be understood that a positive correlation means that the dependent variable increases as the independent variable increases, and the dependent variable decreases as the independent variable decreases. The specific relationship can be additive or multiplicative, etc., and is determined by the actual application. This application does not impose any special restrictions. A negative correlation means that the dependent variable decreases as the independent variable increases, and the dependent variable increases as the independent variable decreases. The relationship can be subtractive or divisive, etc., and is determined by the actual application.

[0055] Preferably, as one implementation method, in this embodiment, the expression for the estimated value of any type of logging interpretation data for each reservoir under each oil well is: In the formula, Represents the reservoir of the nth oil well. The estimated value of the i-th type of well logging interpretation data; Represents the reservoir of the nth oil well. The distance to its corresponding reservoir k; Represents the reservoir of the nth oil well. The sum of distances to all its correlated reservoirs; Represents the reservoir of the nth oil well. The i-th logging interpretation data of the comparative reservoir k; Represents the reservoir of the nth oil well. The number of all contrasted reservoirs.

[0056] Based on the estimated values ​​of any logging interpretation data for each reservoir under each oil well, it can be understood that the estimated values ​​reflect the expected values ​​of the logging interpretation data, are used to characterize the geological continuity trend of the reservoir in its neighborhood space, and reflect the numerical level that the logging interpretation data should occupy in space; if the reservoir under the nth oil well... The distance to its corresponding reservoir k is the distance to the reservoir in the nth well. The larger the proportion of the sum of distances to all its compared reservoirs, the stronger the reservoir. The greater the distance between reservoir k and the i-th logging interpretation data, the more valuable the i-th logging interpretation data is to the reservoir. The smaller the contribution of the i-th type of well logging interpretation data estimate;

[0057] Conversely, if the nth oil well goes into the reservoir The distance to its corresponding reservoir k is the distance to the reservoir in the nth well. The smaller the proportion of the sum of distances to all its compared reservoirs, the better the reservoir. The closer the i-th logging interpretation data is to reservoir k, the better the interpretation of the i-th logging data for the reservoir. The greater the reference significance of the estimated value of the i-th type of well logging interpretation data.

[0058] Furthermore, this embodiment determines the spatial residual characteristic value of any logging interpretation data for each reservoir under each oil well based on the difference between the data and the estimated value. Specifically:

[0059] The spatial residual characteristic value of any logging interpretation data for each reservoir under each oil well is the normalized result of the absolute difference between any logging interpretation data for each reservoir under each oil well and its estimated value.

[0060] It should be further explained that the normalization method here is as follows: calculate the standard deviation of each reservoir and all its comparative reservoirs under each oil well in any logging interpretation data, calculate the sum of the standard deviation and the preset value, and divide the spatial residual characteristic value by the sum as the normalized result of the absolute difference. In practical applications, as other implementation methods, implementers may also use other normalization methods such as the maximum-minimum value normalization method or the z-score standardization method according to the specific situation. This embodiment does not impose any special restrictions on the selection of normalization methods.

[0061] The preset value is set manually to prevent the denominator from being 0. In this embodiment, the preset value is 0.01. In actual applications, as other implementation methods, the implementer can also set it according to the specific situation, provided that the denominator is not 0 and does not excessively affect the calculation result. This embodiment does not impose any special restrictions.

[0062] Based on the spatial residual characteristic value of any type of well logging interpretation data under each oil well, it can be understood that the spatial residual characteristic value is used to characterize the relative degree of deviation of the actual well logging interpretation data of the reservoir from its estimated value. If the spatial residual characteristic value of the current type of well logging interpretation data under the current reservoir is larger, it indicates that the current type of well logging interpretation data is more isolated in space, indicating that the current well logging interpretation data may be anomalies caused by non-geological factors such as well logging interpretation errors. Their existence will seriously interfere with the subsequent heterogeneity evaluation.

[0063] Conversely, if the spatial residual characteristic value of the current type of well logging interpretation data under the current reservoir is smaller, it indicates that the current well logging interpretation data matches the predicted value of its neighborhood and is integrated into the continuous trend of local geological space. This suggests that the current well logging interpretation data is relatively reliable and can truly reflect the inherent properties of the reservoir.

[0064] Thus, this embodiment first constructs an estimate reflecting the trend of geological continuity for each reservoir by weighting the spatial distance. Then, it quantifies the spatial residual characteristic value by using the deviation between the actual data and the estimate. This method can effectively identify isolated anomalous data that disrupts geological continuity in the local neighborhood, laying the foundation for subsequent intelligent differentiation between well logging interpretation errors and real geological abrupt changes, and accurate evaluation of reservoir heterogeneity.

[0065] S2.2: Based on the differences between each reservoir under each oil well and its corresponding comparative reservoirs on various sedimentary facies logging curves, determine the first sedimentary characteristic value of each reservoir under each oil well; based on the differences between all comparative reservoirs under each oil well on various sedimentary facies logging curves, determine the second sedimentary characteristic value of each reservoir under each oil well, and combine it with the first sedimentary characteristic value to determine the sedimentary facies difference value of each reservoir under each oil well; based on the spatial residual characteristic value and the sedimentary facies difference value, determine the deviation index of any logging interpretation data for each reservoir under each oil well.

[0066] However, relying solely on spatial residual eigenvalues ​​to determine data deviation has a significant drawback: spatial residual eigenvalues ​​cannot distinguish between logging errors and actual geological abrupt changes. Geologically, reservoirs in similar spatial locations may exhibit different sedimentary facies due to drastic changes in the sedimentary environment, which in itself can lead to substantial differences in parameters such as permeability.

[0067] Therefore, this embodiment determines the first sedimentary characteristic value of each reservoir under each oil well based on the differences between each reservoir and its corresponding comparison reservoirs on various sedimentary facies logging curves; it also determines the second sedimentary characteristic value of each reservoir under each oil well based on the differences between all comparison reservoirs under each oil well on various sedimentary facies logging curves, and combines this with the first sedimentary characteristic value to determine the sedimentary facies difference value of each reservoir under each oil well; based on the spatial residual characteristic value and the sedimentary facies difference value, it determines the deviation index of any logging interpretation data for each reservoir under each oil well, supplementing the spatial residual characteristic value. By judging whether a high deviation point is accompanied by a sudden change in sedimentary facies, it is possible to distinguish whether it is a logging interpretation data error or a real geological discontinuity, thereby avoiding the geological information being misjudged as interference data and discarded. The specific process is as follows:

[0068] First, this embodiment determines the first sedimentary characteristic value of each reservoir under each oil well based on the differences between each reservoir and its corresponding comparison reservoir on various sedimentary facies logging curves. Specifically:

[0069] The differences between each reservoir under each oil well and its corresponding comparative reservoir on various sedimentary facies logging curves are recorded as the difference values ​​between each reservoir under each oil well and its corresponding comparative reservoir on various sedimentary facies.

[0070] Furthermore, the sum of the differences between each reservoir under each oil well and its corresponding comparative reservoir across all sedimentary facies is calculated and recorded as the sedimentary difference between each reservoir under each oil well and its corresponding comparative reservoir.

[0071] Furthermore, the average value of the sedimentary differences between each reservoir under each oil well and all its comparative reservoirs is used as the first sedimentary characteristic value of each reservoir under each oil well.

[0072] It should be noted that there are many methods to measure the differences between curves. In this embodiment, the DTW distance between each reservoir under each oil well and its corresponding comparison reservoir on various sedimentary facies logging curves is used as the difference between each reservoir under each oil well and its corresponding comparison reservoir on various sedimentary facies logging curves. In actual application, as other implementation methods, implementers may also use other methods such as Euclidean distance or Mahalanobis distance according to specific circumstances. This embodiment does not impose any special restrictions.

[0073] The method for calculating the DTW distance is a well-known technique, and the specific calculation process will not be described in detail here.

[0074] Based on the first sedimentary characteristic value of each reservoir under each oil well, it can be understood that the first sedimentary characteristic value is used to measure the difference in sedimentary facies characteristics between each reservoir and its comparative reservoirs. It is used to indicate the geological background heterogeneity of each reservoir in its local environment. If the current reservoir differs from its comparative reservoirs in various sedimentary facies logging curves, the larger the first sedimentary characteristic value of the current reservoir, the greater the difference in the logging curve morphology of the current reservoir, that is, the greater the difference between the sedimentary facies characteristic value and the average morphology of the comparative reservoir. This indicates that the current reservoir may have formed in a different sedimentary environment. For example, the comparative reservoir is floodplain mudstone, while the current reservoir is channel sandstone. This huge difference in geological background results in a sudden change in its physical properties, such as permeability. Conversely, if the first sedimentary characteristic value of the current reservoir is smaller, it indicates that its sedimentary facies characteristics are highly consistent with the average morphology of neighboring reservoirs, indicating that it was formed in a similar, homogeneous sedimentary environment.

[0075] Furthermore, this embodiment determines the second sedimentary characteristic value of each reservoir under each oil well based on the differences in logging curves of various sedimentary facies across all comparative reservoirs under each oil well. Specifically:

[0076] In this embodiment, the sum of the differences of all comparative reservoirs under each oil well on various sedimentary facies logging curves is calculated and recorded as the sedimentary difference sum of each reservoir under each oil well on various sedimentary facies.

[0077] The average of the depositional differences and values ​​of each reservoir under each oil well across all sedimentary facies is used as the second depositional characteristic value of each reservoir under each oil well.

[0078] It should be noted that in this embodiment, the DTW distance of all the comparative reservoirs under each reservoir in each oil well on the sedimentary facies logging curve is used as the difference of all the comparative reservoirs under each oil well on various sedimentary facies logging curves. In actual application, the implementer may also use other methods such as Euclidean distance or Mahalanobis distance to measure the difference between curves according to the specific situation. This embodiment does not impose any special restrictions.

[0079] Based on the second sedimentary characteristic value of each reservoir under each oil layer, it can be understood that the second sedimentary characteristic value is used to measure the average level of sedimentary facies characteristics differences among all the correlated reservoirs of each reservoir, and is used to characterize the homogeneity of the geological background of the neighborhood where each reservoir is located. If the DTW distance of the logging curves among all the correlated reservoirs of the current reservoir is larger, the second sedimentary characteristic value of the current reservoir is larger, indicating that the sedimentary facies differences within the neighborhood of the current reservoir are themselves large, indicating that the geological background is very complex, and the deviation of the logging interpretation data of the current reservoir is more likely to be caused by the complex geological background.

[0080] Conversely, if the second sedimentary characteristic value of the current reservoir is smaller, it indicates that the sedimentary facies characteristics within its neighborhood are highly consistent and the geological background is extremely homogeneous and stable. This means that the neighborhood itself is a geologically homogeneous body. In this context, if the second sedimentary characteristic value of the current reservoir is larger, it will become very significant, indicating that the deviation in the well logging interpretation data of the current reservoir is caused by errors in the well logging interpretation data.

[0081] Furthermore, this embodiment determines the sedimentary facies difference value of each reservoir under each oil well based on the first sedimentary characteristic value and the second sedimentary characteristic value, specifically:

[0082] In this embodiment, the sedimentary facies difference value of each reservoir under each oil well is positively correlated with the first sedimentary characteristic value of each reservoir under each oil well, and negatively correlated with the second sedimentary characteristic value.

[0083] It should be noted that in this embodiment, the first and second sedimentary characteristic values ​​are processed using maximum-minimum normalization, and both the first and second sedimentary characteristic values ​​are mapped to... Within this scope, in practical applications, as other implementation methods, implementers may also adopt other normalization methods such as z-score normalization in combination with specific circumstances. This embodiment does not impose special restrictions on the selection of normalization methods.

[0084] Among them, the maximum-minimum normalization method is a well-known technique, and the specific process of using it to normalize data will not be elaborated here.

[0085] Based on the sedimentary facies difference value of any logging interpretation data for each reservoir under each oil well, it can be understood that the sedimentary facies difference value is used as an indicator to assess the isolation of sedimentary facies in each reservoir, and is used to characterize the degree of abruptness of the sedimentary facies characteristics of each reservoir relative to its geological environment. If the first sedimentary characteristic value of the current reservoir is larger and the second sedimentary characteristic value is smaller, the sedimentary facies difference value is larger, indicating that the spatial isolation of the current reservoir is stronger, so that the logging interpretation data under the current reservoir is not misjudged as erroneous data.

[0086] Conversely, if the first sedimentary characteristic value of the current reservoir is smaller and the second sedimentary characteristic value is larger, the sedimentary facies difference value is smaller. This indicates that the current reservoir is not only highly similar to the sedimentary facies characteristics of the neighboring area, but also that the geological background of the neighboring area itself is very complex and chaotic. In this case, any spatial isolation of the current reservoir lacks special support in the geological background. Therefore, if the logging interpretation data under the current reservoir shows a high deviation, it is more likely to be judged as logging error.

[0087] Furthermore, this embodiment determines the deviation index of any logging interpretation data for each reservoir under each oil well based on the spatial residual characteristic value and the sedimentary facies difference value. Specifically:

[0088] In this embodiment, the result of the normalized value of the spatial residual characteristic value of any logging interpretation data of each reservoir under each oil well being divided by the normalized value of the upper sedimentary facies difference value is used as the deviation index of any logging interpretation data of each reservoir under each oil well.

[0089] Preferably, the schematic diagram of the deviation index extraction process provided in this embodiment is as follows: Figure 2 As shown.

[0090] Based on the deviation index of any type of logging interpretation data for each reservoir under each oil well, it can be understood that the deviation index is used to characterize whether the logging interpretation data is a suspicious error or a credible mutation. If the spatial residual characteristic value of the current type of logging interpretation data for the current reservoir is larger and the sedimentary facies difference value of the current reservoir is smaller, the corresponding deviation index is also larger, indicating that the current type of logging interpretation data is more likely to have an error, rather than being caused by a complex geological background.

[0091] Conversely, if the spatial residual characteristic value of the current type of well logging interpretation data of the current reservoir is larger, but its sedimentary facies difference value is also larger, the corresponding deviation index will be significantly reduced. This indicates that although the data point is spatially isolated, this isolation has a strong geological origin to support it. Therefore, it is judged as a "credible mutation" that can truly reflect the heterogeneity of the reservoir, and its value should be preserved to avoid losing key geological information due to simple rejection.

[0092] Thus, this embodiment constructs a deviation index that integrates spatial isolation and geological rationality by introducing sedimentary facies difference values ​​as a criterion for geological genesis. This enables the identification of "suspicious errors" and "credible mutations" in well logging interpretation data, effectively overcoming the defect of traditional methods that easily misjudge real geological information as interference, and providing a reliable data foundation for accurately evaluating reservoir heterogeneity.

[0093] Step S3: Based on the deviation index, clean the logging interpretation data of each reservoir under each oil well to evaluate the heterogeneity of each oil well at the current moment.

[0094] Based on the deviation index obtained in step S2, this embodiment further cleans the logging interpretation data of each reservoir under each oil well based on the deviation index. Specifically:

[0095] In this embodiment, firstly, based on the average distribution and dispersion of the deviation index of any type of well logging interpretation data for each reservoir across all oil wells, the deviation index threshold for any type of well logging interpretation data at the current moment is determined. Specifically:

[0096] At the current moment, the mean of the deviation index of any logging interpretation data for each reservoir under all oil wells is added to three times the standard deviation of the deviation index, and the result is used as the deviation index threshold of any logging interpretation data for each reservoir at the current moment.

[0097] If, at the current moment, the deviation index of the qth logging interpretation data of reservoir p under well h is greater than the deviation index threshold, then the qth logging interpretation data of reservoir p under well h is discarded. Then, all wells are traversed to obtain the logging interpretation data of the remaining wells for well heterogeneity evaluation.

[0098] The evaluation of oil well heterogeneity is a well-known technique and will not be elaborated further.

[0099] Thus, this embodiment effectively eliminates outliers that interfere with the evaluation of heterogeneity by setting dynamic thresholds and intelligently cleaning logging data based on deviation index, thereby improving the accuracy of reservoir heterogeneity evaluation.

[0100] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0101] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0102] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for improving the accuracy of the evaluation of the heterogeneity of Class 3 oil layers, characterized by, The method comprises the following steps: Obtaining a plurality of well logging interpretation data of each reservoir in three types of oil layers under each oil well at the current time and a plurality of well logging curves of various sedimentary facies under each reservoir; Based on the distance from each oil well and its reservoir to all the remaining oil wells and their reservoirs, all the comparative reservoirs of each reservoir under each oil well are determined; based on the distance from each reservoir under each oil well to its comparative reservoirs and any well logging interpretation data of the comparative reservoirs, the estimated value of any well logging interpretation data of each reservoir under each oil well is determined; based on the difference between any well logging interpretation data of each reservoir under each oil well and its estimated value, the spatial residual characteristic value of any well logging interpretation data of each reservoir under each oil well is determined; Based on the difference between each reservoir under each oil well and its comparative reservoirs in various sedimentary facies well logging curves, the first sedimentary characteristic value of each reservoir under each oil well is determined; based on the difference between all the comparative reservoirs of each reservoir under each oil well in various sedimentary facies well logging curves, the second sedimentary characteristic value of each reservoir under each oil well is determined, and the sedimentary facies difference value of each reservoir under each oil well is determined in combination with the first sedimentary characteristic value; based on the spatial residual characteristic value and the sedimentary facies difference value, the deviation index of any well logging interpretation data of each reservoir under each oil well is determined; Based on the deviation index, the well logging interpretation data of each reservoir under each oil well is cleaned for evaluating the heterogeneity of each oil well at the current time.

2. The method for improving the accuracy of evaluating the heterogeneity of the Class 3 oil reservoir according to claim 1, wherein, The plurality of well logging interpretation data includes sandstone thickness, effective thickness, permeability, porosity, and oil saturation.

3. The method for improving the accuracy of evaluating the heterogeneity of the Class 3 oil reservoir according to claim 1, wherein, The determination method of all the comparative reservoirs of each reservoir under each oil well is as follows: In the distance from each oil well to all the remaining oil wells, the oil wells with a distance value less than a preset distance threshold value are taken as the neighborhood oil wells of each oil well; In all the reservoirs of each neighborhood oil well of each oil well, the reservoirs with a longitudinal depth difference of no more than a preset depth from each reservoir under each oil well are taken as the comparative reservoirs of each reservoir, and all the neighborhood oil wells of each oil well are traversed to obtain all the comparative reservoirs of each reservoir under each oil well.

4. The method for improving the accuracy of evaluating the heterogeneity of the Class 3 oil reservoir according to Claim 1, wherein, The estimated value of any well logging interpretation data of each reservoir under each oil well is negatively correlated with the distance from each reservoir under each oil well to its comparative reservoirs and positively correlated with any well logging interpretation data of the comparative reservoirs.

5. The method for improving the accuracy of evaluating the heterogeneity of the Class 3 oil reservoir according to claim 1, wherein, The spatial residual characteristic value of any well logging interpretation data of each reservoir under each oil well is the normalized result of the absolute difference between any well logging interpretation data of each reservoir under each oil well and its estimated value.

6. The method for improving the accuracy of evaluating the heterogeneity of the Class 3 oil reservoir according to Claim 1, wherein, The determination method of the first sedimentary characteristic value of each reservoir under each oil well is as follows: The difference between each reservoir under each oil well and its comparative reservoirs in various sedimentary facies well logging curves is recorded as the difference value between each reservoir under each oil well and its comparative reservoirs in various sedimentary facies; The sum of the difference values between each reservoir under each oil well and its comparative reservoirs in all sedimentary facies is calculated and recorded as the sedimentary difference value between each reservoir under each oil well and its comparative reservoirs; The mean value of the sedimentary difference between each reservoir under each oil well and all its comparative reservoirs is taken as the first sedimentary characteristic value of each reservoir under each oil well.

7. The method for improving the accuracy of evaluating the heterogeneity of the Class 3 oil reservoir according to claim 1, wherein, The determination method of the second sedimentary characteristic value of each reservoir under each oil well is as follows: The sum value of the difference of each reservoir under each oil well in various sedimentary facies logging curves is calculated, and is recorded as the sedimentary difference sum value of each reservoir under each oil well in various sedimentary facies; The average value of the sedimentary difference sum value of each reservoir under each oil well in all sedimentary facies is taken as the second sedimentary characteristic value of each reservoir under each oil well.

8. The method for improving the accuracy of evaluating the heterogeneity of the Class 3 oil reservoir according to claim 1, wherein, The sedimentary facies difference value of each reservoir under each oil well is positively correlated with the first sedimentary characteristic value of each reservoir under each oil well, and is negatively correlated with the second sedimentary characteristic value.

9. The method for improving the accuracy of evaluating the heterogeneity of the Class 3 oil reservoir according to Claim 1, wherein, The deviation index of each kind of logging interpretation data of each reservoir under each oil well is the normalized value of the spatial residual characteristic value of each kind of logging interpretation data of each reservoir under each oil well divided by the normalized value of the sedimentary facies difference value.

10. The method for improving the accuracy of evaluating the heterogeneity of the Class 3 oil reservoir according to Claim 1, wherein, The cleaning of the logging interpretation data of each reservoir under each oil well comprises: At the current time, the deviation index threshold of each kind of logging interpretation data of each reservoir is determined based on the average distribution and the dispersion degree of the deviation index of each kind of logging interpretation data of each reservoir under all oil wells; If the deviation index of the qth kind of logging interpretation data of reservoir p under oil well h at the current time is greater than the deviation index threshold, the qth kind of logging interpretation data of reservoir p under oil well h is removed, and the logging interpretation data of the remaining oil wells is obtained through traversing all oil wells, and is used for oil well heterogeneity evaluation.

Images