Single well seepage field characterization method and device based on hierarchical fuzzy comprehensive evaluation

Abstract

The invention relates to a single well seepage field characterization method and device based on hierarchical fuzzy comprehensive evaluation. The method comprises the steps that multiple potential influence factors of seepage field formation and distribution of a target well are obtained; performing multi-factor grading fuzzy comprehensive evaluation processing according to the influence degree of the plurality of potential influence factors on the seepage field intensity and the relationship among the factors to obtain multi-level evaluation factors and weights corresponding to the evaluation factors; determining a weight matrix according to the weight corresponding to each level of evaluation factor; determining a membership matrix according to the value of each measurement point in the target well corresponding to the multi-level evaluation factor; and calculating according to the membership matrix and the weight matrix to obtain the seepage field characterization intensity of the target well. Through hierarchical fuzzy comprehensive evaluation, numerous evaluation factors are divided into multiple levels, so that the weight matrix and the membership matrix corresponding to each evaluation factor can be more accurately determined, and the seepage field characterization intensity of the target well can be more accurately determined.

Description

Technical Field

[0001] This disclosure relates to the field of oil and gas development technology, and in particular to a method and apparatus for characterizing the seepage field of a single well based on hierarchical fuzzy comprehensive evaluation. Background Technology

[0002] In the development of oil and gas, the analysis and research of working areas such as oil reservoir seepage field and gas reservoir seepage field play a positive role in understanding the geological environment, predicting oil and gas reserves, determining the extraction location and development technology, and improving oil and gas recovery rate.

[0003] In realizing the present invention, the inventors discovered at least the following technical problems in the related technology: the distribution of the seepage field is affected by many factors. The current seepage field characterization method considers and analyzes multiple factors according to the same level, which results in a very small influence weight for each factor, and the final seepage field characterization field strength deviates greatly from the actual situation. Summary of the Invention

[0004] To solve the above-mentioned technical problems, or at least partially solve them, embodiments of this disclosure provide a method and apparatus for characterizing the seepage field of a single well based on hierarchical fuzzy comprehensive evaluation.

[0005] In a first aspect, embodiments of this disclosure provide a method for characterizing the seepage field of a single well based on multi-factor hierarchical fuzzy comprehensive evaluation. The method includes: acquiring multiple potential influencing factors of the formation and distribution of the seepage field in the target well; performing multi-factor hierarchical fuzzy comprehensive evaluation based on the degree of influence of the multiple potential influencing factors on the seepage field intensity and the relationship between the factors, to obtain multi-level evaluation factors and the weights corresponding to each level of evaluation factors; determining a weight matrix based on the weights corresponding to each level of evaluation factors; determining a membership matrix based on the values ​​of each measurement point in the target well corresponding to the multi-level evaluation factors; and calculating the seepage field characterization intensity of the target well based on the membership matrix and the weight matrix.

[0006] In some embodiments, the multi-level evaluation factors include: primary category evaluation factors and at least one level of more refined category evaluation factors. Determining the weight matrix based on the weights corresponding to each level of evaluation factors includes: multiplying the corresponding weights according to the hierarchical relationship between the evaluation factors at each level to obtain the weights corresponding to each most refined category evaluation factor; and determining the weights corresponding to each most refined category evaluation factor as elements of the weight matrix; wherein the weight matrix has a dimension of m rows × 1 column, where m represents the total number of most refined category evaluation factors, and m ≥ 2.

[0007] In some embodiments, the aforementioned multi-level evaluation factors include: a primary category evaluation factor and at least one level of more refined category evaluation factors. Determining the membership matrix based on the values ​​of each measurement point in the target well corresponding to the aforementioned multi-level evaluation factors includes: obtaining the actual measurement values ​​of each measurement point in the target well corresponding to each most refined category evaluation factor; normalizing the actual measurement values ​​of all measurement points under the same most refined category evaluation factor to obtain the normalized values ​​of each measurement point corresponding to each most refined category evaluation factor; and determining the normalized values ​​of each measurement point corresponding to each most refined category evaluation factor as elements of the membership matrix; wherein the membership matrix has a dimension of n rows × m columns; where n represents the total number of measurement points, n≥2; and m represents the total number of most refined category evaluation factors, m≥2.

[0008] In some embodiments, the true measured values ​​of all measurement points under the same most refined category evaluation factor are normalized to obtain the normalized value of each measurement point corresponding to each most refined category evaluation factor. This includes: for the same most refined category evaluation factor, determining the maximum and minimum values ​​among the true measured values ​​of all measurement points, and calculating the range between the maximum and minimum values; for each measurement point, calculating the difference between the true measured value of the current measurement point corresponding to the current most refined category evaluation factor and the minimum or maximum value, and calculating the ratio between the above difference and the corresponding range, where the ratio is the normalized value of the current measurement point corresponding to the current most refined category evaluation factor.

[0009] In some embodiments, the intensity of the seepage field characterization of the target well is obtained by calculating based on the membership matrix and the weight matrix, including: performing matrix multiplication of the membership matrix and the weight matrix to obtain the seepage field intensity corresponding to each measurement point; and performing interpolation based on the seepage field intensity corresponding to each measurement point in the target well to obtain the intensity of the seepage field characterization in the work area corresponding to the target well.

[0010] In some embodiments, interpolation is performed based on radial basis functions. The aforementioned multi-level evaluation factors include: main category evaluation factors and at least one level of more refined category evaluation factors; the membership matrix has an n-row × m-column dimension, and the weight matrix has an m-row × 1-column dimension; where n represents the total number of measurement points, n≥2; m represents the total number of most refined category evaluation factors, m≥2; the membership matrix and the weight matrix are multiplied to obtain the seepage field strength corresponding to the n measurement points.

[0011] In some embodiments, the above-mentioned multi-level evaluation factors are two-level evaluation factors, wherein the main categories of evaluation factors corresponding to the first-level evaluation factors are: static factors and dynamic factors. The sub-levels of static factors correspond to at least one of the following refined categories of evaluation factors: gradient, permeability, sand body thickness, porosity, flow coefficient, initial oil saturation, number of interconnected wells, and sedimentary microfacies. The sub-levels of dynamic factors correspond to at least one of the following refined categories of evaluation factors: cumulative water injection intensity, relative water absorption, cumulative water injection intensity, relative production intensity, effectiveness, injection-production well spacing, oil saturation, water cut, and cumulative water-oil ratio.

[0012] Secondly, embodiments of this disclosure provide a single-well seepage field characterization device based on multi-factor hierarchical fuzzy comprehensive evaluation. The device includes: a factor acquisition module, a factor evaluation module, a weight matrix determination module, a membership matrix determination module, and a field strength characterization module. The factor acquisition module acquires multiple potential influencing factors related to the formation and distribution of the seepage field in the target well. The factor evaluation module performs multi-factor hierarchical fuzzy comprehensive evaluation based on the degree of influence of these potential influencing factors on the seepage field strength and the relationships between the factors, obtaining multi-level evaluation factors and their corresponding weights. The weight matrix determination module determines a weight matrix based on the weights corresponding to the evaluation factors at each level. The membership matrix determination module determines a membership matrix based on the values ​​of the multi-level evaluation factors at each measurement point in the target well. The field strength characterization module calculates the seepage field characterization intensity of the target well based on the membership matrix and the weight matrix.

[0013] Thirdly, embodiments of this disclosure provide an electronic device. The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus; the memory stores computer programs; and the processor, when executing the program stored in the memory, implements the single-well seepage field characterization method based on multi-factor hierarchical fuzzy comprehensive evaluation as described above.

[0014] Fourthly, embodiments of this disclosure provide a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the single-well seepage field characterization method based on multi-factor hierarchical fuzzy comprehensive evaluation as described above.

[0015] The technical solutions provided in the embodiments of this disclosure have at least some or all of the following advantages:

[0016] By acquiring multiple potential influencing factors of the formation and distribution of the seepage field in the target well, and based on the degree of influence of these multiple potential influencing factors on the seepage field strength and the relationship between the factors, a multi-factor hierarchical fuzzy comprehensive evaluation is performed to obtain multi-level evaluation factors and the weights corresponding to each level of evaluation factors. Through multi-factor hierarchical fuzzy comprehensive evaluation, the truly influential factors among the multiple potential influencing factors can be identified as evaluation factors. At the same time, by performing hierarchical fuzzy comprehensive evaluation, numerous evaluation factors are divided into multiple levels, thereby enabling a more accurate determination of the weight matrix and membership matrix corresponding to each evaluation factor, and thus a more accurate determination of the seepage field characterization intensity of the target well. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0018] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a single-well seepage field characterization method based on multi-factor hierarchical fuzzy comprehensive evaluation according to an embodiment of the present disclosure is shown schematically.

[0020] Figure 2 The diagrams illustrate (a) the evaluation factor analysis of static flow field intensity and (b) the evaluation factor analysis of dynamic flow field intensity according to an embodiment of the present disclosure.

[0021] Figure 3 The illustration schematically shows a process of obtaining multi-level evaluation factors and characterizing the seepage field of a single well by performing multi-factor hierarchical fuzzy comprehensive evaluation of multiple potential influencing factors according to an embodiment of the present disclosure.

[0022] Figure 4 A schematic diagram illustrating the process of interpolation based on radial basis functions according to an embodiment of the present disclosure is shown.

[0023] Figure 5 The diagram schematically illustrates the intensity distribution corresponding to the intensity of the seepage field characterization in the work area after interpolation according to an embodiment of the present disclosure.

[0024] Figure 6 A schematic diagram of a single-well seepage field characterization device based on multi-factor hierarchical fuzzy comprehensive evaluation according to an embodiment of the present disclosure is shown.

[0025] Figure 7 A schematic block diagram of an electronic device provided in an embodiment of the present disclosure is shown. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0027] The first exemplary embodiment of this disclosure provides a method for characterizing the seepage field of a single well based on multi-factor hierarchical fuzzy comprehensive evaluation. This method can be executed by an electronic device with computing capabilities.

[0028] Figure 1 A flowchart illustrating a single-well seepage field characterization method based on multi-factor hierarchical fuzzy comprehensive evaluation according to an embodiment of the present disclosure is shown schematically.

[0029] Reference Figure 1 As shown in the embodiments of this disclosure, the single-well seepage field characterization method based on multi-factor hierarchical fuzzy comprehensive evaluation includes the following steps: S110, S120, S130, S140 and S150.

[0030] In step S110, multiple potential influencing factors of the formation and distribution of the seepage field in the target well are obtained.

[0031] A target well is a production well deployed in an oil or gas reservoir development area. The aim is to analyze the seepage field and characterize the field strength of the target well.

[0032] In some embodiments, the acquisition method in step S110 includes at least one of the following approaches: the electronic device receives multiple potential influencing factors input by the user (the user can be an experienced person who obtains multiple potential influencing factors through manual analysis of reservoir geological characteristics, reservoir development data and numerical simulation results); or, the electronic device analyzes and obtains multiple potential influencing factors for the formation and distribution of seepage field through at least one of reservoir geological characteristics, reservoir development data and numerical simulation results.

[0033] There may be correlations among the aforementioned potential influencing factors, or one or more potential influencing factors may be useless for characterizing the field strength of the seepage field. Through subsequent multi-factor hierarchical fuzzy comprehensive evaluation processing, factors that are not related to the characterization of the seepage field are removed from these potential influencing factors, and the various potential factors are merged or split to ensure that the various evaluation factors obtained later are mutually independent dimensions.

[0034] In step S120, a multi-factor hierarchical fuzzy comprehensive evaluation is performed based on the degree of influence of the above-mentioned multiple potential influencing factors on the seepage field strength and the relationship between the factors, to obtain the multi-level evaluation factors and the weights corresponding to each level of evaluation factors.

[0035] In some embodiments, the above-mentioned multi-level evaluation factors include: a primary category evaluation factor and at least one subordinate level corresponding to a more refined category evaluation factor. The lowest subordinate level corresponds to the most refined category evaluation factor, which, according to the hierarchical relationship, is the factor with the smallest granularity.

[0036] Figure 2 The diagrams illustrate (a) the evaluation factor analysis of static flow field intensity and (b) the evaluation factor analysis of dynamic flow field intensity according to an embodiment of the present disclosure.

[0037] For example, refer to Figure 2 As shown in (a) and (b), the analysis of the seepage field for static and dynamic flow field intensities reveals that multiple factors are involved, which can be broadly categorized into two types: one type is characterizing factors, which often reflect the intensity of the seepage field and are passively changing factors. These factors can usually be analyzed to understand the changes in the intensity of the seepage field; the other type is influencing factors, which are the main controlling factors affecting the formation of the seepage field. The flow field intensity can be changed by adjusting these influencing factors.

[0038] Figure 3 The illustration schematically shows a process of performing multi-factor hierarchical fuzzy comprehensive evaluation processing on multiple potential influencing factors to obtain multi-level evaluation factors and characterizing the seepage field of a single well, according to an embodiment of the present disclosure.

[0039] Reference Figure 3 As shown, a hierarchical analysis is performed based on the relationship between multiple potential influencing factors, dividing them into two levels of evaluation factors. The first-level evaluation factors correspond to two main categories of evaluation factors: static factors (specifically, static flow field intensity evaluation factors, referred to as static factors) and dynamic factors (specifically, dynamic flow field intensity evaluation factors, referred to as dynamic factors). Based on the main category (static factors or dynamic factors) to which each potential influencing factor belongs, the sub-level of the static factors is further refined into detailed category evaluation factors.

[0040] As an example, the sub-level evaluation factors corresponding to static factors include: grade difference, permeability, sand body thickness, porosity, flow coefficient, original oil saturation, number of interconnected wells, and sedimentary microfacies. The sub-level evaluation factors corresponding to dynamic factors include: cumulative water injection intensity, relative water absorption, cumulative water injection intensity, relative production intensity, effectiveness, injection-production well spacing, oil saturation, water cut, and cumulative water-oil ratio. That is, in this embodiment, a multi-factor hierarchical fuzzy comprehensive evaluation is performed on multiple potential influencing factors, dividing them into two levels of evaluation factors; the primary evaluation factors at the first level are static factors and dynamic factors, respectively; the secondary level corresponds to the most refined category of evaluation factors, which includes a total of 17 evaluation factors.

[0041] In other embodiments, for other work areas, the corresponding evaluation factors may have two or more levels. The specific sub-levels may contain fewer or more factors. The content of the specific factors (e.g., sedimentary microfacies) may be the same as in the example, or there may be other factors that are different from the example (e.g., other influencing factors may also be included).

[0042] By analyzing the degree of influence of each potential influencing factor on the seepage field strength, the weights corresponding to the above 17 evaluation factors can be obtained. For example, after determining all seepage field characterization factors, the weights of various characterization factors (specifically 17 evaluation factors) of the first-level fuzzy evaluation factors (static factors and dynamic factors) and the second-level fuzzy evaluation factors (flow field strength) are calculated according to the analytic hierarchy process, so as to obtain the final weights of various characterization parameters.

[0043] In some embodiments, based on the variability and influence correlation of the evaluation factors corresponding to the static flow field intensity and the dynamic flow field intensity, the first-level fuzzy evaluation factors are determined: the weights corresponding to the dynamic factors and the static factors are 2 / 3 (0.6667 expressed as a decimal to four decimal places) and 1 / 3 (0.3333 expressed as a decimal to four decimal places), respectively.

[0044] The weight vector B1 corresponding to the second-level fuzzy evaluation factors of the static flow field (the weights within their respective levels; the overall weights need to be multiplied sequentially at each level):

[0045] B1 = (Sedimentary microfacies, grade difference, permeability, sand body thickness, porosity, flow coefficient, original oil saturation, inter-well connectivity) T

[0046] =(0.4174, 0.1229, 0.0731, 0.0422, 0.0251, 0.1602, 0.0974, 0.0615) T .

[0047] The weight vector B2 corresponding to the second-level fuzzy evaluation factors of the dynamic flow field (the weights within their respective levels; the overall weights need to be multiplied progressively at each level):

[0048] B2 = (Cumulative production intensity, relative production intensity, effectiveness, injection-production well spacing, water saturation, water cut, cumulative oil-water ratio, cumulative water injection intensity, relative water absorption) T

[0049] =(0.3112, 0.1556, 0.2775, 0.1602, 0.0514, 0.0283, 0.0155, 0.6667, 0.3333) T .

[0050] In step S130, the weight matrix is ​​determined according to the weights corresponding to the evaluation factors at each level.

[0051] In step S130 above, the weight matrix is ​​determined based on the weights corresponding to the evaluation factors at each level, including:

[0052] The weights of each evaluation factor are multiplied according to their hierarchical relationship to obtain the weights of the most refined category evaluation factors. For example, the weight vector B1 corresponding to the second-level fuzzy evaluation factor of the static flow field is multiplied by 1 / 3 of the weight of the corresponding static factor of the upper-level category to obtain the weights of the most refined category evaluation factors: {sedimentary microfacies, grade difference, permeability, sand body thickness, porosity, flow coefficient, original oil saturation, well connectivity}. The weight vector B2 corresponding to the second-level fuzzy evaluation factor of the dynamic flow field is multiplied by 2 / 3 of the weight of the corresponding dynamic factor of the upper-level category to obtain the weights of the most refined category evaluation factors: {cumulative production intensity, relative production intensity, effectiveness, injection-production well spacing, water saturation, water cut, cumulative oil-water ratio, cumulative water injection intensity, relative water absorption}.

[0053] The weights corresponding to the most refined category evaluation factors mentioned above are determined as elements of the weight matrix.

[0054] The weight matrix described above has a dimension of m rows × 1 column, where m represents the total number of evaluation factors corresponding to the most granular category, and m ≥ 2. In this embodiment, the weight matrix has a dimension of 17 rows × 1 column.

[0055] In step S140, the membership matrix is ​​determined based on the values ​​of the multi-level evaluation factors corresponding to each measurement point in the target well.

[0056] In some embodiments, step 140 above, determining the membership matrix based on the values ​​of the multi-level evaluation factors corresponding to each measurement point in the target well, includes:

[0057] Obtain the actual measurement values ​​of each measurement point in the target well corresponding to each of the most refined category evaluation factors;

[0058] The actual measured values ​​of all measurement points under the same most refined category evaluation factor are normalized to obtain the normalized values ​​of each measurement point corresponding to each most refined category evaluation factor.

[0059] The normalized values ​​of each measurement point corresponding to each of the most refined category evaluation factors are determined as elements of the membership matrix.

[0060] By defining the normalized values ​​as elements of the membership matrix, different evaluation elements can be unified into a dimensionless form, and the magnitude of the values ​​can reflect the membership degree of each evaluation element to the seepage field strength.

[0061] In some embodiments, the true measured values ​​of all measurement points under the same most refined category evaluation factor are normalized to obtain the normalized value of each measurement point corresponding to each most refined category evaluation factor. This includes: for the same most refined category evaluation factor, determining the maximum and minimum values ​​among the true measured values ​​of all measurement points, and calculating the range between the maximum and minimum values; for each measurement point, calculating the difference between the true measured value of the current measurement point corresponding to the current most refined category evaluation factor and the minimum or maximum value, and calculating the ratio between the above difference and the corresponding range, where the ratio is the normalized value of the current measurement point corresponding to the current most refined category evaluation factor.

[0062] For example, the process of normalizing the actual measured value of the grade difference (X) is as follows:

[0063]

[0064] Where FX(i) represents the normalized value of the grade difference at the i-th measurement point, i represents the index of the measurement point, and n represents the total number of measurement points; X(i) represents the actual measured value of the grade difference at the i-th measurement point; X min X represents the minimum value among the actual measured values ​​of the grade difference for all measurement points; max This represents the maximum value among the actual measured values ​​of the grade difference across all measurement points.

[0065] The process of normalizing the actual measured value of permeability (k) is as follows:

[0066]

[0067] Where Fk(i) represents the normalized permeability value at the i-th measurement point; k(i) represents the actual measured permeability value at the i-th measurement point; k min k represents the minimum of the actual permeability measurements at all measurement points.max This represents the maximum value among all the actual permeability measurements at all measurement points.

[0068] The process of normalizing the actual measured value of sand body thickness (h) is as follows:

[0069]

[0070] Where Fh(i) represents the normalized value of the sand body thickness at the i-th measurement point; h(i) represents the actual measured value of the sand body thickness at the i-th measurement point; h min h represents the minimum value among the actual measured values ​​of sand body thickness at all measurement points. max This represents the maximum value among the actual measured values ​​of sand body thickness at all measurement points.

[0071] Regarding porosity The process of normalizing the actual measured values ​​is as follows:

[0072]

[0073] in, This represents the normalized porosity value at the i-th measurement point; This represents the actual measured value of porosity at the i-th measurement point; This represents the minimum value among the actual measured porosity values ​​at all measurement points; This represents the maximum value among all the actual measured porosity values ​​at all measurement points.

[0074] The process of normalizing the actual measured value of the flow coefficient (C) is as follows:

[0075]

[0076] Where FC(i) represents the normalized value of the flow coefficient at the i-th measurement point; C(i) represents the actual measured value of the flow coefficient at the i-th measurement point; C min C represents the minimum value among the actual measured values ​​of the flow coefficient at all measurement points. max This represents the maximum value among the actual measured values ​​of the flow coefficient at all measurement points.

[0077] The process of normalizing the original measured oil saturation (So) is as follows:

[0078]

[0079] Where FSo(i) represents the normalized value of the original oil saturation at the i-th measurement point; So(i) represents the actual measured value of the original oil saturation at the i-th measurement point; So minSo represents the minimum value among the actual measured values ​​of the original oil saturation at all measurement points; max This represents the maximum value among all the original oil saturation measurements at all measurement points.

[0080] The process of normalizing the actual measured value of the number of interconnected wells (N) is as follows:

[0081]

[0082] Where FN(i) represents the normalized value of the number of interconnected wells at the i-th measurement point; N(i) represents the actual measured value of the number of interconnected wells at the i-th measurement point; N min N represents the minimum value among all the original oil saturation measurements at all measurement points; max This represents the maximum value among the actual measured values ​​of the number of interconnected wells across all measurement points.

[0083] The process for determining normalized values ​​for sedimentary microfacies is as follows: The average fluid velocity of each microfacies is statistically analyzed, and the maximum value is used as the denominator to normalize the fluvial and deltaic facies in the study area. The normalized values ​​are then used as elements of the membership matrix. The results of the normalization are shown in Table 1 below.

[0084] Table 1. Examples of normalized values ​​for the sedimentary facies evaluation factor.

[0085] Microphase type Average fluid velocity Normalized values Microphase type Average fluid velocity Normalized values beach 58.04 1 Underwater diversion channel 41.14 1 Riverbed sedimentation 40.59 0.7 River Estuary Dam 35.23 0.85 River floodplain 1.2 0.05 Diversion Bay 3.04 0.1

[0086] The process of normalizing the actual measured value of cumulative water injection intensity (QwH) is as follows:

[0087]

[0088] Where FQwH(i) represents the normalized value of the cumulative water injection intensity at the i-th measurement point; QwH(i) represents the actual measured value of the cumulative water injection intensity at the i-th measurement point; QwH min This represents the minimum value among the actual measured values ​​of the cumulative water injection intensity at all measuring points; QwH max This represents the maximum value among the actual measured values ​​of the cumulative water injection intensity at all measurement points.

[0089] The process of normalizing the actual measured value of relative water absorption (W) is as follows:

[0090]

[0091] Where FW(i) represents the normalized value of the relative water absorption at the i-th measurement point; W(i) represents the actual measured value of the relative water absorption at the i-th measurement point; W minW represents the minimum of the actual measured values ​​of relative water absorption at all measuring points. max This represents the maximum value of the actual measured relative water absorption at all measurement points.

[0092] The process of normalizing the actual measured value of cumulative water injection intensity (QH) is as follows:

[0093]

[0094] Where FQH(i) represents the normalized value of the cumulative water injection intensity at the i-th measurement point; QH(i) represents the actual measured value of the cumulative water injection intensity at the i-th measurement point; QH min QH represents the minimum value among the actual measured values ​​of the cumulative water injection intensity at all measuring points. max This represents the maximum value of the actual measured cumulative water injection intensity at all measurement points.

[0095] The process of normalizing the actual measured value of relative production intensity (Q) is as follows:

[0096]

[0097] Where FQ(i) represents the normalized value of the relative liquid production intensity at the i-th measurement point; Q9i) represents the actual measured value of the relative liquid production intensity at the i-th measurement point; Q min Q represents the minimum of the actual measured values ​​of the relative fluid production intensity at all measurement points; max This represents the maximum value of the actual measured relative liquid production intensity at all measurement points.

[0098] Based on the degree of effectiveness, the following levels and normalized values ​​are defined according to the differences in effectiveness:

[0099] Strong effect = 1, Significant effect = 0.8, Moderate effect = 0.6, Slight effect = 0.3, No effect = 0;

[0100] The process of normalizing the actual measured value of the injection-production well spacing (D) is as follows:

[0101]

[0102] Where FD(i) represents the normalized value of the injection-production well distance at the i-th measurement point; D(i) represents the actual measured value of the injection-production well distance at the i-th measurement point; D min D represents the minimum value among the actual measured values ​​of the injection-production well distance at all measurement points. max This represents the maximum value of the actual measured distance between injection and production wells for all measurement points.

[0103] The process of normalizing the actual measured value of oil saturation (SoN) is as follows:

[0104]

[0105] Where FSoN(i) represents the normalized value of oil saturation at the i-th measurement point; SoN(i) represents the true measured value of oil saturation at the i-th measurement point; SoN min SoN represents the minimum value among all measured oil saturation values ​​at all measurement points. max This represents the maximum value of the actual measured oil saturation at all measurement points.

[0106] The process of normalizing the actual measured moisture content (MC) is as follows:

[0107]

[0108] Where FMC(i) represents the normalized value of the moisture content at the i-th measurement point; MC(i) represents the actual measured value of the moisture content at the i-th measurement point; MC min MC represents the minimum of the actual measured moisture content values ​​at all measurement points. max This represents the maximum value of the actual measured moisture content at all measurement points.

[0109] The process of normalizing the actual measured value of the cumulative oil-water ratio (OSR) is as follows:

[0110]

[0111] Where FOSR(i) represents the normalized cumulative water-oil ratio value at the i-th measurement point; OSR(i) represents the actual measured cumulative water-oil ratio value at the i-th measurement point; OSR min OSR represents the minimum cumulative water-to-oil ratio among all measured values. max This represents the maximum value of the cumulative water-to-oil ratio at all measurement points.

[0112] In some embodiments, the membership matrix has an n-row × m-column dimension; where n represents the total number of measurement points, n≥2; and m represents the total number of the most refined category evaluation factors, m≥2.

[0113] For example, the membership matrix can be represented as follows:

[0114]

[0115] Among them, the first column element 'a' in the membership degree is... 11 ~a n1 The first column represents the normalized value of the first to nth measurement points corresponding to the first most refined category evaluation factor; the mth column represents the normalized value of the first to nth measurement points corresponding to the mth most refined category evaluation factor.

[0116] The weight matrix is ​​represented as follows:

[0117]

[0118] The weight matrix described above has an m-row × 1-column dimension, where m represents the total number of evaluation factors corresponding to the most granular category, m ≥ 2, and b1 ~ b m This indicates the weights of all measurement points corresponding to the 1st to mth most refined category evaluation factors.

[0119] In step S150, the intensity of the seepage field characterization of the target well is obtained by calculating based on the membership matrix and the weight matrix.

[0120] In some embodiments, step S150 above, calculating the seepage field characterization intensity of the target well based on the membership matrix and the weight matrix, includes:

[0121] The membership matrix and the weight matrix are multiplied to obtain the seepage field strength corresponding to each measurement point. For example, the membership matrix has a dimension of n rows × m columns, and the weight matrix has a dimension of m rows × 1 column. The membership matrix and the weight matrix are multiplied to obtain the seepage field strength corresponding to n measurement points. For example, the matrix multiplication of formula (16) and formula (17) can obtain a matrix result of n rows and 1 column, where the value of each row is the seepage field strength of the corresponding measurement point. Based on the seepage field strength corresponding to each measurement point in the target well, interpolation is performed to obtain the seepage field characterization intensity in the work area corresponding to the target well.

[0122] Figure 4 A schematic diagram illustrating the process of interpolation based on radial basis functions according to an embodiment of the present disclosure is shown. Figure 5 An intensity distribution diagram corresponding to the intensity of the seepage field characterization in the work area after interpolation according to an embodiment of the present disclosure is schematically shown.

[0123] In some embodiments, refer to Figure 4 As shown, the seepage field intensity at each measurement point is a simulation result, which is discrete scatter data (the location of each measurement point and the corresponding calculated seepage field intensity). By reading the scatter data, performing data slicing and meshing, the data corresponding to each measurement point is distributed in a coordinate system. Then, interpolation is performed based on the radial basis function to generate the intensity distribution map corresponding to the interpolated seepage field intensity in the work area. See [link to relevant documentation]. Figure 5 As shown. In Figure 5 The diagram visually illustrates the distribution of the seepage field intensity. Based on the contour values ​​of the seepage field intensity, it can be determined that the seepage field intensity gradually decreases from high to low values.

[0124] Radial basis functions are radially symmetric scalar functions, typically defined as monotonic functions of the Euclidean radial distance between the sample and the data center. In the embodiments of this disclosure, discrete nodes are distributed within an irregular region to construct a mesh model. Interpolation based on radial basis functions within the mesh model solves problems that uniform meshes cannot address, improving the accuracy of seepage field intensity prediction and reducing interpolation errors. Specifically, interpolation based on radial basis functions not only adapts to the distribution of the seepage field and characterizes the local features of the data, but also, due to its ability to handle irregular or sparse data, achieves high interpolation accuracy, reduces interpolation errors, and effectively improves the accuracy of seepage field intensity prediction.

[0125] In embodiments including steps S110 to S150, multiple potential influencing factors of the formation and distribution of the seepage field of the target well are obtained; based on the degree of influence of the multiple potential influencing factors on the seepage field strength and the relationship between the factors, a multi-factor hierarchical fuzzy comprehensive evaluation is performed to obtain multi-level evaluation factors and the weights corresponding to each level of evaluation factors; by performing multi-factor hierarchical fuzzy comprehensive evaluation, the factors that truly have an impact among the multiple potential influencing factors can be discovered as evaluation factors. At the same time, by performing hierarchical fuzzy comprehensive evaluation, numerous evaluation factors are divided into multiple levels, thereby enabling a more accurate determination of the weight matrix and membership matrix corresponding to each evaluation factor, and thus a more accurate determination of the seepage field characterization intensity of the target well.

[0126] By setting up a hierarchical fuzzy comprehensive evaluation process, the qualitative evaluation of fuzzy concepts is transformed into quantitative evaluation results, which helps to further analyze the distribution of the seepage field strength and its subsequent applications.

[0127] In subsequent application stages, for example, by combining multiple factors, cluster analysis can be used to classify the flow field into four levels: I, II, III, and IV. The intensity of all flow fields is ranked to determine the distribution of each level, as shown in Table 2.

[0128] Table 2 Classification and Evaluation Criteria for Seepage Fields

[0129]

[0130]

[0131] A second exemplary embodiment of this disclosure provides a single-well seepage field characterization device based on multi-factor hierarchical fuzzy comprehensive evaluation.

[0132] Figure 6 A schematic diagram of a single-well seepage field characterization device based on multi-factor hierarchical fuzzy comprehensive evaluation according to an embodiment of the present disclosure is shown.

[0133] Reference Figure 6 As shown in the embodiments of this disclosure, the single-well seepage field characterization device 600 based on multi-factor hierarchical fuzzy comprehensive evaluation includes: a factor acquisition module 610, a factor evaluation module 620, a weight matrix determination module 630, a membership matrix determination module 640, and a field strength characterization module 650.

[0134] The aforementioned factor acquisition module 610 is used to acquire multiple potential influencing factors of the formation and distribution of the seepage field in the target well.

[0135] The aforementioned factor evaluation module 620 is used to perform multi-factor hierarchical fuzzy comprehensive evaluation processing based on the degree of influence of the above-mentioned multiple potential influencing factors on the seepage field strength and the relationship between the factors, so as to obtain the multi-level evaluation factors and the weights corresponding to each level of evaluation factors.

[0136] The aforementioned weight matrix determination module 630 is used to determine the weight matrix based on the weights corresponding to the evaluation factors at each level.

[0137] The membership matrix determination module 640 is used to determine the membership matrix based on the values ​​of each measurement point in the target well corresponding to the above-mentioned multi-level evaluation factors.

[0138] The field strength characterization module 650 is used to calculate the seepage field characterization intensity of the target well based on the membership matrix and the weight matrix.

[0139] More details and beneficial effects included in this embodiment can be found in the relevant description of the first embodiment, which will not be repeated here.

[0140] Any number of the functional modules included in the aforementioned single-well seepage field characterization device 600 can be combined into one module, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. At least one of the functional modules included in the aforementioned single-well seepage field characterization device 600 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the functional modules included in the aforementioned single-well seepage field characterization device 600 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0141] A third exemplary embodiment of this disclosure provides an electronic device.

[0142] Figure 7 A schematic block diagram of an electronic device provided in an embodiment of the present disclosure is shown.

[0143] Reference Figure 7 As shown, the electronic device 700 provided in this embodiment includes a processor 701, a communication interface 702, a memory 703, and a communication bus 704. The processor 701, the communication interface 702, and the memory 703 communicate with each other through the communication bus 704. The memory 703 is used to store computer programs. When the processor 701 executes the program stored in the memory, it implements the single-well seepage field characterization method based on multi-factor hierarchical fuzzy comprehensive evaluation as described above.

[0144] A fourth exemplary embodiment of this disclosure also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the single-well seepage field characterization method based on multi-factor hierarchical fuzzy comprehensive evaluation as described above.

[0145] The computer-readable storage medium may be included in the device or apparatus described in the above embodiments; or it may exist independently and not assembled into the device or apparatus. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0146] According to embodiments of this disclosure, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0147] It should be noted that the collection, gathering, updating, analysis, processing, use, transmission, and storage of user personal information involved in the technical solutions provided in this disclosure comply with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken to prevent unauthorized access to user personal information data and to safeguard user personal information security, network security, and national security.

[0148] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0149] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for characterizing the seepage field of a single well based on hierarchical fuzzy comprehensive evaluation, characterized in that, include: To identify multiple potential influencing factors of the formation and distribution of the seepage field in the target well; Based on the degree of influence of the multiple potential influencing factors on the seepage field strength and the relationship between the factors, a multi-factor hierarchical fuzzy comprehensive evaluation process is performed to obtain the multi-level evaluation factors and the weights corresponding to each level of evaluation factors. Determine the weight matrix based on the weights corresponding to the evaluation factors at each level; The membership matrix is ​​determined based on the values ​​of the multi-level evaluation factors corresponding to each measurement point in the target well; The intensity of the seepage field characterization of the target well is obtained by calculating based on the membership matrix and the weight matrix.

2. The method according to claim 1, characterized in that, The multi-level evaluation factors include: main category evaluation factors and more detailed category evaluation factors corresponding to at least one subordinate level; The weight matrix is ​​determined based on the weights corresponding to the evaluation factors at each level, including: The weights of each most detailed category of evaluation factors are obtained by multiplying the corresponding weights according to the hierarchical relationship between the evaluation factors at each level. The weights corresponding to each of the most refined category evaluation factors are determined as elements of the weight matrix; The weight matrix has a dimension of m rows × 1 column, where m represents the total number of evaluation factors corresponding to the most refined category, and m ≥ 2.

3. The method according to claim 1, characterized in that, The multi-level evaluation factors include: main category evaluation factors and more detailed category evaluation factors corresponding to at least one subordinate level; Specifically, the membership matrix is ​​determined based on the values ​​of the multi-level evaluation factors corresponding to each measurement point in the target well, including: Obtain the actual measurement values ​​of each measurement point in the target well corresponding to each of the most refined category evaluation factors; The actual measured values ​​of all measurement points under the same most refined category evaluation factor are normalized to obtain the normalized values ​​of each measurement point corresponding to each most refined category evaluation factor. The normalized values ​​of each measurement point corresponding to each of the most refined category evaluation factors are determined as elements of the membership matrix; The membership matrix has n rows and m columns; where n represents the total number of measurement points, n≥2; and m represents the total number of the most refined category evaluation factors, m≥2.

4. The method according to claim 3, characterized in that, The actual measured values ​​of all measurement points under the same most granular category evaluation factor are normalized to obtain the normalized values ​​of each measurement point corresponding to each most granular category evaluation factor, including: For the same most refined category evaluation factor, determine the maximum and minimum values ​​among the actual measured values ​​corresponding to all measurement points, and calculate the range between the maximum and minimum values; For each measurement point, calculate the difference between the actual measured value and the minimum or maximum value of the evaluation factor corresponding to the current most refined category for the current measurement point, and calculate the ratio between the difference and the corresponding range. The ratio is the normalized value of the evaluation factor corresponding to the current most refined category for the current measurement point.

5. The method according to claim 1, characterized in that, The intensity of the seepage field characterization of the target well is calculated based on the membership matrix and the weight matrix, including: The membership matrix and the weight matrix are multiplied to obtain the seepage field strength corresponding to each measurement point. Based on the seepage field strength corresponding to each measurement point in the target well, interpolation processing is performed to obtain the seepage field characterization intensity in the work area corresponding to the target well.

6. The method according to claim 5, characterized in that, Interpolation is performed based on radial basis functions; The multi-level evaluation factors include: main category evaluation factors and more detailed category evaluation factors corresponding to at least one subordinate level; The membership matrix has an n-row × m-column dimension, and the weight matrix has an m-row × 1-column dimension; where n represents the total number of measurement points, n≥2; and m represents the total number of the most refined category evaluation factors, m≥2. The membership matrix and the weight matrix are multiplied to obtain the seepage field strength corresponding to the n measurement points.

7. The method according to claim 1, characterized in that, The multi-level evaluation factors are two-level evaluation factors, where the main categories of evaluation factors corresponding to the first-level evaluation factors are: static factors and dynamic factors. Among them, the sub-level evaluation factors corresponding to the static factors include at least one of the following: grade difference, permeability, sand body thickness, porosity, flow coefficient, original oil saturation, number of interconnected wells, and sedimentary microfacies; The sub-level evaluation factors corresponding to dynamic factors include at least one of the following: cumulative water injection intensity, relative water absorption, cumulative water injection intensity, relative fluid production intensity, effectiveness, injection-production well distance, oil saturation, water cut, and cumulative water-oil ratio.

8. A single-well seepage field characterization device based on hierarchical fuzzy comprehensive evaluation, characterized in that, include: The factor acquisition module is used to acquire multiple potential influencing factors of the formation and distribution of the seepage field in the target well; The factor evaluation module is used to perform multi-factor hierarchical fuzzy comprehensive evaluation processing based on the degree of influence of the multiple potential influencing factors on the seepage field strength and the relationship between the factors, so as to obtain the multi-level evaluation factors and the weights corresponding to each level of evaluation factors. The weight matrix determination module is used to determine the weight matrix based on the weights corresponding to the evaluation factors at each level. The membership matrix determination module is used to determine the membership matrix based on the values ​​of each measurement point in the target well corresponding to the multi-level evaluation factors. The field strength characterization module is used to calculate the seepage field characterization intensity of the target well based on the membership matrix and the weight matrix.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1-7.

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