CN112412444B - Injection and production communication strength determining method and device, computer equipment and storage medium - Google Patents

Injection and production communication strength determining method and device, computer equipment and storage medium Download PDF

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Publication number
CN112412444B
CN112412444B CN202011314804.0A CN202011314804A CN112412444B CN 112412444 B CN112412444 B CN 112412444B CN 202011314804 A CN202011314804 A CN 202011314804A CN 112412444 B CN112412444 B CN 112412444B
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water injection
oil extraction
layer
oil
time sequence
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CN112412444A (en
Inventor
孙琦
倪天禄
徐思远
王晴
李海甫
王艳丽
孙海燕
李博文
郭振
王娜娜
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Petrochina Co Ltd
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Petrochina Co Ltd
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E21B43/14—Obtaining from a multiple-zone well
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E21B43/16—Enhanced recovery methods for obtaining hydrocarbons
    • E21B43/20—Displacing by water
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
    • Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping

Abstract

The application discloses a method, a device, computer equipment and a storage medium for determining injection and production communication strength, and belongs to the field of petroleum exploitation. In the embodiment of the application, the oil extraction-oil extraction time series of a plurality of water injection layers in the water injection well and the oil extraction-oil extraction time series of a plurality of oil extraction layers in the oil extraction well can be directly obtained through the water injection quantity split model and the oil extraction split model. Based on the similarity between the oil recovery-oil recovery time series of the plurality of water injection layers and the oil recovery-oil recovery time series of the plurality of oil recovery layers, the injection and production communication strength between the plurality of water injection layers and the plurality of oil recovery layers can be obtained quickly. Through the technical scheme, the process of determining the injection-production communication strength does not need to stop production, complicated operation and additional construction operation are not needed, the efficiency of determining the injection-production communication strength is higher, and the cost is lower.

Description

Injection and production communication strength determining method and device, computer equipment and storage medium
Technical Field
The present disclosure relates to the field of oil exploitation, and in particular, to a method and apparatus for determining injection and production communication strength, a computer device, and a storage medium.
Background
The injection and production communication strength refers to the communication condition between a water injection well and an oil production well in water flooding oil reservoir development. In oilfield practical production, injection and production communication is a very difficult but important problem. The accurate judgment of the injection and production communication condition can provide basis for the description of the distribution of residual oil and the formulation of an oil field development scheme, has a certain guiding effect on the production adjustment and the stable oil and water control of the oil field, and has important significance on improving the recovery ratio of crude oil of a water-drive reservoir.
In the related technology, the research on the injection and production communication conditions mainly comprises tracer testing, multi-well test analysis, geochemistry methods and the like, the methods are often complex in operation and require additional construction operation, normal production operation of an oil field can be influenced in the implementation process, and the efficiency of determining the injection and production communication intensity is low.
Disclosure of Invention
The embodiment of the application provides a method, a device, computer equipment and a storage medium for determining injection and production communication intensity, which can improve the efficiency of determining the injection and production communication intensity. The technical scheme is as follows:
in one aspect, a method for determining injection and production communication strength is provided, the method comprising:
acquiring a water injection quantity-water injection time sequence of a water injection well and an oil extraction-oil extraction time sequence of an oil extraction well;
Inputting the water injection quantity-water injection time sequence of the water injection well and the permeability of each water injection layer in the water injection well into a water injection quantity splitting model, and outputting the water injection quantity-water injection time sequence of each water injection layer by the water injection quantity splitting model, wherein the water injection quantity splitting model is obtained by training according to the water injection quantity-water injection time sequence of at least one sample water injection well, the sample water injection quantity-water injection time sequence of each sample water injection layer in the at least one sample water injection well and the sample permeability;
inputting the oil extraction-oil extraction time sequence of the oil extraction well and the permeability of each oil extraction layer in the oil extraction well into an oil extraction splitting model, and outputting the oil extraction-oil extraction time sequence of each oil extraction layer by the oil extraction splitting model, wherein the oil extraction splitting model is obtained by training according to the oil extraction-oil extraction time sequence of at least one sample oil extraction well, the sample oil extraction-oil extraction time sequence and the sample permeability of each sample oil extraction layer in the at least one sample oil extraction well;
inputting the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer into a correlation determination model, and outputting a correlation coefficient between the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer through the correlation determination model;
And determining the injection and production communication strength between each water injection layer and each oil extraction layer based on the correlation coefficient.
In one possible implementation manner, the inputting the water injection amount-water injection time sequence of the water injection well and the permeability of each water injection layer in the water injection well into the water injection amount splitting model, and outputting the water injection amount-water injection time sequence of each water injection layer by the water injection amount splitting model includes:
inputting a water injection quantity-water injection time sequence of the water injection well and the permeability of each water injection layer in the water injection well into a water injection quantity splitting model, and obtaining a first seepage limit of each water injection layer based on the permeability of each water injection layer through the water injection quantity splitting model, wherein the first seepage limit is the ratio between the permeability of each water injection layer and the highest permeability in each water injection layer;
obtaining water injection distribution proportion of each water injection layer based on the permeability of each water injection layer and the seepage limit of each water injection layer through the water injection amount splitting model;
and obtaining the water injection quantity-water injection time sequence of each water injection layer based on the water injection distribution proportion and the water injection quantity-water injection time sequence of the water injection well.
In one possible embodiment, the inputting the oil recovery-oil recovery time series of the oil recovery well and the permeability of each oil recovery layer in the oil recovery well into an oil recovery split model, and outputting the oil recovery-oil recovery time series of each oil recovery layer by the oil recovery split model includes:
inputting the oil extraction-oil extraction time sequence of the oil extraction well and the permeability of each oil extraction layer in the oil extraction well into an oil extraction splitting model, and obtaining a second seepage limit of each oil extraction layer based on the permeability of each oil extraction layer through the oil extraction splitting model, wherein the second seepage limit is the ratio between the permeability of each oil extraction layer and the highest permeability in each oil extraction layer;
obtaining the oil yield ratio of each oil recovery layer based on the permeability of each oil recovery layer and the seepage limit of each oil recovery layer through the oil recovery splitting model;
and obtaining the oil extraction-oil extraction time series of each oil extraction layer based on the oil extraction proportion and the oil extraction-oil extraction time series of the oil extraction well.
In one possible embodiment, the inputting the water injection amount-water injection time series of each water injection layer and the oil recovery-oil recovery time series of each oil recovery layer into a correlation determination model, and outputting the correlation coefficient between the water injection amount-water injection time series of each water injection layer and the oil recovery-oil recovery time series of each oil recovery layer through the correlation determination model includes:
Inputting the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer into a correlation determination model, and acquiring a water injection quantity-oil extraction difference matrix between each water injection layer and each oil extraction layer through the correlation determination model, wherein the numerical value in the water injection quantity-oil extraction difference matrix is the difference value between the water injection quantity of each water injection layer and the oil extraction of each oil extraction layer;
and obtaining a correlation coefficient between the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer based on the water injection quantity-oil extraction difference matrix through the correlation determination model.
In one possible implementation manner, the obtaining, by the correlation determination model, a correlation coefficient between the water injection amount-water injection time sequence of each water injection layer and the oil recovery-oil recovery time sequence of each oil recovery layer based on the water injection amount-oil recovery difference matrix includes:
obtaining a target path taking the upper left corner of the water injection quantity-oil extraction difference matrix as a starting point and the lower right corner of the water injection quantity-oil extraction difference matrix as an end point through the correlation determination model, wherein the target path is a path with the smallest sum of the passing numerical values;
And determining the sum of the values of the target path as the correlation coefficient.
In one possible embodiment, the determining the injection and production communication strength between the respective water injection layer and the respective oil recovery layer based on the correlation coefficient comprises:
and carrying out normalization processing on the correlation coefficient, and determining the injection and production communication strength between each water injection layer and each oil recovery layer.
In one possible embodiment, the distance between the water injection well and the oil recovery well is less than or equal to a distance threshold.
In one aspect, an injection and production communication strength determining device is provided, the device comprising:
the acquisition module is used for acquiring a water injection quantity-water injection time sequence of the water injection well and an oil extraction-oil extraction time sequence of the oil extraction well;
the first input module is used for inputting the water injection quantity-water injection time sequence of the water injection well and the permeability of each water injection layer in the water injection well into a water injection quantity splitting model, outputting the water injection quantity-water injection time sequence of each water injection layer by the water injection quantity splitting model, wherein the water injection quantity splitting model is obtained by training according to the water injection quantity-water injection time sequence of at least one sample water injection well, the sample water injection quantity-water injection time sequence of each sample water injection layer in the at least one sample water injection well and the sample permeability;
The second input module is used for inputting the oil extraction-oil extraction time sequence of the oil extraction well and the permeability of each oil extraction layer in the oil extraction well into an oil extraction split model, and outputting the oil extraction-oil extraction time sequence of each oil extraction layer by the oil extraction split model, wherein the oil extraction split model is obtained by training according to the oil extraction-oil extraction time sequence of at least one sample oil extraction well, the sample oil extraction-oil extraction time sequence of each sample oil extraction layer in the at least one sample oil extraction well and the sample permeability;
the third input module is used for inputting the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer into a correlation determination model, and outputting a correlation coefficient between the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer through the correlation determination model;
and the injection and production communication strength determining module is used for determining the injection and production communication strength between each injection and production layer and each oil extraction layer based on the correlation coefficient.
In one possible implementation manner, the first input module is configured to input a water injection amount-water injection time sequence of the water injection well and a permeability of each water injection layer in the water injection well into a water injection amount splitting model, and obtain, by using the water injection amount splitting model, a first seepage limit of each water injection layer based on the permeability of each water injection layer, where the first seepage limit is a ratio between the permeability of each water injection layer and a highest permeability in each water injection layer; obtaining water injection distribution proportion of each water injection layer based on the permeability of each water injection layer and the seepage limit of each water injection layer through the water injection amount splitting model; and obtaining the water injection quantity-water injection time sequence of each water injection layer based on the water injection distribution proportion and the water injection quantity-water injection time sequence of the water injection well.
In one possible implementation, the second input module is configured to input the oil recovery-oil recovery time sequence of the oil recovery well and the permeability of each oil recovery layer in the oil recovery well into an oil recovery split model, and obtain, based on the permeability of each oil recovery layer, a second seepage limit of each oil recovery layer by using the oil recovery split model, where the second seepage limit is a ratio between the permeability of each oil recovery layer and the highest permeability in each oil recovery layer; obtaining the oil yield ratio of each oil recovery layer based on the permeability of each oil recovery layer and the seepage limit of each oil recovery layer through the oil recovery splitting model; and obtaining the oil extraction-oil extraction time series of each oil extraction layer based on the oil extraction proportion and the oil extraction-oil extraction time series of the oil extraction well.
In one possible implementation manner, the third input module is configured to input the water injection amount-water injection time sequence of each water injection layer and the oil recovery-oil recovery time sequence of each oil recovery layer into a correlation determination model, and obtain a water injection amount-oil recovery difference matrix between each water injection layer and each oil recovery layer through the correlation determination model, where a value in the water injection amount-oil recovery difference matrix is a difference value between the water injection amount of each water injection layer and the oil recovery of each oil recovery layer; and obtaining a correlation coefficient between the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer based on the water injection quantity-oil extraction difference matrix through the correlation determination model.
In one possible implementation manner, the third input module is configured to obtain, through the correlation determination model, a target path starting from an upper left corner of the water injection quantity-oil extraction difference matrix and ending at a lower right corner of the water injection quantity-oil extraction difference matrix, where the target path is a path with a minimum sum of values passing through; and determining the sum of the values of the target path as the correlation coefficient.
In one possible implementation manner, the injection and production communication strength determining module is configured to normalize the correlation coefficient to determine injection and production communication strength between each water injection layer and each oil recovery layer.
In one possible embodiment, the distance between the water injection well and the oil recovery well is less than or equal to a distance threshold.
In one aspect, a computer device is provided that includes one or more processors and one or more memories having stored therein at least one program code loaded and executed by the one or more processors to implement the injection and production communication strength determination method.
In one aspect, a computer readable storage medium having at least one program code stored therein, the program code loaded and executed by a processor to implement the injection and production communication strength determination method is provided.
In the embodiment of the application, the oil extraction-oil extraction time series of a plurality of water injection layers in the water injection well and the oil extraction-oil extraction time series of a plurality of oil extraction layers in the oil extraction well can be directly obtained through the water injection quantity split model and the oil extraction split model. Based on the similarity between the oil recovery-oil recovery time series of the plurality of water injection layers and the oil recovery-oil recovery time series of the plurality of oil recovery layers, the injection and production communication strength between the plurality of water injection layers and the plurality of oil recovery layers can be obtained quickly. Through the technical scheme, the process of determining the injection-production communication strength does not need to stop production, complicated operation and additional construction operation are not needed, the efficiency of determining the injection-production communication strength is higher, and the cost is lower.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for determining injection and production communication strength according to an embodiment of the present application;
FIG. 2 is a flowchart of a method for determining the communication strength of injection and production according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an injection and production communication strength determining device according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a server according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
In the embodiment of the application, the water injection quantity split model and the oil recovery split model are related, and in order to more clearly explain the embodiment of the application, first, training methods of the water injection quantity split model and the oil recovery split model are described. In addition, in the training of the water injection amount splitting model and the oil extraction amount splitting model, a terminal may be used as an execution body, or a server may be used as an execution body.
In one possible implementation, the training of the split model of the water injection amount by the terminal includes two processes of sample preparation and model training, and the two processes are respectively described below.
In the sample preparation process, the terminal can collect a sample water injection amount-water injection time sequence of the sample water injection wells, a sample water injection amount-water injection time sequence of each sample water injection layer in the sample water injection wells, and a permeability of each sample water injection layer, and optionally, the number of the sample water injection wells is plural, and only one sample water injection well will be described as an example.
For a sample water injection rate-water injection time sequence of the sample water injection well, the terminal can acquire sample water injection rates of the sample water injection well at different times, and the acquired sample water injection rates are arranged according to the time corresponding to the sample water injection rates to obtain the sample water injection rate-water injection rate of the sample water injection wellTime series. For example, for a sample injection well, the terminal obtains 5 sample injection volumes 100m on the same day 3 /min、80m 3 /min、90m 3 /min、105m 3 /min and 95m 3 At the same time, the water injection rate of 5 samples is 100m 3 /min、80m 3 /min、90m 3 /min、105m 3 /min and 95m 3 The time corresponding to each of the min is 8: 20. 9: 20. 10: 20. 11:20 and 12:20, the terminal can obtain a sample water injection amount-water injection amount time sequence (100, 80, 90, 105, 95) of the sample water injection well.
For the sample water injection amount-water injection time series of each sample water injection layer in the sample water injection well, the terminal can be obtained in any one of the following ways.
In the mode 1, in the process of injecting water into a sample injection well, a technician adds isotopes into injected water, the technician measures the radioactivity intensity of each sample injection layer in the sample injection well after the isotopes are injected through a radioactivity intensity measuring device, and the technician sends the measured radioactivity intensity to a terminal. And the terminal obtains a sample water injection quantity-water injection time sequence of each sample water injection layer according to the radioactivity intensity.
For example, if the terminal obtains a sample injection rate-injection rate time sequence of the sample injection well (100, 80, 90, 105, 95). The sample water injection well comprises two sample water injection layers, wherein the corresponding radioactivity intensity of one sample water injection layer is 2 bevac (Bq), the corresponding radioactivity intensity of the other sample water injection layer is 3Bq, and the terminal can determine the water absorption ratio of the two sample water injection layers according to the corresponding radioactivity intensities of the two sample water injection layers respectively, namely, the water absorption ratio of one sample water injection layer is 2/(2+3) =0.4, and the water absorption ratio of the other sample water injection layer is 3/(2+3) =0.6. The terminal obtains a sample water injection rate-water injection rate time sequence (40, 32, 36, 42, 38) of one sample water injection layer and a sample water injection rate-water injection rate time sequence (60, 48, 54, 63, 57) of the other sample water injection layer according to the water absorption ratios of the two sample water injection layers of 0.4 and 0.6 and the sample water injection rate-water injection rate time sequence (100, 80, 90, 105, 95) of the sample water injection well.
Mode 2, a technician can put a turbine flowmeter with a positioning device into the sample injection well, and the turbine flowmeter with the positioning device can communicate with a terminal. The terminal can determine the current position of the turbine flowmeter through the positioning device, and the measured sample water injection layer is determined according to the position of the turbine flowmeter. The terminal can obtain the flow rate of water in each sample water injection layer through the turbine flowmeter. The terminal can obtain a sample water injection amount-water injection amount time sequence of each sample water injection layer based on the flow rate of water in each sample water injection layer and the cross section area of the sample water injection layer.
For the permeability of each sample water injection layer, the terminal can determine the rock stratum type corresponding to each sample water injection layer according to the logging curve. And the terminal acquires the permeability corresponding to each sample water injection layer based on the rock stratum type corresponding to each sample water injection layer. Of course, the formation type corresponding permeability may be entered in advance by a technician or obtained from a server by a terminal, which is not limited in the embodiments of the present application.
In the model training process, the terminal can input a sample water injection quantity-water injection quantity time sequence of the sample water injection well and the permeability of each sample water injection layer into a first model, and the first model predicts the permeability of each sample water injection layer to obtain the predicted water absorption ratio corresponding to each sample water injection layer. And outputting the predicted water injection quantity-water injection quantity time sequence of each sample water injection layer by the terminal through the first model based on the sample water injection quantity-water injection quantity time sequence of the sample water injection well and the predicted water absorption proportion corresponding to each sample water injection layer. And the terminal adjusts model parameters of the first model according to the difference information between the predicted water injection quantity-water injection quantity time sequence of each sample water injection layer and the sample water injection quantity-water injection quantity time sequence of each sample water injection layer until the loss function of the first model converges to the objective function value or the iteration number of the first model reaches the objective number of times, and the training of the first model is stopped. The terminal obtains the first model at this time as a water injection quantity split model, wherein the objective function value and the objective frequency are set by a technician according to actual conditions, and the embodiment of the application does not limit the method.
It should be noted that the split model of the water injection rate can be selected from linear regression (Linear Regression), logistic regression (Logistic Regression), linear discriminant analysis (Linear Discriminant Analysis), classification and regression tree model (Classification and Regression Trees), naive BayesBayes), K nearest neighbor (K-Nearest Neighbors), learning vector quantization (Learning Vector Quantization), support vector machine (Support Vector Machines), random Forest (Random Forest), etc., which is not limited in this embodiment of the present application.
Optionally, the terminal can also train two or more models simultaneously based on sample data acquired in a sample preparation process, and can test the trained multiple models, and determine a model with the best water injection amount splitting effect from the multiple models as a water injection amount splitting model in a subsequent use process, wherein the water injection amount splitting effect is best that the similarity between a predicted water injection amount-water injection time sequence of each sample water injection layer and a sample water injection amount-water injection time sequence of each sample water injection layer predicted by the model is highest.
For example, if the sample water injection amount-water injection time sequence of each of 100 sample water injection wells and 100 sample water injection layers in the sample water injection wells is obtained as a training sample in the data preparation process, the terminal can use the sample water injection amount-water injection time sequence of each of 80 sample water injection wells and the sample water injection layers in the sample water injection wells as a training set, and use the sample water injection amount-water injection time sequence of each of the remaining 20 sample water injection wells and the sample water injection layers in the sample water injection wells as a test set. The terminal trains three models based on the training set, and respectively marks as a model A, a model B and a model C. The terminal can test the model A, the model B and the model C respectively by adopting a test set to obtain the similarity between the predicted water injection amount-water injection time sequence of each sample water injection layer and the sample water injection amount-water injection time sequence of each sample water injection layer output by the model A, the model B and the model C. And the terminal determines the model with the highest similarity as a water injection quantity split model. Alternatively, the terminal can determine the similarity between the predicted water injection amount-water injection time series of each sample water injection layer and the sample water injection amount-water injection time series of each sample water injection layer by calculating the euclidean distance between the two sequences.
Under this kind of embodiment, the terminal can confirm the best model of water injection volume split effect from multiple type models and divide the model as the water injection volume split model, in the later in-process that adopts this water injection volume split model to split the water injection volume of water injection well, can reach better water injection volume split effect.
In one possible embodiment, the training of the oil recovery split model by the terminal includes two processes, sample preparation and model training, which are described separately below.
In the sample preparation process, the terminal is capable of collecting a sample oil-extraction time series of the sample oil-extraction well, a sample oil-extraction time series of each sample oil-extraction layer in the sample oil-extraction well, and a permeability of each sample oil-extraction layer, optionally, a plurality of sample oil-extraction wells, and only one sample oil-extraction well will be described as an example.
For a sample oil extraction-oil extraction time sequence of a sample oil extraction well, the terminal can acquire sample oil extraction of the sample oil extraction well at different times, and the acquired sample oil extraction is arranged according to the time corresponding to the sample oil extraction, so as to obtain the sample oil extraction-oil extraction time sequence of the sample oil extraction well. For example, for a sample oil well, the terminal obtains 5 sample oil recovery 90m of the same day 3 /min、70m 3 /min、80m 3 /min、100m 3 /min and 90m 3 Per min, at the same time, 5 samples were sampled with a fuel recovery of 90m 3 /min、70m 3 /min、80m 3 /min、100m 3 /min and 90m 3 The time corresponding to each of the min is 8: 20. 9: 20. 10: 20. 11:20 and 12:20, the terminal is capable of obtaining a sample oil recovery-oil recovery time series (90, 70, 80, 100, 90) for the sample oil recovery well.
For example, a technician can place a turbine flow meter with a locating device in the sample well, which can communicate with a terminal. The terminal can determine the current sample oil recovery layer measured by the turbine flowmeter through the positioning device, and the flow rate of water in each sample oil recovery layer can be obtained through the turbine flowmeter. The terminal can derive a sample oil recovery-oil recovery time series for each sample oil recovery based on the flow rate of water in each sample oil recovery layer and the cross-sectional area of the sample oil recovery layer.
For permeability of each sample reservoir, the terminal is able to determine the formation type for each sample reservoir from the log. And the terminal acquires the permeability corresponding to each sample oil extraction layer based on the rock stratum type corresponding to each sample oil extraction layer.
In the model training process, the terminal can input the sample oil extraction-oil extraction time sequence of the sample oil extraction well and the permeability of each sample oil extraction layer into a second model, and the second model predicts the permeability of each sample oil extraction layer to obtain the predicted oil extraction proportion corresponding to each sample oil extraction layer. And outputting the predicted oil recovery-oil recovery time sequence of each sample oil recovery layer based on the sample oil recovery-oil recovery time sequence of the sample oil recovery well and the predicted oil recovery proportion corresponding to each sample oil recovery layer by the terminal through the second model. And the terminal adjusts model parameters of the second model according to the difference information between the predicted oil recovery-oil recovery time sequence of each sample oil recovery layer and the sample oil recovery-oil recovery time sequence of each sample oil recovery layer until the loss function of the second model converges to the objective function value or the iteration number of the second model reaches the objective number of times, and the training of the second model is stopped. And the terminal acquires the second model at the moment as an oil production split model.
It should be noted that, the oil recovery splitting model may be selected from linear regression (Linear Regression), logistic regression (Logistic Regression), linear discriminant analysis (Linear Discriminant Analysis), classification and regression tree model (Classification and Regression Trees), naive Bayes (Naive Bayes), K nearest neighbor (K-Nearest Neighbors), learning vector quantization (Learning Vector Quantization), support vector machine (Support Vector Machines), random forest (Bagging and Random Forest), and the like, which are not limited in this embodiment of the present application.
Alternatively, the terminal can also train two or more models simultaneously based on sample data acquired in the sample preparation process, and the terminal can test the trained models, and determine a model with the best oil extraction splitting effect from the models as an oil extraction splitting model in the subsequent use process, wherein the oil extraction splitting effect preferably means that the similarity between a predicted oil extraction-oil extraction time sequence of each sample oil extraction layer and a sample oil extraction-oil extraction time sequence of each sample oil extraction layer predicted by the model is highest.
For example, if the sample oil recovery-oil recovery time series of 100 sample oil recovery wells and each sample oil recovery layer in 100 sample oil recovery wells are obtained as training samples in the data preparation process, the terminal can use the sample oil recovery-oil recovery time series of 80 sample oil recovery wells and each sample oil recovery layer in the sample oil recovery wells as training sets and the sample oil recovery-oil recovery time series of the remaining 20 sample oil recovery wells and each sample oil recovery layer in the sample oil recovery wells as test sets. The terminal trains three models based on the training set, and respectively marks as a model D, a model E and a model F. The terminal can test the model D, the model E and the model F respectively by adopting a test set to obtain the similarity between the predicted oil extraction-oil extraction time sequence of each sample oil extraction layer and the sample oil extraction-oil extraction time sequence of each sample oil extraction layer output by the model D, the model E and the model F. And the terminal determines the model with the highest similarity as the oil extraction split model. Alternatively, the terminal can determine the similarity between the predicted oil recovery-oil recovery time series for each sample oil recovery layer and the sample oil recovery-oil recovery time series for each sample oil recovery layer by calculating the euclidean distance between the two sequences.
Under the implementation mode, the terminal can determine the model with the best oil extraction splitting effect from various models as the oil extraction splitting model, and can achieve better oil extraction splitting effect in the process of splitting the oil extraction of the oil extraction well by adopting the oil extraction splitting model later.
It should be noted that, the water injection amount split model and the oil extraction split model are the same or different models, which is not limited in this embodiment of the present application.
Fig. 1 is a flowchart of a method for determining injection and production communication strength, referring to fig. 1, taking an execution body as a terminal as an example, where the method includes:
101. and the terminal acquires a water injection quantity-water injection time sequence of the water injection well and a oil extraction-oil extraction time sequence of the oil extraction well.
102. And the terminal inputs the water injection quantity-water injection time sequence of the water injection well and the permeability of each water injection layer in the water injection well into a water injection quantity splitting model, the water injection quantity-water injection time sequence of each water injection layer is output by the water injection quantity splitting model, and the water injection quantity splitting model is obtained by training according to the water injection quantity-water injection time sequence of at least one sample water injection well, the sample water injection quantity-water injection time sequence of each sample water injection layer in the at least one sample water injection well and the sample permeability.
103. The terminal inputs the oil extraction-oil extraction time sequence of the oil extraction well and the permeability of each oil extraction layer in the oil extraction well into an oil extraction splitting model, the oil extraction-oil extraction time sequence of each oil extraction layer is output by the oil extraction splitting model, and the oil extraction splitting model is obtained by training according to the oil extraction-oil extraction time sequence of at least one sample oil extraction well, the sample oil extraction-oil extraction time sequence of each sample oil extraction layer in the at least one sample oil extraction well and the sample permeability.
104. And the terminal inputs the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer into a correlation determination model, and outputs a correlation coefficient between the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer through the correlation determination model.
105. And the terminal determines the injection and production communication strength between each water injection layer and each oil extraction layer based on the correlation coefficient.
In the embodiment of the application, the oil extraction-oil extraction time series of a plurality of water injection layers in the water injection well and the oil extraction-oil extraction time series of a plurality of oil extraction layers in the oil extraction well can be directly obtained through the water injection quantity split model and the oil extraction split model. Based on the similarity between the oil recovery-oil recovery time series of the plurality of water injection layers and the oil recovery-oil recovery time series of the plurality of oil recovery layers, the injection and production communication strength between the plurality of water injection layers and the plurality of oil recovery layers can be obtained quickly. Through the technical scheme, the process of determining the injection-production communication strength does not need to stop production, complicated operation and additional construction operation are not needed, the efficiency of determining the injection-production communication strength is higher, and the cost is lower.
In one possible embodiment, inputting the water injection amount-water injection time sequence of the water injection well and the permeability of each water injection layer in the water injection well into the water injection amount splitting model, and outputting the water injection amount-water injection time sequence of each water injection layer by the water injection amount splitting model includes:
inputting the water injection quantity-water injection time sequence of the water injection well and the permeability of each water injection layer in the water injection well into a water injection quantity splitting model, and obtaining the first seepage range of each water injection layer based on the permeability of each water injection layer through the water injection quantity splitting model, wherein the first seepage range is the ratio between the permeability of each water injection layer and the highest permeability in each water injection layer.
And obtaining the water injection distribution proportion of each water injection layer based on the permeability of each water injection layer and the extremely poor seepage of each water injection layer through a water injection amount splitting model.
And obtaining the water injection quantity-water injection time sequence of each water injection layer based on the water injection distribution proportion and the water injection quantity-water injection time sequence of the water injection well.
In one possible embodiment, inputting the oil recovery-oil recovery time series of the oil recovery well and the permeability of each oil recovery layer in the oil recovery well into the oil recovery split model, outputting the oil recovery-oil recovery time series of each oil recovery layer from the oil recovery split model comprises:
And inputting the oil extraction-oil extraction time sequence of the oil extraction well and the permeability of each oil extraction layer in the oil extraction well into an oil extraction splitting model, and obtaining the second seepage extremely poor of each oil extraction layer based on the permeability of each oil extraction layer through the oil extraction splitting model, wherein the second seepage extremely poor is the ratio between the permeability of each oil extraction layer and the highest permeability in each oil extraction layer.
And obtaining the oil yield of each oil recovery layer based on the permeability of each oil recovery layer and the extremely poor seepage of each oil recovery layer through the oil recovery splitting model.
And obtaining the oil extraction quantity-oil extraction time sequence of each oil extraction layer based on the oil extraction proportion and the oil extraction quantity-oil extraction time sequence of the oil extraction well.
In one possible embodiment, inputting the water injection amount-water injection time series of each water injection layer and the oil recovery-oil recovery time series of each oil recovery layer into the correlation determination model, and outputting the correlation coefficient between the water injection amount-water injection time series of each water injection layer and the oil recovery-oil recovery time series of each oil recovery layer through the correlation determination model includes:
and inputting the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer into a correlation determination model, and acquiring a water injection quantity-oil extraction difference matrix between each water injection layer and each oil extraction layer through the correlation determination model, wherein the numerical value in the water injection quantity-oil extraction difference matrix is the difference value between the water injection quantity of each water injection layer and the oil extraction of each oil extraction layer.
And obtaining a correlation coefficient between the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer based on the water injection quantity-oil extraction difference matrix through a correlation determination model.
In one possible embodiment, obtaining, by the correlation determination model, a correlation coefficient between the water injection amount-water injection time series of each water injection layer and the oil recovery-oil recovery time series of each oil recovery layer based on the water injection amount-oil recovery difference matrix includes:
and obtaining a target path taking the upper left corner of the water injection quantity-oil extraction difference matrix as a starting point and the lower right corner of the water injection quantity-oil extraction difference matrix as an end point through a correlation determination model, wherein the target path is the path with the smallest sum of the passing numerical values.
The sum of the values traversed by the target path is determined as a correlation coefficient.
In one possible embodiment, determining the strength of the injection and production communication between each of the water injection layers and each of the oil recovery layers based on the correlation coefficients comprises:
and carrying out normalization processing on the correlation coefficient, and determining the injection and production communication intensity between each water injection layer and each oil extraction layer.
In one possible embodiment, the distance between the water injection well and the production well is less than or equal to a distance threshold.
Any combination of the above optional solutions may be adopted to form an optional embodiment of the present application, which is not described herein in detail.
Fig. 2 is a flowchart of a method for determining injection and production communication strength, referring to fig. 2, taking an execution body as a terminal as an example, where the method includes:
201. and the terminal acquires a water injection quantity-water injection time sequence of the water injection well and a oil extraction-oil extraction time sequence of the oil extraction well.
In one possible embodiment, a water injection pump is installed on the water injection well, and a water injection flow meter is installed on the water injection pump, and the water injection flow meter can acquire the water injection amount of water injected into the water injection well through the water injection pump in real time. The terminal can link to each other with the water injection flowmeter of installing on the water injection pump, acquires the water injection pump through the water injection flowmeter and carries out the water injection volume of water injection to the water injection well. And the terminal is used for injecting water into the water injection well based on the water injection pump, so as to obtain a water injection amount-water injection time sequence of the water injection well. Correspondingly, the oil extraction pump is arranged on the oil extraction well, the oil extraction flowmeter is arranged on the oil extraction pump, and the oil extraction flowmeter can acquire the oil extraction amount of oil extraction from the oil extraction well through the oil extraction pump in real time. The terminal can be connected with an oil extraction flowmeter arranged on the oil extraction pump, and the oil extraction amount of the oil extraction pump for extracting oil from the oil extraction well is obtained through the oil extraction flowmeter. The terminal obtains the oil extraction-oil extraction time sequence of the oil extraction well based on the oil extraction amount of the oil extraction pump from the oil extraction well.
In one possible implementation manner, the terminal can obtain the water injection amount-water injection time sequence of the water injection well and the oil extraction-oil extraction time sequence of the oil extraction well from the server, that is, each water injection well and each oil extraction well obtain data in the production process, the server collects and summarizes the data, and the terminal can directly obtain the water injection amount-water injection time sequence of the water injection well and the oil extraction-oil extraction time sequence of the oil extraction well from the server, wherein the data obtained in the production process comprises: the position of each water injection well, the position of each oil production well, the water injection amount of each water injection well at different times, the oil production amount of each oil production well at different times, the permeability of each water injection layer in each water injection well, the permeability of each oil production layer in each oil production well, and the like.
For example, in the process of producing an oilfield, multiple water injection wells and multiple production wells are often drilled in the oilfield. An oilfield has a data collection facility for collecting and storing data associated with the production process. The technician can number a plurality of water injection wells and a plurality of oil recovery wells respectively, and the numbers of the water injection wells and the oil recovery wells are recorded in the data collecting equipment. In addition, technicians can also import logging curves corresponding to a plurality of water injection wells and a plurality of oil extraction wells into the data collection equipment, so that unified management of data is facilitated. Of course, the water injection flowmeter in the water injection pump installed on the water injection well can send the water injection quantity collected in real time to the data collection equipment. The data collection equipment can bind and store the water injection quantity and the water injection well with the corresponding number, and correspondingly, the data collection equipment can also bind and store the oil production quantity and the oil production well with the corresponding number. The data collection device can send the stored related data to the server, the server stores the data, the server correspondingly maintains a database, and the database stores the related data on different oil fields. By the method, the safety of the related data can be improved, and the terminal can conveniently and rapidly call the related data from the server. When a technician needs to process the related data of a certain oilfield, the related data of the oilfield can be obtained quickly through the terminal, namely, the technician inputs the identification of the oilfield on the terminal, the terminal sends the identification of the oilfield to the server, the server queries in a corresponding maintained database, and the related data corresponding to the identification of the oilfield is sent to the terminal. And the terminal receives the related data sent by the server, and acquires a water injection quantity-water injection time sequence of the water injection well and a oil extraction-oil extraction time sequence of the oil extraction well from the related data.
In one possible embodiment, the distance between the water injection well and the production well is less than or equal to the distance threshold, that is, in this embodiment, the terminal is able to obtain only the water injection-water injection time series and the oil recovery-production time series from the water injection well and the production well that are eligible. Because the water injection well and the oil extraction well are often in a many-to-many relationship, namely one water injection well is possibly communicated with a plurality of oil extraction wells, when water is injected into the oil extraction wells, crude oil can be produced in the corresponding plurality of oil extraction wells under the driving of water. And the oil recovery wells with the shorter distance from the water injection well exist in the plurality of oil recovery wells, and the oil recovery wells with the longer distance from the water injection well also exist in the oil recovery wells with the shorter distance from the water injection well, so that when the water injection well is used for injecting water, the oil recovery wells with the shorter distance can rapidly respond to the water injected in the water injection well, namely crude oil is produced under the driving of the water. For the oil recovery well far away from the water injection well, when the water injection well is used for injecting water, the kinetic energy consumption is more in the process of flowing water injected from the water injection well in the stratum due to the far away distance, and crude oil in the oil recovery well far away from the water injection well cannot be effectively extruded, that is, the influence of the liquid injected into the water injection well on crude oil production of the oil recovery well far away from the water injection well is small. Therefore, by limiting the distance between the water injection well and the oil extraction well, some oil extraction wells with weak correlation with the water injection well can be eliminated, the data volume is reduced, the calculation resources of a terminal are saved, and the calculation efficiency is improved.
202. And the terminal preprocesses the water injection quantity-water injection time sequence of the water injection well and the oil extraction quantity-oil extraction time sequence of the oil extraction well.
In one possible implementation, the terminal can preprocess the water injection amount-water injection time sequence of the water injection well and the oil extraction-oil extraction time sequence of the oil extraction well, and delete the abnormal water injection amount in the water injection amount-water injection time sequence and the abnormal oil extraction in the oil extraction-oil extraction time sequence so as to improve the accuracy of determining the injection-extraction communication intensity based on the water injection amount-water injection time sequence and the oil extraction-oil extraction time sequence.
For example, a technician can set a first water injection rate threshold, i.e., the maximum water injection rate into the water injection well, and a first oil recovery threshold, i.e., the maximum oil recovery from the oil recovery well, at the terminal. When any water injection amount greater than the first water injection amount threshold exists in the water injection amount-water injection time sequence, the water injection amount is an abnormal water injection amount, and the terminal can delete the water injection amount from the water injection amount-water injection time sequence. When there is any oil recovery in the oil recovery-oil recovery time series that is greater than the first oil recovery threshold, which is indicative of an abnormal oil recovery, the terminal is able to delete the oil recovery from the oil recovery-oil recovery time series.
Correspondingly, the technician can also set a second water injection rate threshold, i.e., the minimum water injection rate for injecting water into the water injection well, and a second oil recovery threshold, i.e., the maximum oil recovery rate from the oil recovery well, on the terminal. When any water injection amount smaller than the second water injection amount threshold exists in the water injection amount-water injection time sequence, the water injection amount is the abnormal water injection amount, and the terminal can delete the water injection amount from the water injection amount-water injection time sequence. When there is any oil recovery in the oil recovery-oil recovery time series that is less than the second oil recovery threshold, which is indicative of an abnormal oil recovery, the terminal is able to delete the oil recovery from the oil recovery-oil recovery time series.
It should be noted that, the foregoing is described by taking the terminal as an example of deleting the excessive or insufficient water injection amount and oil production in the water injection amount-water injection time sequence of the water injection well and the oil production-oil production time sequence of the oil production well, and in other possible embodiments, the terminal can also perform pretreatment on the water injection amount-water injection time sequence of the water injection well and the oil production-oil production time sequence of the oil production well in other ways, so as to delete the abnormal values in the water injection amount-water injection time sequence of the water injection well and the oil production-oil production time sequence of the oil production well.
203. And the terminal inputs the water injection quantity-water injection time sequence of the water injection well and the permeability of each water injection layer in the water injection well into a water injection quantity splitting model, the water injection quantity-water injection time sequence of each water injection layer is output by the water injection quantity splitting model, and the water injection quantity splitting model is obtained by training according to the water injection quantity-water injection time sequence of at least one sample water injection well, the sample water injection quantity-water injection time sequence of each sample water injection layer in the at least one sample water injection well and the sample permeability.
The training process of the split model of the water injection rate is described in the foregoing, and will not be described in detail herein.
In one possible implementation manner, the terminal inputs the water injection amount-water injection time sequence of the water injection well and the permeability of each water injection layer in the water injection well into a water injection amount splitting model, and obtains a first seepage limit of each water injection layer based on the permeability of each water injection layer through the water injection amount splitting model, wherein the first seepage limit is a ratio between the permeability of each water injection layer and the highest permeability in each water injection layer. And the terminal obtains the water injection distribution proportion of each water injection layer based on the permeability of each water injection layer and the extremely poor seepage of each water injection layer through a water injection amount splitting model. The terminal obtains the water injection quantity-water injection time sequence of each water injection layer based on the water injection distribution proportion and the water injection quantity-water injection time sequence of the water injection well.
For example, if the water injection rate-water injection time sequence of the water injection well is (100, 80, 90, 105, 95), the water injection well comprises three water injection layers, and the permeability of the three water injection layers is 500 millidarcy (md), 400md and 600md, respectively. After the water injection amount-water injection time sequence (100, 80, 90, 105, 95) is input into the water injection amount splitting model by the terminal, the first seepage of the three water injection layers is extremely bad based on the seepage rate of the three water injection layers through the water injection amount splitting model, namely 500/600=0.83, 400/600=0.66 and 600/600=1. Linear regression (Line) with split model of water injection quantityar filtration) model, the terminal can splice the permeability (500, 400, 600) of the three water injection layers and the first seepage range (0.83,0.66,1) of the three water injection layers to obtain a two-dimensional vectorThe terminal is based on a weight matrix and a bias coefficient of a linear regression model, and is used for two-dimensional vector +.>Processing to obtain water injection distribution ratios of three water injection layers, such as 0.3:0.2:0.5. the terminal can allocate the proportion based on the water injection to be 0.3:0.2:0.5, the water injection rate-water injection time sequence of the water injection well is (100, 80, 90, 105, 95), and the water injection rate-water injection time sequences (30, 24, 27, 31.5, 28.5), (20, 16, 18, 21, 19) and (50, 40, 45, 52.5, 47.5) corresponding to the three water injection layers are obtained respectively.
In the above description, the water injection amount split model is taken as an example of a linear regression model, and in other possible embodiments, the water injection amount split model may be a model with other structures, which is not limited in this embodiment.
In one possible implementation manner, if the terminal trains a plurality of split water injection models with different structures, the terminal can input the water injection quantity-water injection time sequence of the water injection well and the permeability of each water injection layer in the water injection well into each split water injection model, and each split water injection model outputs the water injection quantity-water injection time sequence of each water injection layer respectively. And the terminal performs weighted summation on the water injection quantity-water injection time sequence of each water injection layer output by each water injection quantity splitting model to obtain the water injection quantity-water injection time sequence of each water injection layer.
For example, if the terminal trains two split water injection models with different structures, one split water injection model is a classification and regression tree (Classification and Regression Trees) model, and the other split water injection model is a Random Forest (Random Forest) model, the terminal can perform weighted summation on the water injection rate-water injection time sequences of the water injection layers respectively output by the classification and regression tree model and the Random Forest model, so as to obtain the water injection rate-water injection time sequences of the water injection layers. In some embodiments, the weight of the weighted sum is related to the splitting effect of the water injection amount of the model during the model test, that is, the better the splitting effect of the water injection amount is, the higher the corresponding weight is, and the worse the splitting effect of the water injection amount is, the lower the corresponding weight is. Of course, the technician can dynamically adjust the weight of the weighted summation through the terminal according to the actual situation, so as to improve the accuracy of the water injection quantity-water injection time sequence of each water injection layer.
For example, one structure is that the water injection amount-water injection time sequence of one water injection layer output by the water injection amount splitting model of the classified and regression tree is (30, 24, 27, 32, 30), the water injection amount-water injection time sequence of the same water injection layer output by the water injection amount splitting model of the random forest is (25, 20, 29, 30, 32), in the model test process, the average similarity between the water injection amount-water injection time sequence output by the water injection amount splitting model of the classified and regression tree and the real water injection amount-water injection time sequence is 0.85, the average similarity between the water injection amount-water injection time sequence output by the water injection amount splitting model of the random forest and the real water injection amount-water injection time sequence is 0.70, then the terminal can determine that the weight of the classified and the water injection amount splitting model of the regression tree is 0.85/(0.85+0.7) =0.55, and the weight of the random forest is 1-0.55=0.45. The terminal performs weighted summation on the water injection quantity-water injection time series (30, 24, 27, 32, 30) and the water injection quantity-water injection time series (25, 20, 29, 30, 32) based on the two weights, and obtains (27.8, 22.2, 27.9, 31.1, 30.6).
204. The terminal inputs the oil extraction-oil extraction time sequence of the oil extraction well and the permeability of each oil extraction layer in the oil extraction well into an oil extraction splitting model, the oil extraction-oil extraction time sequence of each oil extraction layer is output by the oil extraction splitting model, and the oil extraction splitting model is obtained by training according to the oil extraction-oil extraction time sequence of at least one sample oil extraction well, the sample oil extraction-oil extraction time sequence of each sample oil extraction layer in the at least one sample oil extraction well and the sample permeability.
The training process of the oil extraction split model is described in the foregoing, and is not described herein.
In one possible embodiment, the terminal inputs the time series of oil recovery from the oil recovery well and the permeability of each of the oil recovery layers in the oil recovery well into an oil recovery split model, and obtains a second differential in permeability of each of the oil recovery layers based on the permeability of each of the oil recovery layers by the oil recovery split model, the second differential being a ratio between the permeability of each of the oil recovery layers and a highest permeability in each of the oil recovery layers. And the terminal obtains the oil yield of each oil recovery layer based on the permeability of each oil recovery layer and the extremely poor seepage of each oil recovery layer through the oil recovery splitting model. The terminal obtains the oil extraction-oil extraction time series of each oil extraction layer based on the oil extraction proportion and the oil extraction-oil extraction time series of the oil extraction well.
For example, if the oil recovery-oil recovery time series of the oil recovery well is (90, 70, 80, 100, 90), the oil recovery well includes three oil recovery layers with permeabilities of 600md, 450md and 800md, respectively. After the oil recovery-oil recovery time sequence (90, 70, 80, 100, 90) is input into the oil recovery splitting model by the terminal, the second seepage of the three oil recovery layers is extremely poor based on the seepage rate of the three oil recovery layers, namely 600/800=0.75, 450/800=0.56 and 800/800=1. Taking the oil recovery split model as a linear regression (Linear Regression) model as an example, the terminal can splice the permeability (600, 450, 800) of the three oil recovery layers and the second seepage limit (0.75,0.56,1) of the three oil recovery layers to obtain a two-dimensional vectorThe terminal is based on a weight matrix and a bias coefficient of a linear regression model, and is used for two-dimensional vector +.>Treatment is carried out to obtain oil recovery distribution ratios of three oil recovery layers, such as 0.3:0.25:0.45. The terminal can allocate the ratio 0.3:0.25 based on oil recovery: 0.45, processing the oil recovery-oil recovery time sequence of the oil recovery well (90, 70, 80, 100, 90) to obtain oil recovery-oil recovery time sequences (27, 21, 24, 30, 27), (22.5, 17.5, 20, 25, 22.5) and (40.5, 31.5, 36, 45, 40.5) corresponding to the three oil recovery layers respectively.
It should be noted that, the foregoing is described taking the oil recovery split model as an example of the linear regression model, and in other possible embodiments, the oil recovery split model may be a model with other structures, which is not limited in this embodiment of the present application.
In one possible embodiment, if the terminal trains a plurality of oil recovery split models of different structures, the terminal is able to input the oil recovery-oil recovery time series of the oil recovery well and the permeability of each oil recovery layer in the oil recovery well into each oil recovery split model, and the oil recovery-oil recovery time series of each oil recovery layer is output by each oil recovery split model. And the terminal performs weighted summation on the oil extraction-oil extraction time sequences of the oil extraction layers output by the oil extraction splitting models to obtain the oil extraction-oil extraction time sequences of the oil extraction layers.
For example, if the terminal trains two oil recovery split models with different structures, one of the oil recovery split models is a classification and regression tree (Classification and Regression Trees) model, and the other oil recovery split model is a Random Forest (Random Forest) model, the terminal can perform weighted summation on the oil recovery-oil recovery time sequences of the oil recovery layers respectively output by the classification and regression tree model and the Random Forest model, so as to obtain the oil recovery-oil recovery time sequences of the oil recovery layers. In some embodiments, the weight of the weighted sum is related to the oil extraction split effect of the model when the model is tested, that is, the better the oil extraction split effect is in the model test process, the higher the corresponding weight is, and the worse the oil extraction split effect is, the lower the corresponding weight is. Of course, the technician can dynamically adjust the weight of the weighted summation through the terminal according to the actual situation, so as to improve the accuracy of the oil extraction-oil extraction time sequence of each oil extraction layer.
For example, one is the oil recovery-oil recovery time series of one oil recovery layer output by the oil recovery split model of the classified and regression tree is (27, 21, 24, 30, 27), the other is the oil recovery-oil recovery time series of the same oil recovery layer output by the oil recovery split model of the random forest is (25, 18, 26, 28, 33), during the model test, the average similarity between the oil recovery-oil recovery time series output by the oil recovery split model of the classified and regression tree and the actual oil recovery-oil recovery time series is 0.75, the average similarity between the oil recovery-oil recovery time series output by the oil recovery split model of the random forest and the actual oil recovery-oil recovery time series is 0.70, and then the terminal can determine that the weight of the classified and the oil recovery split model of the regression tree is 0.75/(0.75+0.7) =0.52, and the weight of the random forest is 1-0.52=0.48. The terminal performs a weighted summation of the oil-recovery time series (27, 21, 24, 30, 27) and the oil-recovery time series (25, 18, 26, 28, 33) based on the two weights, resulting in (26, 19.5, 25, 29, 30).
In addition, after step 203, the terminal can train the water injection amount splitting model and the oil extraction splitting model based on the data acquired in the production process, that is, update the model parameters of the water injection amount splitting model and the oil extraction splitting model in real time in the use process, so as to achieve better water injection amount splitting effect and oil extraction splitting effect.
205. And the terminal inputs the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer into a correlation determination model, and outputs a correlation coefficient between the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer through the correlation determination model.
In one possible implementation manner, the terminal inputs the water injection amount-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer into a correlation determination model, and obtains a water injection amount-oil extraction difference matrix between each water injection layer and each oil extraction layer through the correlation determination model, wherein the value in the water injection amount-oil extraction difference matrix is the difference between the water injection amount of each water injection layer and the oil extraction of each oil extraction layer. And the terminal obtains the correlation coefficient between the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer based on the water injection quantity-oil extraction difference matrix through a correlation determination model.
For example, the terminal inputs a water injection amount-water injection time sequence of a water injection layer and a oil extraction-oil extraction time sequence of an oil extraction layer into the correlation determination model at one time, and obtains a water injection amount-oil extraction difference matrix between the water injection layer and the oil extraction layer through the correlation determination model. And the terminal obtains a target path taking the upper left corner of the water injection quantity-oil extraction difference matrix as a starting point and the lower right corner of the water injection quantity-oil extraction difference matrix as an end point through a correlation determination model, wherein the target path is the path with the smallest sum of the passing numerical values. And the terminal determines the sum of the values of the target path as a correlation coefficient, wherein the magnitude of the correlation coefficient is inversely proportional to the similarity between the water injection quantity-water injection time sequence and the oil recovery quantity-oil recovery time sequence.
For example, the water injection rate-water injection time sequence of the water injection layer is A (20, 16, 18, 21, 19), the oil extraction rate-oil extraction time sequence of the oil extraction layer is B (27, 21, 24, 30, 27), and the terminal obtains the water injection rate-oil extraction difference matrix between the water injection layer and the oil extraction layer through a correlation determination modelWherein each value in the water injection quantity-oil extraction difference matrix is the difference between one value in the water injection quantity-water injection time series A (20, 16, 18, 21, 19) and the other value in the oil extraction-oil extraction time series B (27, 21, 24, 30, 27). For example, for the first digit "7" in the upper left hand corner of the water injection quantity-oil recovery difference matrix, it is the difference between the first digit "27" in the oil recovery-oil recovery time series B (27, 21, 24, 30, 27) and the first digit "20" in the water injection quantity-water injection time series a (20, 16, 18, 21, 19). For the upper left of the water injection quantity-oil extraction quantity difference matrix The second number "11" for the angle is the difference between the first value "27" in the oil recovery-oil recovery time series B (27, 21, 24, 30, 27) and the second value "16" in the water injection-water injection time series a (20, 16, 18, 21, 19), and so on.
The process of reaching the last value "8" in the lower right corner of the water injection rate-oil recovery differential matrix starting from the first value "7" in the upper left corner of the water injection rate-oil recovery differential matrix is described below. In order to ensure that the target path is the path with the smallest sum of the passing values, the terminal can compare the sizes of each number on the advancing path in real time, and ensure that the smallest value is passed when each step advances. Starting from "7", there are three values "1", "11" and "5" next to "7", and the terminal determines that "1" is the smallest value among the three values, and then takes the value "1" as the advancing position. The terminal continues to start from the value "1", compares the magnitudes of the three values "4", "8" and "5" in front of the value "1", and determines that "4" is the smallest value of the three values, and then takes the value "4" as the advancing position. After multiple comparisons and advances, the terminal can obtain a target path of "7+1+4+8+6+3+5+11+8", the length of the target path is 53, and the length of the target path is the correlation coefficient between the water injection layer and the oil recovery layer.
It should be noted that, the above description process is described by taking the terminal to obtain the correlation coefficient between one water injection layer and one oil recovery layer as an example, and the method for obtaining the correlation coefficient between multiple water injection layers and multiple oil recovery layers by the terminal and the above process belong to the same inventive concept, and are not repeated herein.
206. And the terminal determines the injection and production communication strength between each water injection layer and each oil extraction layer based on the correlation coefficient.
In one possible implementation, the terminal normalizes the correlation coefficients to determine the strength of the injection and production communication between each of the water injection layers and each of the oil recovery layers. Optionally, a method adopted by the terminal for normalizing the correlation coefficient is soft maximization (Softmax) or S-type growth curve (Sigmoid), which is not limited in the embodiment of the present application.
Taking a water injection layer as an example, the terminal obtains correlation coefficients between the water injection layer and three oil recovery layers respectively of 53, 27 and 20 through a correlation determination model. The terminal can normalize (0.53,0.27,0.2) the pair (53, 27,20) using Softmax. The terminal can obtain the reciprocal (1/0.53,1/0.27,1/0.2) of the normalized correlation coefficient, and takes the reciprocal (1/0.53,1/0.27,1/0.2) as the injection and production communication strength between the water injection layer and the three oil recovery layers, wherein the larger the value of the injection and production communication strength is, the better the connectivity between the water injection layer and the oil recovery layers is identified.
It should be noted that, the foregoing steps 201 to 206 are described by taking the terminal as an execution body as an example, and in other possible embodiments, the steps 201 to 206 can be executed by using a server as the execution body, and the embodiment of the present application is not limited to the type of the execution body.
In the embodiment of the application, the oil extraction-oil extraction time series of a plurality of water injection layers in the water injection well and the oil extraction-oil extraction time series of a plurality of oil extraction layers in the oil extraction well can be directly obtained through the water injection quantity split model and the oil extraction split model. Based on the similarity between the oil recovery-oil recovery time series of the plurality of water injection layers and the oil recovery-oil recovery time series of the plurality of oil recovery layers, the injection and production communication strength between the plurality of water injection layers and the plurality of oil recovery layers can be obtained quickly. Through the technical scheme, the process of determining the injection-production communication strength does not need to stop production, complicated operation and additional construction operation are not needed, the efficiency of determining the injection-production communication strength is higher, and the cost is lower.
Fig. 3 is a schematic structural diagram of an injection-production communication strength determining device provided in an embodiment of the present application, referring to fig. 3, the device includes: an acquisition module 301, a first input module 302, a second input module 303, a third input module 304, and an injection and production communication strength determination module 305.
The obtaining module 301 is configured to obtain a water injection amount-water injection time sequence of a water injection well and a oil production-oil recovery time sequence of an oil recovery well.
The first input module 302 is configured to input a water injection amount-water injection time sequence of the water injection well and a permeability of each water injection layer in the water injection well into a water injection amount splitting model, and output the water injection amount-water injection time sequence of each water injection layer by the water injection amount splitting model, where the water injection amount splitting model is obtained by training according to the water injection amount-water injection time sequence of at least one sample water injection well, and a sample water injection amount-water injection time sequence and a sample permeability of each sample water injection layer in the at least one sample water injection well.
And a second input module 303, configured to input the oil extraction-oil extraction time sequence of the oil extraction well and the permeability of each oil extraction layer in the oil extraction well into an oil extraction splitting model, and output the oil extraction-oil extraction time sequence of each oil extraction layer by the oil extraction splitting model, where the oil extraction splitting model is obtained by training according to the oil extraction-oil extraction time sequence of at least one sample oil extraction well, and the sample oil extraction-oil extraction time sequence and the sample permeability of each sample oil extraction layer in the at least one sample oil extraction well.
And a third input module 304, configured to input the water injection amount-water injection time sequence of each water injection layer and the oil recovery-oil recovery time sequence of each oil recovery layer into a correlation determination model, and output a correlation coefficient between the water injection amount-water injection time sequence of each water injection layer and the oil recovery-oil recovery time sequence of each oil recovery layer through the correlation determination model.
And the injection and production communication strength determining module 305 is used for determining the injection and production communication strength between each water injection layer and each oil production layer based on the correlation coefficient.
In one possible implementation manner, the first input module is configured to input a water injection amount-water injection time sequence of the water injection well and a permeability of each water injection layer in the water injection well into the water injection amount splitting model, and obtain, based on the permeability of each water injection layer, a first seepage limit of each water injection layer through the water injection amount splitting model, where the first seepage limit is a ratio between the permeability of each water injection layer and a highest permeability in each water injection layer. And obtaining the water injection distribution proportion of each water injection layer based on the permeability of each water injection layer and the extremely poor seepage of each water injection layer through a water injection amount splitting model. And obtaining the water injection quantity-water injection time sequence of each water injection layer based on the water injection distribution proportion and the water injection quantity-water injection time sequence of the water injection well.
In one possible embodiment, the second input module is configured to input the oil recovery-oil recovery time sequence of the oil recovery well and the permeability of each oil recovery layer in the oil recovery well into the oil recovery split model, and obtain, based on the permeability of each oil recovery layer, a second seepage limit of each oil recovery layer by the oil recovery split model, where the second seepage limit is a ratio between the permeability of each oil recovery layer and a highest permeability in each oil recovery layer. And obtaining the oil yield of each oil recovery layer based on the permeability of each oil recovery layer and the extremely poor seepage of each oil recovery layer through the oil recovery splitting model. And obtaining the oil extraction quantity-oil extraction time sequence of each oil extraction layer based on the oil extraction proportion and the oil extraction quantity-oil extraction time sequence of the oil extraction well.
In one possible implementation manner, the third input module is configured to input the water injection amount-water injection time sequence of each water injection layer and the oil recovery amount-oil recovery time sequence of each oil recovery layer into the correlation determination model, and obtain a water injection amount-oil recovery difference matrix between each water injection layer and each oil recovery layer through the correlation determination model, where a value in the water injection amount-oil recovery difference matrix is a difference value between the water injection amount of each water injection layer and the oil recovery amount of each oil recovery layer. And obtaining a correlation coefficient between the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer based on the water injection quantity-oil extraction difference matrix through a correlation determination model.
In one possible implementation manner, the third input module is configured to obtain, through the correlation determination model, a target path starting from an upper left corner of the water injection rate-oil recovery difference matrix and ending at a lower right corner of the water injection rate-oil recovery difference matrix, where the target path is a path with a minimum sum of values passing through. The sum of the values traversed by the target path is determined as a correlation coefficient.
In one possible implementation, the injection and production communication strength determining module is configured to normalize the correlation coefficient to determine injection and production communication strength between each water injection layer and each oil recovery layer.
In one possible embodiment, the distance between the water injection well and the production well is less than or equal to a distance threshold.
It should be noted that: in determining the injection-production communication strength, the injection-production communication strength determining device provided in the above embodiment is only exemplified by the division of the above functional modules, and in practical application, the above functional allocation may be performed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the injection and production communication strength determining device provided in the foregoing embodiment and the injection and production communication strength determining method embodiment belong to the same concept, and detailed implementation processes of the injection and production communication strength determining device are shown in the method embodiment, and are not repeated here.
In the embodiment of the application, the oil extraction-oil extraction time series of a plurality of water injection layers in the water injection well and the oil extraction-oil extraction time series of a plurality of oil extraction layers in the oil extraction well can be directly obtained through the water injection quantity split model and the oil extraction split model. Based on the similarity between the oil recovery-oil recovery time series of the plurality of water injection layers and the oil recovery-oil recovery time series of the plurality of oil recovery layers, the injection and production communication strength between the plurality of water injection layers and the plurality of oil recovery layers can be obtained quickly. Through the technical scheme, the process of determining the injection-production communication strength does not need to stop production, complicated operation and additional construction operation are not needed, the efficiency of determining the injection-production communication strength is higher, and the cost is lower.
In the embodiment of the present application, the electronic device may be implemented as a terminal or a server, and the structure of the terminal is described below.
Fig. 4 shows a block diagram of a terminal 400 according to an exemplary embodiment of the present application. The terminal 400 may be a portable mobile terminal such as: smart phones, tablet computers, notebook computers or desktop computers. The terminal 400 may also be referred to by other names as user equipment, portable terminal, laptop terminal, desktop terminal, etc.
In general, the terminal 400 includes: a processor 401 and a memory 402.
Processor 401 may include one or more processing cores such as a 4-core processor, an 8-core processor, etc. The processor 401 may be implemented in at least one hardware form of DSP (Digital Signal Processing ), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array ). The processor 401 may also include a main processor, which is a processor for processing data in an awake state, also called a CPU (Central Processing Unit ), and a coprocessor; a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 401 may be integrated with a GPU (Graphics Processing Unit, image processor) for taking care of rendering and drawing of content that the display screen needs to display. In some embodiments, the processor 401 may also include an AI (Artificial Intelligence ) processor for processing computing operations related to machine learning.
Memory 402 may include one or more computer-readable storage media, which may be non-transitory. Memory 402 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 402 is used to store at least one program code for execution by processor 401 to implement the injection and production communication strength determination method provided by the method embodiments herein.
In some embodiments, the terminal 400 may further optionally include: a peripheral interface 403 and at least one peripheral. The processor 401, memory 402, and peripheral interface 403 may be connected by a bus or signal line. The individual peripheral devices may be connected to the peripheral device interface 403 via buses, signal lines or a circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 404, a display screen 405, a camera assembly 406, an audio circuit 407, a positioning assembly 408, and a power supply 409.
Peripheral interface 403 may be used to connect at least one Input/Output (I/O) related peripheral to processor 401 and memory 402. In some embodiments, processor 401, memory 402, and peripheral interface 403 are integrated on the same chip or circuit board; in some other embodiments, either or both of the processor 401, memory 402, and peripheral interface 403 may be implemented on separate chips or circuit boards, which is not limited in this embodiment.
The Radio Frequency circuit 404 is configured to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuitry 404 communicates with a communication network and other communication devices via electromagnetic signals. The radio frequency circuit 404 converts an electrical signal into an electromagnetic signal for transmission, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 404 includes: antenna systems, RF transceivers, one or more amplifiers, tuners, oscillators, digital signal processors, codec chipsets, subscriber identity module cards, and so forth. The radio frequency circuitry 404 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: the world wide web, metropolitan area networks, intranets, generation mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and/or WiFi (Wireless Fidelity ) networks. In some embodiments, the radio frequency circuitry 404 may also include NFC (Near Field Communication ) related circuitry, which is not limited in this application.
The display screen 405 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 405 is a touch display screen, the display screen 405 also has the ability to collect touch signals at or above the surface of the display screen 405. The touch signal may be input as a control signal to the processor 401 for processing. At this time, the display screen 405 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display 405 may be one and disposed on the front panel of the terminal 400; in other embodiments, the display 405 may be at least two, and disposed on different surfaces of the terminal 400 or in a folded design; in other embodiments, the display 405 may be a flexible display disposed on a curved surface or a folded surface of the terminal 400. Even more, the display screen 405 may be arranged in an irregular pattern that is not rectangular, i.e. a shaped screen. The display 405 may be made of LCD (Liquid Crystal Display ), OLED (Organic Light-Emitting Diode) or other materials.
The camera assembly 406 is used to capture images or video. Optionally, camera assembly 406 includes a front camera and a rear camera. Typically, the front camera is disposed on the front panel of the terminal and the rear camera is disposed on the rear surface of the terminal. In some embodiments, the at least two rear cameras are any one of a main camera, a depth camera, a wide-angle camera and a tele camera, so as to realize that the main camera and the depth camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize a panoramic shooting and Virtual Reality (VR) shooting function or other fusion shooting functions. In some embodiments, camera assembly 406 may also include a flash. The flash lamp can be a single-color temperature flash lamp or a double-color temperature flash lamp. The dual-color temperature flash lamp refers to a combination of a warm light flash lamp and a cold light flash lamp, and can be used for light compensation under different color temperatures.
The audio circuit 407 may include a microphone and a speaker. The microphone is used for collecting sound waves of users and environments, converting the sound waves into electric signals, and inputting the electric signals to the processor 401 for processing, or inputting the electric signals to the radio frequency circuit 404 for realizing voice communication. For the purpose of stereo acquisition or noise reduction, a plurality of microphones may be respectively disposed at different portions of the terminal 400. The microphone may also be an array microphone or an omni-directional pickup microphone. The speaker is used to convert electrical signals from the processor 401 or the radio frequency circuit 404 into sound waves. The speaker may be a conventional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, not only the electric signal can be converted into a sound wave audible to humans, but also the electric signal can be converted into a sound wave inaudible to humans for ranging and other purposes. In some embodiments, audio circuit 407 may also include a headphone jack.
The location component 408 is used to locate the current geographic location of the terminal 400 to enable navigation or LBS (Location Based Service, location-based services). The positioning component 408 may be a positioning component based on the united states GPS (Global Positioning System ), the chinese beidou system, or the russian galileo system.
The power supply 409 is used to power the various components in the terminal 400. The power supply 409 may be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When power supply 409 comprises a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. The wired rechargeable battery is a battery charged through a wired line, and the wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery may also be used to support fast charge technology.
In some embodiments, the terminal 400 further includes one or more sensors 410. The one or more sensors 410 include, but are not limited to: acceleration sensor 411, gyroscope sensor 412, pressure sensor 413, fingerprint sensor 414, optical sensor 415, and proximity sensor 416.
The acceleration sensor 411 may detect the magnitudes of accelerations on three coordinate axes of the coordinate system established with the terminal 400. For example, the acceleration sensor 411 may be used to detect components of gravitational acceleration on three coordinate axes. The processor 401 may control the display screen 405 to display the user interface in a lateral view or a longitudinal view according to the gravitational acceleration signal acquired by the acceleration sensor 411. The acceleration sensor 411 may also be used for the acquisition of motion data of a game or a user.
The gyro sensor 412 may detect a body direction and a rotation angle of the terminal 400, and the gyro sensor 412 may collect a 3D motion of the user to the terminal 400 in cooperation with the acceleration sensor 411. The processor 401 may implement the following functions according to the data collected by the gyro sensor 412: motion sensing (e.g., changing UI according to a tilting operation by a user), image stabilization at shooting, game control, and inertial navigation.
The pressure sensor 413 may be disposed at a side frame of the terminal 400 and/or at a lower layer of the display 405. When the pressure sensor 413 is disposed at a side frame of the terminal 400, a grip signal of the terminal 400 by a user may be detected, and the processor 401 performs a left-right hand recognition or a shortcut operation according to the grip signal collected by the pressure sensor 413. When the pressure sensor 413 is disposed at the lower layer of the display screen 405, the processor 401 controls the operability control on the UI interface according to the pressure operation of the user on the display screen 405. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.
The fingerprint sensor 414 is used to collect a fingerprint of the user, and the processor 401 identifies the identity of the user based on the fingerprint collected by the fingerprint sensor 414, or the fingerprint sensor 414 identifies the identity of the user based on the collected fingerprint. Upon recognizing that the user's identity is a trusted identity, the user is authorized by the processor 401 to perform relevant sensitive operations including unlocking the screen, viewing encrypted information, downloading software, paying for and changing settings, etc. The fingerprint sensor 414 may be provided at the front, rear, or side of the terminal 400. When a physical key or vendor Logo is provided on the terminal 400, the fingerprint sensor 414 may be integrated with the physical key or vendor Logo.
The optical sensor 415 is used to collect the ambient light intensity. In one embodiment, processor 401 may control the display brightness of display screen 405 based on the ambient light intensity collected by optical sensor 415. Specifically, when the intensity of the ambient light is high, the display brightness of the display screen 405 is turned up; when the ambient light intensity is low, the display brightness of the display screen 405 is turned down. In another embodiment, the processor 401 may also dynamically adjust the shooting parameters of the camera assembly 406 according to the ambient light intensity collected by the optical sensor 415.
A proximity sensor 416, also referred to as a distance sensor, is typically provided on the front panel of the terminal 400. The proximity sensor 416 is used to collect the distance between the user and the front of the terminal 400. In one embodiment, when the proximity sensor 416 detects a gradual decrease in the distance between the user and the front face of the terminal 400, the processor 401 controls the display 405 to switch from the bright screen state to the off screen state; when the proximity sensor 416 detects that the distance between the user and the front surface of the terminal 400 gradually increases, the processor 401 controls the display 405 to switch from the off-screen state to the on-screen state.
Those skilled in the art will appreciate that the structure shown in fig. 4 is not limiting of the terminal 400 and may include more or fewer components than shown, or may combine certain components, or may employ a different arrangement of components.
Fig. 5 is a schematic structural diagram of a server according to an embodiment of the present application, where the server 500 may have a relatively large difference due to different configurations or performances, and may include one or more processors 501 and one or more memories 502, where the one or more memories 502 store at least one program code, and the at least one program code is loaded and executed by the one or more processors 501 to implement the methods provided in the foregoing method embodiments. Of course, the server 500 may also have a wired or wireless network interface, a keyboard, an input/output interface, and other components for implementing the functions of the device, which are not described herein.
In an exemplary embodiment, a computer readable storage medium, such as a memory, comprising program code executable by a processor to perform the injection and production communication strength determination method of the above embodiments is also provided. For example, the computer readable storage medium may be Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), compact disc Read-Only Memory (CD-ROM), magnetic tape, floppy disk, optical data storage device, and the like.
It will be appreciated by those of ordinary skill in the art that all or part of the steps of implementing the above embodiments may be implemented by hardware, or may be implemented by program code related hardware, where the program may be stored in a computer readable storage medium, where the storage medium may be a read only memory, a magnetic disk or optical disk, etc.
The foregoing description of the preferred embodiments is merely exemplary in nature and is not intended to limit the invention, but is intended to cover various modifications, substitutions, improvements, and alternatives falling within the spirit and principles of the invention.

Claims (10)

1. A method for determining injection and production communication strength, the method comprising:
acquiring a water injection quantity-water injection time sequence of a water injection well and an oil extraction-oil extraction time sequence of an oil extraction well;
inputting the water injection quantity-water injection time sequence of the water injection well and the permeability of each water injection layer in the water injection well into a water injection quantity splitting model, and outputting the water injection quantity-water injection time sequence of each water injection layer by the water injection quantity splitting model, wherein the water injection quantity splitting model is obtained by training according to the water injection quantity-water injection time sequence of at least one sample water injection well, the sample water injection quantity-water injection time sequence of each sample water injection layer in the at least one sample water injection well and the sample permeability;
Inputting the oil extraction-oil extraction time sequence of the oil extraction well and the permeability of each oil extraction layer in the oil extraction well into an oil extraction splitting model, and outputting the oil extraction-oil extraction time sequence of each oil extraction layer by the oil extraction splitting model, wherein the oil extraction splitting model is obtained by training according to the oil extraction-oil extraction time sequence of at least one sample oil extraction well, the sample oil extraction-oil extraction time sequence and the sample permeability of each sample oil extraction layer in the at least one sample oil extraction well;
inputting the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer into a correlation determination model, and outputting a correlation coefficient between the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer through the correlation determination model;
and determining the injection and production communication strength between each water injection layer and each oil extraction layer based on the correlation coefficient.
2. The method of claim 1, wherein inputting the water injection amount-water injection time series of the water injection well and the permeability of each water injection layer in the water injection well into a water injection amount split model, and outputting the water injection amount-water injection time series of each water injection layer from the water injection amount split model comprises:
Inputting a water injection quantity-water injection time sequence of the water injection well and the permeability of each water injection layer in the water injection well into a water injection quantity splitting model, and obtaining a first seepage limit of each water injection layer based on the permeability of each water injection layer through the water injection quantity splitting model, wherein the first seepage limit is the ratio between the permeability of each water injection layer and the highest permeability in each water injection layer;
obtaining water injection distribution proportion of each water injection layer based on the permeability of each water injection layer and the seepage limit of each water injection layer through the water injection amount splitting model;
and obtaining the water injection quantity-water injection time sequence of each water injection layer based on the water injection distribution proportion and the water injection quantity-water injection time sequence of the water injection well.
3. The method of claim 1, wherein said inputting the time series of oil recovery from the oil recovery well and the permeability of each oil recovery layer in the oil recovery well into an oil recovery split model, and outputting the time series of oil recovery from each oil recovery layer by the oil recovery split model comprises:
inputting the oil extraction-oil extraction time sequence of the oil extraction well and the permeability of each oil extraction layer in the oil extraction well into an oil extraction splitting model, and obtaining a second seepage limit of each oil extraction layer based on the permeability of each oil extraction layer through the oil extraction splitting model, wherein the second seepage limit is the ratio between the permeability of each oil extraction layer and the highest permeability in each oil extraction layer;
Obtaining the oil yield ratio of each oil recovery layer based on the permeability of each oil recovery layer and the seepage limit of each oil recovery layer through the oil recovery splitting model;
and obtaining the oil extraction-oil extraction time series of each oil extraction layer based on the oil extraction proportion and the oil extraction-oil extraction time series of the oil extraction well.
4. The method of claim 1, wherein said inputting the water injection amount-water injection time series of the respective water injection layers and the oil recovery-oil recovery time series of the respective oil recovery layers into a correlation determination model, and outputting a correlation coefficient between the water injection amount-water injection time series of the respective water injection layers and the oil recovery-oil recovery time series of the respective oil recovery layers through the correlation determination model comprises:
inputting the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer into a correlation determination model, and acquiring a water injection quantity-oil extraction difference matrix between each water injection layer and each oil extraction layer through the correlation determination model, wherein the numerical value in the water injection quantity-oil extraction difference matrix is the difference value between the water injection quantity of each water injection layer and the oil extraction of each oil extraction layer;
And obtaining a correlation coefficient between the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer based on the water injection quantity-oil extraction difference matrix through the correlation determination model.
5. The method of claim 4, wherein said obtaining, by said correlation determination model, a correlation coefficient between the water injection amount-water injection time series of each water injection layer and the oil recovery-oil recovery time series of each oil recovery layer based on said water injection amount-oil recovery difference matrix comprises:
obtaining a target path taking the upper left corner of the water injection quantity-oil extraction difference matrix as a starting point and the lower right corner of the water injection quantity-oil extraction difference matrix as an end point through the correlation determination model, wherein the target path is a path with the smallest sum of the passing numerical values;
and determining the sum of the values of the target path as the correlation coefficient.
6. The method of claim 1, wherein the determining, based on the correlation coefficients, a strength of injection-production communication between the respective injection layer and the respective production layer comprises:
and carrying out normalization processing on the correlation coefficient, and determining the injection and production communication strength between each water injection layer and each oil recovery layer.
7. The method of claim 1, wherein a distance between the water injection well and the oil recovery well is less than or equal to a distance threshold.
8. An injection and production communication strength determining device, comprising:
the acquisition module is used for acquiring a water injection quantity-water injection time sequence of the water injection well and an oil extraction-oil extraction time sequence of the oil extraction well;
the first input module is used for inputting the water injection quantity-water injection time sequence of the water injection well and the permeability of each water injection layer in the water injection well into a water injection quantity splitting model, outputting the water injection quantity-water injection time sequence of each water injection layer by the water injection quantity splitting model, wherein the water injection quantity splitting model is obtained by training according to the water injection quantity-water injection time sequence of at least one sample water injection well, the sample water injection quantity-water injection time sequence of each sample water injection layer in the at least one sample water injection well and the sample permeability;
the second input module is used for inputting the oil extraction-oil extraction time sequence of the oil extraction well and the permeability of each oil extraction layer in the oil extraction well into an oil extraction split model, and outputting the oil extraction-oil extraction time sequence of each oil extraction layer by the oil extraction split model, wherein the oil extraction split model is obtained by training according to the oil extraction-oil extraction time sequence of at least one sample oil extraction well, the sample oil extraction-oil extraction time sequence of each sample oil extraction layer in the at least one sample oil extraction well and the sample permeability;
The third input module is used for inputting the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer into a correlation determination model, and outputting a correlation coefficient between the water injection quantity-water injection time sequence of each water injection layer and the oil extraction-oil extraction time sequence of each oil extraction layer through the correlation determination model;
and the injection and production communication strength determining module is used for determining the injection and production communication strength between each injection and production layer and each oil extraction layer based on the correlation coefficient.
9. A computer device comprising one or more processors and one or more memories, the one or more memories having stored therein at least one program code loaded and executed by the one or more processors to implement the injection and production communication strength determination method of any of claims 1-7.
10. A computer readable storage medium having stored therein at least one program code loaded and executed by a processor to implement the injection and production communication strength determination method of any one of claims 1 to 7.
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Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101042048A (en) * 2006-03-24 2007-09-26 中国石油天然气股份有限公司 Complicated fault block fluvial facies reservoir oil water well using situation split system
CN101382070A (en) * 2007-09-03 2009-03-11 中国石油天然气集团公司 Electromagnetical method for dynamically monitoring oil reservoir injection-production
CN104732359A (en) * 2015-04-08 2015-06-24 陕西延长石油（集团）有限责任公司 Oil field geographic information and exploration development collaboration work platform system
CN107339087A (en) * 2017-08-10 2017-11-10 中国石油天然气股份有限公司 A kind of water injection rate splits a point method and device
CN108240208A (en) * 2018-02-05 2018-07-03 东北石油大学 A kind of oilfield water flooding classification well group development effectiveness is to marking method
CN110348137A (en) * 2019-07-15 2019-10-18 西南石油大学 A kind of water-drive pool seepage field evaluation method based on Vector Autoression Models
US10458207B1 (en) * 2016-06-09 2019-10-29 QRI Group, LLC Reduced-physics, data-driven secondary recovery optimization
CN110765624A (en) * 2019-10-29 2020-02-07 中国石油化工股份有限公司 Reasonable layering method for water injection oil reservoir
CN111936719A (en) * 2018-02-07 2020-11-13 液压声学公司 Oil recovery tool and system

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10267130B2 (en) * 2016-09-26 2019-04-23 International Business Machines Corporation Controlling operation of a steam-assisted gravity drainage oil well system by adjusting controls to reduce model uncertainty
US10352142B2 (en) * 2016-09-26 2019-07-16 International Business Machines Corporation Controlling operation of a stem-assisted gravity drainage oil well system by adjusting multiple time step controls

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101042048A (en) * 2006-03-24 2007-09-26 中国石油天然气股份有限公司 Complicated fault block fluvial facies reservoir oil water well using situation split system
CN101382070A (en) * 2007-09-03 2009-03-11 中国石油天然气集团公司 Electromagnetical method for dynamically monitoring oil reservoir injection-production
CN104732359A (en) * 2015-04-08 2015-06-24 陕西延长石油（集团）有限责任公司 Oil field geographic information and exploration development collaboration work platform system
US10458207B1 (en) * 2016-06-09 2019-10-29 QRI Group, LLC Reduced-physics, data-driven secondary recovery optimization
CN107339087A (en) * 2017-08-10 2017-11-10 中国石油天然气股份有限公司 A kind of water injection rate splits a point method and device
CN108240208A (en) * 2018-02-05 2018-07-03 东北石油大学 A kind of oilfield water flooding classification well group development effectiveness is to marking method
CN111936719A (en) * 2018-02-07 2020-11-13 液压声学公司 Oil recovery tool and system
CN110348137A (en) * 2019-07-15 2019-10-18 西南石油大学 A kind of water-drive pool seepage field evaluation method based on Vector Autoression Models
CN110765624A (en) * 2019-10-29 2020-02-07 中国石油化工股份有限公司 Reasonable layering method for water injection oil reservoir

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