CN114169616A - Method for distinguishing low-yield and low-efficiency well - Google Patents

Method for distinguishing low-yield and low-efficiency well Download PDF

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CN114169616A
CN114169616A CN202111509841.1A CN202111509841A CN114169616A CN 114169616 A CN114169616 A CN 114169616A CN 202111509841 A CN202111509841 A CN 202111509841A CN 114169616 A CN114169616 A CN 114169616A
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董凤娟
孙泽庸
卢学飞
任大忠
黄海
陈悦
屈乐
周佳怡
周超
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Abstract

A discrimination method of low-yield and low-efficiency wells comprises the steps of calculating the cumulative yield contribution rate of a single well according to the actual production characteristics of an oil/gas field, and drawing a distribution curve of the cumulative yield contribution rate of the oil/gas well; introducing slope change rates of any two continuous points of an oil/gas well yield accumulated contribution rate curve, determining class division points of the oil/gas well yield accumulated contribution rate curve, and dynamically classifying oil/gas wells; based on the oil/gas well dynamic classification result, in order to consider the interlayer interference condition in the oil/gas well exploitation process, in addition to selecting conventional static geological evaluation parameters, introducing the vertical commingled production sand volume number as an evaluation index, and performing oil/gas well static classification by adopting an entropy weight-ideal point method; based on the dynamic and static classification results of the oil/gas well, introducing autocorrelation distance and mode thereof, considering the development practice of the oil/gas reservoir, judging abnormal value points, and optimizing the low-yield and low-efficiency gas well; the invention can accurately and reliably carry out the low-yield and low-efficiency well optimization.

Description

Method for distinguishing low-yield and low-efficiency well
Technical Field
The invention relates to the technical field of oil and gas field development, in particular to a method for distinguishing a low-yield low-efficiency well.
Background
The low-yield and low-efficiency well refers to a well with low accumulated production of a single well within a certain time, and from the economic benefit perspective, the well with no economic benefit or low economic benefit is produced in a near stage.
With the extension of the development time of oil and gas fields, the oil and gas fields enter a medium-high water-containing period, because of increasingly prominent contradictions between layers and in-layers, sand production (geological factors), high water content, underground falling objects to be overhauled, to be operated and other reasons, low-yield and low-efficiency wells are increased year by year, and the improvement of the production and the recovery ratio of the oil fields is greatly influenced, so that the method becomes one of key problems restricting the development of the oil and gas fields. Oil and gas field development practices show that at present, a part of old wells are produced again through measures such as layer adjustment and repair, water shutoff, sidetracking, overhaul, repeated fracturing and the like on site, and a good effect is achieved. However, the discrimination of the low-yield and low-efficiency well is a multi-factor combined process, the workload is huge, the time consumption is long, and each type of measure also has a certain adaptive range. Meanwhile, the existing oil/gas well classification methods have certain limitations, for example, the reservoir parameter method mainly uses geological parameters (generally selects parameters such as effective sand body thickness, porosity, permeability, gas saturation and the like) as the basis to classify the oil/gas wells, and does not consider the interlayer interference condition in the oil/gas well exploitation process; the oil/gas test unobstructed flow method cannot accurately reflect the actual productivity of the oil/gas well; the daily oil/gas production rule does not consider the influence of production time on the oil/gas production capacity; the unit pressure drop oil/gas production method of the oil/gas well cannot reflect the actual production energy of the discontinuously produced oil/gas well. Therefore, in order to make the discrimination result of the low-yield and low-efficiency well more accurate and reliable, it is necessary to establish a static and dynamic fusion method for discriminating the low-yield and low-efficiency well.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a low-yield and low-efficiency well distinguishing method, which comprehensively considers the static and dynamic characteristics of an oil/gas well, integrates a plurality of methods to distinguish the low-yield and low-efficiency well and has the characteristics of more accurate and reliable distinguishing.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a method for distinguishing a low-yield and low-efficiency well comprises the following steps:
step one, counting the accumulated yield of a single well based on the production dynamic data of a research area, and sequencing and numbering the accumulated yields of the single well in the descending order;
step two, calculating the yield contribution rate of the single well;
step three, drawing an oil/gas well yield accumulated contribution rate distribution curve;
step four, performing oil/gas well dynamic classification by using class division points of the oil/gas well yield accumulation contribution rate distribution curve;
step five, besides selecting conventional static geological evaluation parameters, introducing the number of the commingled production sands in the longitudinal direction as an evaluation index, and performing static classification on the oil/gas well by adopting an entropy weight-ideal point method; the conventional static geological evaluation parameters comprise effective sand body thickness, porosity, permeability and oil/gas saturation;
and step six, introducing autocorrelation distance and mode thereof based on the results of dynamic classification and static classification of the oil/gas wells, considering the actual development of the oil/gas reservoir, judging abnormal value points, and optimizing the low-yield low-efficiency gas well.
The second step comprises the following specific implementation steps:
the yield contribution rate refers to the cumulative yield of a single well in the research area as a percentage of the cumulative yield of the whole block in a certain time.
Figure BDA0003404813180000031
In the formula, thetaiThe yield contribution rate of the ith well; qiCumulative production for the ith well; qtThe production is accumulated for all wells in the zone.
The third step comprises the following specific implementation steps:
(1) on the basis of the single-well yield contribution rate obtained in the step two, sequentially calculating the yield accumulated contribution rate of the oil/gas well by taking the single-well accumulated yield maximum well as a base point according to the sequence number of the well in the step one;
(2) the number of the oil/gas well is represented by an abscissa, the accumulated contribution rate of the yield is represented by an ordinate, and a distribution curve of the accumulated contribution rate of the yield of the oil/gas well is drawn; during drawing, the wells with the largest cumulative yield of the single well are taken as base points and accumulated one by one towards the wells with small cumulative yields.
The class division points in the fourth step are as follows: when the yield contribution rate of the ith oil/gas well obviously changes, an obvious inflection point appears on a yield accumulated contribution rate distribution curve, and the inflection point is called as a class segmentation point and used for dynamically classifying the oil/gas wells, and the specific implementation steps of the method comprise:
(1) if there are n oil/gas wells in the area under study, n>3, P is ═ P1(x1，y1)，P2(x2，y2)，…，Pn(xn，yn)]A set of points forming a cumulative contribution profile for oil/gas well production, and wherein a point may be represented as P (i) ═ Pi(xi，yi) (i is more than or equal to 1 and less than or equal to n); calculating the slope k of a straight line formed by any two continuous points in sequencej(j ═ 1, 2, …, n-1), the rate of change of the cumulative contribution rate of oil/gas well production;
(2) in consideration of the difference of the oil/gas well yield and the contribution rate thereof, introducing the slope change rate of any two continuous points of a yield accumulative contribution rate distribution curve, namely the second-order partial derivative of the oil/gas well yield accumulative contribution rate, wherein different slope change rates represent different types of oil/gas wells, and thus determining the class division point of the oil/gas well yield accumulative contribution rate curve;
(3) and according to the class dividing points of the oil/gas well yield accumulation contribution rate distribution curve, dividing the oil/gas wells in the research area into different types.
The concrete implementation steps of the fifth step comprise:
(1) establishing an evaluation matrix;
for the oil gas/well R to be evaluated, n evaluation indexes are set, the n indexes are regarded as n target functions of the oil gas/well R to be evaluated, and a vector function F (x) is made to be [ f [ ]1(x)，f2(x)，...，fn(x)]The corresponding weight is W1、W2、…、WnTo evaluate the hydrocarbon/well R at the objective function fi(x) The lower value is xi. Its index matrix is
X＝{x1，x2，...，xn}[W1，W2，...，Wn]T
(2) Determining the weight of each index;
firstly, adopting a linear change method to carry out standardization processing on each index; taking the optimum value of each index
Figure BDA0003404813180000041
After normalization, a new evaluation matrix is obtained as follows:
Figure BDA0003404813180000042
secondly, setting the entropy value of the jth evaluation index as:
Figure BDA0003404813180000043
in the formula (I), the compound is shown in the specification,
Figure BDA0003404813180000044
③ f is less than or equal to 0ijE is not more than 1, so that e is not less than 0jLess than or equal to 1, and obtaining the total entropy of all evaluation indexes as follows:
Figure BDA0003404813180000051
fourthly, defining the deviation degree of the index j as dj＝1-ejThen, the entropy weight of the evaluation index j is:
Figure BDA0003404813180000052
(3) determining an ideal point and an anti-ideal point;
the evaluation indexes are divided into two main types, namely positive indexes and negative indexes: the larger the positive index, i.e., the index value, the better, and the smaller the reverse index, i.e., the index value, the better; assuming that the index changes monotonously, thereby defining an ideal point and an anti-ideal point;
when the index is a positive index, there are:
Figure BDA0003404813180000053
when the index is an inverse index, there are:
Figure BDA0003404813180000054
in the formula (I), the compound is shown in the specification,
Figure BDA0003404813180000055
respectively a positive ideal point vector and a negative ideal point vector of the ith index in the n indexes influencing the evaluation object; f. ofi(x) Is the actual value of the index; 1, 2, …, n;
(4) calculating the closeness of the ideal points;
the closer the positive ideal point of the index is dissociated, the farther from the negative ideal point, the better the solution is, and thus, the more the solution isThis n-dimensional space defines a modulus: i f (x) -f*(+)||→min，||f(x)-f*(-) - | → max, using euclidean distances, i.e. the relative distances to the positive and negative ideal points are:
Figure BDA0003404813180000061
Figure BDA0003404813180000062
the calculation formula of the ideal point closeness is T ═ D2/(D1+D2) T belongs to the interval [0, 1]The condition of the object is evaluated by an index T, and the larger T, i.e., the closer T is to the ideal point, the farther T is from the ideal point.
(5) And performing static classification on the oil/gas wells according to a normal distribution principle based on the dynamic classification result of the oil/gas wells.
The sixth specific implementation step comprises:
(1) based on the dynamic and static classification results of the oil/gas well, 2 data sequences, namely a dynamic classification result sequence Z are formedi(xi，yi) And static Classification result sequence Z'i(x’i，y’i)；
(2) Finding Zi(xi，yi)、Z’i(x’i，y’i) The autocorrelation distance between two points is calculated by the formula:
Figure BDA0003404813180000063
wherein, yi、y’iRespectively obtaining dynamic and static classification results; for the same oil/gas well there is xi＝x’i(ii) a Then, di＝|yi-y’i|。
(3) Calculating the mode of autocorrelation distance, and combining the oil/gas reservoir development practice to set the self-phaseDistance of turn off diPoints which are larger than a, namely points of which the difference value of the dynamic and static classification results exceeds the interval (-a, a) belong to abnormal points;
(4) and (4) obtaining an abnormal point with the difference value of the dynamic classification result and the static classification result smaller than-a, namely the well with low yield and low efficiency.
The invention has the beneficial effects that:
(1) introducing the yield contribution rate as a dynamic classification index, determining a class division point of a yield accumulative contribution rate distribution curve by using slope change rates (second-order partial derivatives of the yield accumulative contribution rate of the oil/gas well) of any two continuous points of the yield accumulative contribution rate distribution curve, and performing oil/gas well dynamic classification; the method fully considers the difference of the oil/gas well yield and the contribution rate thereof, so that the result of the dynamic classification of the oil/gas well is more objective and accords with the reality.
(2) In order to consider the interlayer interference situation in the oil/gas well exploitation process, 4 conventional static geological evaluation parameters such as effective sand thickness, porosity, permeability, gas saturation and the like are selected, the number of the commingled sand bodies in the longitudinal direction is simultaneously introduced to serve as a static judgment set, an oil/gas well static classification prediction model is established by adopting an entropy weight-ideal point method, the storage capacity and oil and gas production/capacity of a storage layer are reflected, the interlayer interference situation in the oil/gas well exploitation process is fully considered, and the classification result is closer to the oil/gas field development reality.
(3) And (3) based on 2 data sequences formed by the dynamic and static classification results of the oil/gas wells, judging abnormal points by solving the autocorrelation distance and the mode thereof and combining the development practice of the oil/gas reservoir, and performing low-yield and low-efficiency well optimization. The dynamic and static classification results are organically fused fully and objectively, and the optimal reliability of the low-yield and low-efficiency well can be further improved.
Drawings
FIG. 1 is a flow chart of a method for identifying a low-yield and low-efficiency well according to the present invention.
FIG. 2 is a graph of a cumulative contribution rate profile of gas well production.
Fig. 3 is a graph of a slope distribution for two consecutive points.
FIG. 4 is a difference distribution diagram of the gas well dynamic and static classification results.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
The method for judging the low-yield and low-efficiency well comprises the following steps:
step one, counting the yield of a single well based on the production dynamic data of 30 gas wells in a research area, and sequencing and numbering the single well according to the yield of the single well from large to small;
step two, calculating the yield contribution rate of the single well
The yield contribution rate refers to the cumulative yield of a single well in the research area as a percentage of the cumulative yield of the whole block in a certain time.
Figure BDA0003404813180000081
In the formula, thetaiThe yield contribution rate of the ith well; qiCumulative production for the ith well; qtThe production is accumulated for all wells in the zone.
Step three, drawing a gas well yield accumulated contribution rate distribution curve, as shown in figure 2
(1) And on the basis of the yield contribution rate of the single well obtained in the step two, sequentially calculating the yield accumulated contribution rate of the gas wells by taking the well with the maximum yield of the single well as a base point according to the sequence number of the well in the step one.
(2) The number of the oil/gas well is represented by an abscissa, the accumulated contribution rate of the gas well is represented by an ordinate, and a distribution curve of the accumulated contribution rate of the gas well is drawn; during drawing, the wells with small production are accumulated one by taking the well with the maximum production of the single well as a base point.
Fourthly, dynamically classifying the gas wells by using class division points of the gas well yield accumulative contribution rate distribution curve;
when the yield contribution rate of the ith gas well obviously changes, an obvious inflection point appears on a yield accumulation contribution rate distribution curve, the inflection point is called as a class segmentation point and is used for dynamically classifying the gas wells, and the specific implementation steps of the method comprise the following steps:
(1) the research area has 30 gas wells, and p is ═ p1(x1,y1),p2(x2，y2)，…，Pn(xnlyn)]A set of points forming a cumulative contribution profile for gas well production, and wherein a point may be represented as P (i) ═ Pi(xi，yi) (i is more than or equal to 1 and less than or equal to 30); calculating the slope k of a straight line formed by any two continuous points in sequencej(j ═ 1, 2, …, 29), the rate of change of the cumulative contribution rate of gas well production (first order partial derivative of the cumulative contribution rate of oil/gas well production).
(2) In consideration of the difference of gas well production and the contribution rate thereof, introducing slope change rates of any two continuous points of a gas well production cumulative contribution rate distribution curve, wherein different slope change rates represent different types of gas wells, and determining that the gas well production cumulative contribution rate distribution curve has 3 class division points (figure 3).
(3) According to 3 class division points of a gas well yield accumulative contribution rate distribution curve, corresponding yield accumulative contribution rates are 50.0%, 75.0% and 96.0%, and 30 gas wells in a research area are divided into 4 classes (figure 2 and table 1), namely, a class I (high yield well), a class II (medium yield well), a class III (low yield well) and a class IV (ultra-low yield well).
And step five, in order to consider the interlayer interference condition in the gas well exploitation process, except for selecting 4 conventional static geological evaluation parameters such as effective sand body thickness, porosity, permeability, gas saturation and the like, introducing the number of longitudinally co-production sand bodies as evaluation indexes, and performing gas well static classification (I, II, III and IV) by adopting an entropy weight-ideal point method.
(1) Construction of an evaluation matrix
Figure BDA0003404813180000091
(2) And adopting a linear change method to carry out standardization processing on each index. Taking the optimum value of each index
Figure BDA0003404813180000092
Is subjected to standardization treatmentThen, a new evaluation matrix is obtained as follows:
Figure BDA0003404813180000101
(3) calculating the entropy value of each evaluation index as follows:
ej＝[0.965，0.953，0.945，0.894，0.981]T(j＝1,2,3,4,5)
(4) and calculating the weight of each evaluation index as follows:
wj＝[0.133，0.181，0.208，0.405，0.072]T(j＝1,2,3,4,5)
(5) the evaluation indexes can be divided into two categories of positive indexes and negative indexes, the effective sand body thickness, porosity, permeability and gas saturation belong to the positive indexes, and the vertical combined production sand body number belongs to the negative indexes.
(6) The ideal point closeness is calculated as shown in table 1.
TABLE 1 results of dynamic and static classification of gas wells in the research area
Figure BDA0003404813180000102
Figure BDA0003404813180000111
(7) And (3) performing static classification on the gas wells according to a normal distribution principle based on the dynamic classification result of the gas wells, and referring to table 1.
And step six, considering the actual gas reservoir development based on the dynamic and static classification results of the gas wells, introducing autocorrelation distance and mode thereof to judge abnormal value points, and optimizing the low-yield and low-efficiency gas wells.
(1) Based on the gas well dynamic and static classification results, 2 data sequences, namely a dynamic classification result sequence Z are formedi(xi，yi) And static Classification result sequence Z'i(x’i，y’i)。
(2) Finding Zi(xi，yi)、Z’i(x’i，y’i) The autocorrelation distance between two points has values distributed between (0, 2).
(3) The mode of the autocorrelation distance is found to be 1, and the autocorrelation distance (d) is set in consideration of the actual oil/gas reservoir developmenti) The points larger than 1, namely the points where the difference value of the dynamic and static classification results exceeds the interval (-1, 1), belong to abnormal points.
(4) And 4 abnormal points (gas wells No. 11, 25, 26 and 27) with the difference value of the dynamic classification result and the static classification result smaller than-1 are taken as low-yield and low-efficiency gas wells (figure 4).
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can make modifications and equivalents to the embodiments of the present invention without departing from the spirit and scope of the present invention, which is set forth in the claims of the present application.

Claims (5)

1. A method for distinguishing a low-yield and low-efficiency well is characterized by comprising the following steps:
step one, counting the accumulated yield of a single well based on the production dynamic data of a research area, and sequencing and numbering the accumulated yields of the single well in the descending order;
step two, calculating the yield contribution rate of the single well;
step three, drawing an oil/gas well yield accumulated contribution rate distribution curve;
step four, performing oil/gas well dynamic classification by using class division points of the oil/gas well yield accumulation contribution rate distribution curve;
the class division points in the fourth step are as follows: when the yield contribution rate of the ith oil/gas well obviously changes, an obvious inflection point appears on a yield accumulated contribution rate distribution curve, and the inflection point is called as a class segmentation point and used for dynamically classifying the oil/gas wells, and the specific implementation steps of the method comprise:
(1) if there are n oil/gas wells in the area under study, n>3, P is ═ P1(x1，y1)，P2(x2，y2)，…，Pn(xn，yn)]A set of points forming a cumulative contribution profile for oil/gas well production, and wherein a point may be represented as P (i) ═ Pi(xi，yi) (i is more than or equal to 1 and less than or equal to n); calculating the slope k of a straight line formed by any two continuous points in sequencej(j ═ 1, 2, …, n-1), the rate of change of the cumulative contribution rate of oil/gas well production;
(2) in consideration of the difference of the oil/gas well yield and the contribution rate thereof, introducing the slope change rate of any two continuous points of a yield accumulative contribution rate distribution curve, namely the second-order partial derivative of the oil/gas well yield accumulative contribution rate, wherein different slope change rates represent different types of oil/gas wells, and thus determining the class division point of the oil/gas well yield accumulative contribution rate curve;
(3) dividing the oil/gas wells in the research area into different types according to class division points of the oil/gas well yield accumulation contribution rate distribution curve;
step five, besides selecting conventional static geological evaluation parameters, introducing the number of the commingled production sands in the longitudinal direction as an evaluation index, and performing static classification on the oil/gas well by adopting an entropy weight-ideal point method; the conventional static geological evaluation parameters comprise effective sand body thickness, porosity, permeability and oil/gas saturation;
and step six, introducing autocorrelation distance and mode thereof based on the results of dynamic classification and static classification of the oil/gas wells, considering the actual development of the oil/gas reservoir, judging abnormal value points, and optimizing the low-yield low-efficiency gas well.
2. The method for discriminating a low yield and low efficiency well according to claim 1, wherein the second step comprises the following steps:
the yield contribution rate refers to the percentage of the accumulated yield of a single well in a research area in the whole block within a certain time;
Figure FDA0003404813170000021
in the formula, thetaiThe yield contribution rate of the ith well; qiCumulative production for the ith well; qtThe production is accumulated for all wells in the zone.
3. The method for discriminating a low yield and low efficiency well according to claim 1, wherein the third step comprises the following steps:
(1) on the basis of the yield contribution rate of the single well obtained in the step two, sequentially calculating the yield accumulative contribution rate of the oil/gas well by taking the well with the maximum accumulative yield of the single well as a base point according to the sequence number of the well in the step one;
(2) the number of the oil/gas well is represented by an abscissa, the accumulated contribution rate of the yield is represented by an ordinate, and a distribution curve of the accumulated contribution rate of the yield of the oil/gas well is drawn; during drawing, the wells with the largest cumulative yield of the single well are taken as base points and accumulated one by one towards the wells with small cumulative yields.
4. The method for discriminating a low yield and low efficiency well according to claim 1, wherein the concrete implementation steps of the fifth step comprise:
(1) establishing an evaluation matrix;
for the oil gas/well R to be evaluated, n evaluation indexes are set, the n indexes are regarded as n target functions of the oil gas/well R to be evaluated, and a vector function F (x) is made to be [ f [ ]1(x)，f2(x)，...，fn(x)]The corresponding weight is W1、W2、…、WnTo evaluate the hydrocarbon/well R at the objective function fi(x) The lower value is xi(ii) a Its index matrix is
X＝{x1，x2，...，xn}[W1，W2，...，Wn]T
(2) Determining the weight of each index;
firstly, adopting a linear change method to carry out standardization processing on each index; taking the optimum value of each index
Figure FDA0003404813170000031
After normalization, a new evaluation matrix is obtained as follows:
Figure FDA0003404813170000032
secondly, setting the entropy value of the jth evaluation index as:
Figure FDA0003404813170000033
in the formula (I), the compound is shown in the specification,
Figure FDA0003404813170000034
k＞0；ej≥0；
③ f is less than or equal to 0ijE is not more than 1, so that e is not less than 0jLess than or equal to 1, and obtaining the total entropy of all evaluation indexes as follows:
Figure FDA0003404813170000035
fourthly, defining the deviation degree of the index j as dj＝1-ejThen, the entropy weight of the evaluation index j is:
Figure FDA0003404813170000036
(3) determining an ideal point and an anti-ideal point;
the evaluation indexes are divided into two main types, namely positive indexes and negative indexes: the larger the positive index, i.e., the index value, the better, and the smaller the reverse index, i.e., the index value, the better; assuming that the index changes monotonously, thereby defining an ideal point and an anti-ideal point;
when the index is a positive index, there are:
Figure FDA0003404813170000041
when the index is an inverse index, there are:
Figure FDA0003404813170000042
in the formula (f)i *(+)、fi *(-) is a positive ideal point vector and a negative ideal point vector of the ith index in the n indexes influencing the evaluation object respectively; f. ofi(x) Is the actual value of the index; 1, 2, …, n;
(4) calculating the closeness of the ideal points;
the closer the index dissociates from the positive ideal point, the further away from the negative ideal point, the better the solution, thus defining a modulus in this n-dimensional space: i f (x) -f*(+)||→min，||f(x)-f*(-) - | → max, using euclidean distances, i.e. the relative distances to the positive and negative ideal points are:
Figure FDA0003404813170000043
Figure FDA0003404813170000044
the calculation formula of the ideal point closeness is T ═ D2/(D1+D2) T belongs to the interval [0, 1]Evaluating the condition of the object by using an index T, wherein the larger the T is, the closer the representative point is to the normal ideal point is, and the farther the representative point is from the anti-ideal point is;
(5) and performing static classification on the oil/gas wells according to a normal distribution principle based on the dynamic classification result of the oil/gas wells.
5. The method for discriminating a low yield and low efficiency well according to claim 1, wherein the concrete implementation steps of the sixth step comprise:
(1) based on the dynamic and static classification results of the oil/gas well, 2 data sequences are formed, namely dynamic classificationClass result sequence Zi(xi，yi) And static Classification result sequence Z'i(x’i，y’i)；
(2) Finding Zi(xi，yi)、Z’i(x’i，y’i) The autocorrelation distance between two points is calculated by the formula:
Figure FDA0003404813170000051
wherein, yi、y’iRespectively obtaining dynamic and static classification results; for the same oil/gas well there is xi＝x’i(ii) a Then, di＝|yi-y’i|；
(3) Calculating the mode of the autocorrelation distance, and setting the autocorrelation distance d by combining the oil/gas reservoir development practiceiPoints which are larger than a, namely points of which the difference value of the dynamic and static classification results exceeds the interval (-a, a) belong to abnormal points;
(4) and (4) obtaining an abnormal point with the difference value of the dynamic classification result and the static classification result smaller than-a, namely the well with low yield and low efficiency.
CN202111509841.1A 2021-12-10 2021-12-10 Method for distinguishing low-yield and low-efficiency well Pending CN114169616A (en)

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