Well shutdown recovery potential determination method and device, electronic equipment and medium

Abstract

The invention discloses a well shutdown recovery potential determination method and device, electronic equipment and a medium. The method comprises the following steps: acquiring shut-down well production indexes and engineering operation information of a target oil field; according to the shut-down well production indexes and the engineering operation information, shut-down wells in the target oil field are screened, and shut-down wells to be screened are obtained; the economic limit water content and the economic limit daily oil production of the shut-down wells to be screened are determined; screening the shut-down wells to be screened according to the economic limit moisture content and the economic limit daily oil production to obtain target shut-down wells; predicting the recovery potential of the target shut-down well by using a machine learning algorithm, and determining the recovery potential of the shut-down well; wherein the shut-down well recovery potential is composed of main control characteristics and weights which influence the size of the shut-down well recovery potential. According to the technical scheme, the recovery potential of the shut-down well can be accurately and efficiently evaluated, and the method has important guiding significance on implementation of recovery measures on an oil field site.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas development, and in particular to a method, device, electronic equipment and medium for determining the production resumption potential of a shut-in well. Background Art

[0002] At present, many wells are shut down due to extremely high water content, low production and efficiency or engineering conditions. The high proportion of shut-in wells is a common problem faced by old oil fields with extremely high water content. Considering the resumption of production of shut-in wells is an important measure to increase the well opening rate, increase production and improve the effect of oil field development at a low cost.

[0003] Currently, there is no unified standard and process for evaluating and screening the potential for resumption of production of shut-in wells. It mainly relies on the experience of the evaluator, lacks theoretical basis and guiding principles, and lacks a systematic optimization method. The entire evaluation and screening process requires repeated review of various historical production indicators and other data of each well, which is cumbersome and inefficient.

[0004] Therefore, there is an urgent need to develop new methods to determine the production resumption potential of shut-in wells in order to facilitate the evaluation of the production resumption potential of shut-in wells. Summary of the invention

[0005] The present invention provides a method, device, electronic equipment and medium for determining the production resumption potential of a shut-in well, which can accurately and efficiently evaluate the production resumption potential of a shut-in well and has important guiding significance for implementing production resumption measures on site in an oil field.

[0006] According to one aspect of the present invention, a method for determining the production resumption potential of a shut-in well is provided, the method comprising:

[0007] Obtain shut-in well production indicators and engineering operation information for target oil fields;

[0008] Screening the shut-in wells in the target oil field according to the shut-in well production index and engineering operation information to obtain the shut-in wells to be screened;

[0009] Determine the economic limit water content and economic limit daily oil production of the wells to be screened and shut down;

[0010] The shut-in wells to be screened are screened according to the economic limit water content and the economic limit daily oil production to obtain target shut-in wells;

[0011] The resumption potential of the target shut-in well is predicted by using a machine learning algorithm to determine the resumption potential of the shut-in well; wherein the resumption potential of the shut-in well is composed of the main control characteristics and weights that affect the size of the resumption potential of the shut-in well.

[0012] According to another aspect of the present invention, a device for determining the production resumption potential of a shut-in well is provided, the device comprising:

[0013] Parameter acquisition module, used to obtain shut-in well production indicators and engineering operation information of the target oil field;

[0014] A shut-in well screening module, used to screen the shut-in wells in the target oil field according to the shut-in well production index and engineering operation information, to obtain the shut-in wells to be screened;

[0015] A limit information determination module, used to determine the economic limit water content and economic limit daily oil production of the well to be screened and shut down;

[0016] A target shut-in well screening module is used to screen the shut-in wells to be screened according to the economic limit water cut and the economic limit daily oil production to obtain the target shut-in wells;

[0017] The shut-in well resumption potential determination module is used to use a machine learning algorithm to predict the resumption potential of the target shut-in well and determine the resumption potential of the shut-in well; wherein the shutdown well resumption potential is composed of the main control characteristics and weights that affect the size of the shutdown well resumption potential.

[0018] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0019] at least one processor; and

[0020] a memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a method for determining the potential for resumption of production of a shut-in well as described in any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer-readable medium is provided, wherein the computer-readable medium stores computer instructions, and the computer instructions are used to enable a processor to implement a method for determining the production resumption potential of a shut-in well as described in any embodiment of the present invention when executed.

[0023] The technical solution of the embodiment of the present invention obtains the shut-in well production index and engineering operation information of the target oil field, and screens the shut-in wells in the target oil field according to the shut-in well production index and engineering operation information to obtain the shut-in wells to be screened. The economic limit water content and economic limit daily oil production of the shut-in wells to be screened are determined, and then the shut-in wells to be screened are screened according to the economic limit water content and the economic limit daily oil production to obtain the target shut-in wells, and the machine learning algorithm is used to predict the production resumption potential of the target shut-in wells to determine the production resumption potential of the shut-in wells. This technical solution can accurately and efficiently evaluate the production resumption potential of shut-in wells, and has important guiding significance for the implementation of production resumption measures on site in the oil field.

[0024] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 This is a flow chart of a method for determining the production resumption potential of a shut-in well provided according to Embodiment 1 of the present invention;

[0027] Figure 2 This is a flow chart of the shutdown well resumption potential analysis based on machine learning provided in Example 1 of the present application;

[0028] Figure 3 It is a schematic diagram of the economic limit water cut of a single well corresponding to different daily liquid production and oil price conditions provided in Example 1 of the present application;

[0029] Figure 4 It is the relationship between the mean square error of the training data and the number of retained features provided in Example 1 of the present application;

[0030] Figure 5 It is the relationship between the mean square error of the test data and the number of retained features provided in Example 1 of the present application;

[0031] Figure 6 is the prediction result of the training model provided in Example 1 of the present application;

[0032] Figure 7 A schematic diagram of the structure of a device for determining the production resumption potential of a shut-in well provided in Embodiment 2 of the present invention;

[0033] Figure 8 It is a structural schematic diagram of an electronic device for implementing a method for shutting down a well and restoring production potential according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "to be screened", "target", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0036] Embodiment 1

[0037] Figure 1 This is a flow chart of a method for determining the production resumption potential of a shut-in well provided according to the first embodiment of the present invention. This embodiment is applicable to the case of predicting the production resumption potential of a shut-in well. The method can be executed by a shut-in well production resumption potential determination device. The shut-in well production resumption potential determination device can be implemented in the form of hardware and / or software. The shut-in well production resumption potential determination device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0038] S110. Obtaining production indicators and engineering operation information of shut-in wells in the target oil field.

[0039] In this plan, the production indicators of shut-in wells include the shut-in date, shut-in date, daily oil production, daily water production, water content, cumulative oil production, historical maximum oil production, average production indicators and production curves of surrounding producing wells, and various production indicators of re-producing wells. Among them, the distance between the shut-in well and the surrounding producing wells is usually less than 2 times the well spacing.

[0040] The engineering operation information may refer to engineering related information during the drilling and completion process, which may be obtained through engineering operation data.

[0041] In this embodiment, the geological characteristics of the target oil field may also be obtained, and the shut-in wells may be analyzed based on the geological characteristics, production indicators of the shut-in wells, and engineering operation information.

[0042] In this program, Figure 2 is a flowchart of the shutdown well resumption potential analysis based on machine learning provided in Example 1 of the present application, such as Figure 2 As described above, the shut-in well production index and engineering operation information of the target oil field are obtained.

[0043] S120, screening the shut-in wells in the target oil field according to the shut-in well production index and engineering operation information to obtain shut-in wells to be screened.

[0044] In this embodiment, the reasons for shutting down the shut-in wells are divided into three categories, namely, shut-in due to extremely high water content, shut-in due to low production and efficiency, and shut-in due to engineering operations. The reasons for shut-in can be analyzed based on the shut-in well production index and engineering operation information of the shut-in wells, and the shut-in wells to be screened can be selected.

[0045] Optionally, the shut-in wells in the target oil field are screened according to the shut-in well production index and engineering operation information to obtain the shut-in wells to be screened, including:

[0046] According to the shut-in well production index and engineering operation information, the shut-in wells with extremely high water content and the shut-in wells with low production and low efficiency in the shut-in wells in the target oil field are screened to obtain the shut-in wells to be screened.

[0047] In this scheme, if Figure 2 As shown, the reasons for shutting down can be analyzed based on the shut-in production indicators and engineering operation information of the shut-in wells to screen out the shut-in wells that are not restricted by engineering operation conditions, that is, to screen out the shut-in wells with extremely high water content and the shut-in wells with low production and low efficiency.

[0048] In this embodiment, if Figure 2 As shown, for shut-in of wells for engineering operations, it is necessary to conduct equipment inspection in combination with on-site equipment and tubing data in the oilfield to determine the cost and difficulty of resuming production. Specifically, it is possible to conduct inspections from aspects such as sand production, casing damage, fish dropping, and ESP failure to determine whether the shut-in of wells for engineering operations can be resumed. Among them, sand production refers to the phenomenon that during the production process of oil, gas and water wells, due to excessive production pressure difference, loose rock cementation of sandstone oil and gas layers, formation sand flows into the wellbore, blocking the oil and gas passages and causing the oil and gas wells to stop production; casing damage refers to the part of the casing in the sliding formation that will be pushed and damaged; fish dropping refers to the part of the drilling tool that falls or falls into the well during the handling process after the drilling tool breaks due to drill bit breakage, drill bit sticking and other accidents; ESP failure can refer to tubing equipment failure, etc.

[0049] By using the production indicators and engineering operation information of shut-in wells, we can screen the shut-in wells with extremely high water content and low production and low efficiency, and analyze the potential for resumption of production of wells with extremely high water content and low production and low efficiency, which has important guiding significance for the implementation of resumption of production measures on site in the oil field.

[0050] S130, determining the economic limit water content and economic limit daily oil production of the wells to be screened and shut down.

[0051] Among them, when the oil field (or oil well) is developed to a certain stage and its water content reaches a certain value, the after-tax output value of the oil well production in that year is equal to the operating cost. If the water content continues to rise, not only will there be no profit, but there will also be losses. At this time, the water content of the oil field (or oil well) is called the economic limit water content; the output of the oil well is called the economic limit oil production.

[0052] In this scheme, for shut-in wells with extremely high water content and shut-in wells with low production and low efficiency, an economic and technical limit model for shut-in wells is established based on the break-even principle, and the economic limit water content and economic limit daily oil production of different single wells are determined respectively. That is, the shut-in wells to be screened can be screened again based on the economic limit water content and economic limit daily oil production, so as to select the shut-in wells with the potential for resumption of production.

[0053] Among them, the break-even principle includes: crude oil production sales revenue = item acquisition cost + profit + item payment price. When the profit is 0, the corresponding production indicator is the economic limit indicator, and the formula can be simplified to: crude oil production sales revenue = item acquisition cost + item payment price.

[0054] Optionally, determining the economic limit water cut of the wells to be screened and shut down includes:

[0055] The economic limit moisture content is calculated using the following formula;

[0056]

[0057] Among them, f w represents the economic limit water content, C represents the item acquisition cost, T represents the item payment cost, P represents the crude oil sales value attribute value, R represents the crude oil commodity rate, S represents the oil production time rate, q L Indicates daily liquid production.

[0058] In this solution, the item acquisition cost C may refer to the operating cost, the item payment cost T may refer to the tax, and the crude oil sales value attribute value P may refer to the crude oil sales price.

[0059] In this embodiment, the economic limit water cut can be calculated by obtaining the operating cost, tax, crude oil sales price, crude oil commodity rate, oil production time rate and daily liquid production of the target oil field according to a preset economic limit water cut calculation formula.

[0060] By calculating the economic limit water cut, the shut-in wells to be screened can be screened based on the economic limit water cut, thereby selecting the shut-in wells with the potential for resumption of production.

[0061] Optionally, determining the economic limit daily oil production of the shut-in well to be screened includes:

[0062] The economic limit daily oil production is calculated using the following formula;

[0063] q o =q L (1-f w );

[0064] Among them, q o Indicates the economic limit daily oil production.

[0065] In this embodiment, the economic limit daily oil production can be calculated by obtaining the economic limit water content and the daily liquid production according to a preset economic limit daily oil production calculation formula.

[0066] By calculating the economic limit daily oil production, the shut-in wells to be screened can be screened based on the economic limit daily oil production, thereby screening out the shut-in wells with the potential for resumption of production.

[0067] S140, screening the shut-in wells to be screened according to the economic limit water cut and the economic limit daily oil production to obtain target shut-in wells.

[0068] In this scheme, the shut-in wells to be screened can be screened according to their economic limit water content and economic limit daily oil production to obtain the screened target shut-in wells.

[0069] Optionally, the shut-in wells to be screened are screened according to the economic limit water cut and the economic limit daily oil production to obtain target shut-in wells, including:

[0070] The shut-in wells to be screened, whose water cut before shut-in is less than the economic limit water cut and whose daily oil production before shut-in is greater than the economic limit daily oil production, are taken as target shut-in wells.

[0071] In this scheme, the economic limit water cut and the economic limit daily oil production can be used as constraints for the wells to be screened and shut down, and the wells to be screened and shut down whose water cut before shutting down is less than the economic limit water cut and whose daily oil production before shutting down is greater than the economic limit daily oil production are used as target wells to be shut down.

[0072] In this embodiment, if Figure 2 As shown, it is determined whether it is greater than the economic and technical limits. If so, the shut-in well to be screened will be used as the target shut-in well, and the target shut-in well will be further analyzed for its potential for resumption of production; if not, the shut-in well to be screened will not be considered for resumption of production, and the reasons why it is not suitable for resumption of production can be analyzed.

[0073] By screening the shut-in wells to be screened, the shut-in wells with the potential for resumption of production can be selected.

[0074] S150. Use a machine learning algorithm to predict the production resumption potential of the target shut-in well to determine the production resumption potential of the shut-in well; wherein the production resumption potential of the shut-in well is composed of main control characteristics and weights that affect the size of the production resumption potential of the shut-in well.

[0075] Among them, the machine learning algorithm can be a neural network model, an extreme gradient boosting (eXtreme GradientBoosting, XGBoost) model, etc.

[0076] In this scheme, the machine learning algorithm can be used to process the shut-in well production index and remaining oil distribution potential of the target shut-in well to obtain the main control characteristics, weights, and shut-in well resumption potential of the target shut-in well.

[0077] In this embodiment, if Figure 2 As shown in FIG. 1 , the machine learning algorithm can also be used to process the shut-in well production index, remaining oil distribution potential, and geological characteristics of the target shut-in well to obtain the main control characteristics, weights, and shut-in well recovery potential of the target shut-in well. That is, the target shut-in well is analyzed with reference to the geological characteristics. Among them, the geological characteristics include structural location, oil-water interface, connectivity, etc.

[0078] Optionally, a machine learning algorithm is used to predict the production resumption potential of the target shut-in well to determine the production resumption potential of the shut-in well, including:

[0079] The shut-in well production index and remaining oil distribution potential of the target shut-in well are processed using the extreme gradient lifting model to obtain the shut-in well resumption potential.

[0080] In this solution, the extreme gradient boosting model is an ensemble learning algorithm that improves the performance of the entire model by building multiple weak learners. Specifically, it continuously iterates the training, and each training focuses on the errors of the previous training and tries to correct these errors to reduce the loss.

[0081] In this embodiment, the better the geological characteristics, the shorter the shut-in time, the lower the water content before shut-in, the higher the daily oil production, cumulative oil production, historical maximum oil production and EUR (Economic Ultimate Recovery) before shut-in, the greater the potential for remaining oil distribution, indicating that the shut-in well has a relatively greater potential for resuming production.

[0082] Specifically, all the above indicators can be integrated together to construct a data set containing multiple indicators. The input of the system is recorded as x and the output is recorded as y. Then: y = f(x), the input x includes the characteristic variables of the shut-in well production index and the remaining oil distribution potential, and the output y is the recovery potential. Among them, the input x can also include the characteristic variables of geological characteristics, shut-in well production index and remaining oil distribution potential.

[0083] Furthermore, given a data set D containing n target shut-in wells, D = {(x i ,y i )|x i ∈R m ,y i ∈R,i=1,2,…,n}, each shut-in well has m indicators and the target shut-in well x i Corresponding recovery potential i Using the extreme gradient boosting model, the output of the jth prediction tree is for:

[0084]

[0085] Among them, f j is the regression tree, J is the total number of regression trees, and F is the regression tree space.

[0086] By using the extreme gradient lifting model to predict the resumption potential of the target shut-in wells, the resumption potential of the shut-in wells can be accurately and efficiently evaluated, which has important guiding significance for the implementation of resumption measures on site in the oil field.

[0087] Optionally, the shut-in well production index and remaining oil distribution potential of the target shut-in well are processed using the extreme gradient lifting model to obtain the shut-in well resumption potential, including:

[0088] Dividing the shut-in well production index and the remaining oil distribution potential of the target shut-in well to determine training data and test data;

[0089] Training the extreme gradient boosting model to be trained based on the training data to obtain the extreme gradient boosting model;

[0090] Based on the extreme gradient improvement model, the production resumption potential of the test data is predicted to obtain the production resumption potential of the shut-in wells.

[0091] In this scheme, the data set of the target shut-in well can be divided into training data and test data by random partitioning. Among them, 80% of the data is used as training data and 20% of the data is used as test data. The extreme gradient boosting model is trained using the training data, and then the trained model is used to predict the test data, and finally the main control features and weights that affect the potential for the shutdown well to resume production are determined, and the model can be used to efficiently predict the potential for the shutdown well to resume production. Among them, the main control feature can be one or more of the shut-in well production indicators.

[0092] By using the extreme gradient lifting model to predict the resumption potential of the target shut-in wells, the resumption potential of the shut-in wells can be accurately and efficiently evaluated, which has important guiding significance for the implementation of resumption measures on site in the oil field.

[0093] The technical solution of the embodiment of the present invention obtains the shut-in well production index and engineering operation information of the target oil field, and screens the shut-in wells in the target oil field according to the shut-in well production index and engineering operation information to obtain the shut-in wells to be screened. The economic limit water content and the economic limit daily oil production of the shut-in wells to be screened are determined, and then the shut-in wells to be screened are screened according to the economic limit water content and the economic limit daily oil production to obtain the target shut-in wells, and the machine learning algorithm is used to predict the production resumption potential of the target shut-in wells to determine the production resumption potential of the shut-in wells. By executing this technical solution, the data of the target shut-in wells are trained and tested using the principles of machine learning to obtain a stable evaluation model, thereby accurately and efficiently evaluating the production resumption potential of the shut-in wells, which has important guiding significance for the implementation of production resumption measures on site in the oil field.

[0094] In this plan, we screened the potential wells for resumption of production after a large number of production wells were shut down in a target oil field A in South America. Specifically, we obtained the geological characteristics, production indicators and engineering operation information of the shut-in wells in oil field A. Then, we analyzed the reasons for the shut-in based on the geological characteristics, production indicators and engineering operation information of the shut-in wells. Taking a typical well group as an example, we preliminarily screened out 27 shut-in wells with extremely high water content and low production and low efficiency that were not restricted by engineering operation conditions.

[0095] Furthermore, Figure 3 Schematic diagram of the economic limit water content of a single well under different daily liquid production and oil price conditions provided in Example 1 of the present application. The economic limit water content and economic limit daily oil production of the shut-in well are determined based on the actual economic indicators of the target oil field. Figure 3 As shown, there are 22 shut-in wells whose production indicators are greater than the economic and technical limits.

[0096] Furthermore, the indicator data of 17 shut-in wells were selected from the 22 shut-in wells as training data to train the XGBoost model, and then the other 5 shut-in wells were used as test data for prediction. Figure 4 is the relationship between the mean square error of the training data and the number of retained features provided in Example 1 of the present application, Figure 5 is the relationship between the mean square error of the test data and the number of retained features provided in Example 1 of the present application, such as Figure 4 and Figure 5 As shown in the figure, there are five main controlling factors for the potential for resumption of production of shut-in wells, namely daily oil production before shutdown, predicted daily oil production at the end of the contract period, EUR, water cut before shutdown and shutdown time, with weights of 0.93, 0.05, 0.008, 0.005 and 0.001 respectively. Figure 6 is the prediction result of the training model provided in Example 1 of the present application, Figure 6 (a) is the prediction result of the training data, Figure 6 (b) is the prediction result of the test data. The trained model is used to test 5 shut-in wells. Figure 6(a) and Figure 6 As shown in (b), the prediction effect is good and can be widely applied to other well groups for screening wells with production recovery potential.

[0097] Embodiment 2

[0098] Figure 7 This is a schematic diagram of the structure of a device for determining the production resumption potential of a shut-in well provided in the second embodiment of the present invention. Figure 7 As shown, the device comprises:

[0099] The parameter acquisition module 710 is used to obtain the shut-in well production index and engineering operation information of the target oil field;

[0100] A shut-in well screening module 720 is used to screen the shut-in wells in the target oil field according to the shut-in well production index and engineering operation information to obtain the shut-in wells to be screened;

[0101] The limit information determination module 730 is used to determine the economic limit water content and economic limit daily oil production of the well to be screened and shut down;

[0102] A target shut-in well screening module 740 is used to screen the shut-in wells to be screened according to the economic limit water cut and the economic limit daily oil production to obtain target shut-in wells;

[0103] The shut-in well resumption potential determination module 750 is used to use a machine learning algorithm to predict the resumption potential of the target shut-in well and determine the resumption potential of the shut-in well; wherein the shutdown well resumption potential is composed of the main control characteristics and weights that affect the size of the shutdown well resumption potential.

[0104] Optionally, the limit information determination module 730 is specifically configured to:

[0105] The economic limit moisture content is calculated using the following formula;

[0106]

[0107] Among them, f w represents the economic limit water content, C represents the item acquisition cost, T represents the item payment cost, P represents the crude oil sales value attribute value, R represents the crude oil commodity rate, S represents the oil production time rate, q L Indicates daily liquid production.

[0108] Optionally, the limit information determination module 730 is further configured to:

[0109] The economic limit daily oil production is calculated using the following formula;

[0110] q o =q L (1-f w );

[0111] Among them, q o Indicates the economic limit daily oil production.

[0112] Optionally, the shut-in well recovery potential determination module 750 includes:

[0113] The shut-in well resumption potential determination submodule is used to process the shut-in well production index and the remaining oil distribution potential of the target shut-in well by using the extreme gradient lifting model to obtain the shut-in well resumption potential.

[0114] Optional, shut-in well resumption potential determination submodule, specifically used for:

[0115] Dividing the shut-in well production index and the remaining oil distribution potential of the target shut-in well to determine training data and test data;

[0116] Training the extreme gradient boosting model to be trained based on the training data to obtain the extreme gradient boosting model;

[0117] Based on the extreme gradient improvement model, the production resumption potential of the test data is predicted to obtain the production resumption potential of the shut-in wells.

[0118] Optionally, the shut-in well screening module 720 is specifically used to:

[0119] According to the shut-in well production index and engineering operation information, the shut-in wells with extremely high water content and the shut-in wells with low production and low efficiency in the shut-in wells in the target oil field are screened to obtain the shut-in wells to be screened.

[0120] Optionally, the target shut-in well screening module 740 is specifically used to:

[0121] The shut-in wells whose water content before shutting down is less than the economic limit water content and whose daily oil production before shutting down is greater than the economic limit daily oil production are selected as target shut-in wells. A shut-in well resumption potential determination device provided in an embodiment of the present invention can execute a shut-in well resumption potential determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0122] Embodiment 3

[0123] Figure 8A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0124] like Figure 8 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0125] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0126] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a method for determining the potential for resumption of production of a shut-in well.

[0127] In some embodiments, a method for determining the potential for resumption of production of a shut-in well may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining the potential for resumption of production of a shut-in well described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute a method for determining the potential for resumption of production of a shut-in well by any other appropriate means (e.g., by means of firmware).

[0128] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0129] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0130] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0131] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0132] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0133] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0134] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0135] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining the production resumption potential of a shut-in well, characterized in that: include: Obtain shut-in well production indicators and engineering operation information for target oil fields; Screening the shut-in wells in the target oil field according to the shut-in well production index and engineering operation information to obtain the shut-in wells to be screened; Determine the economic limit water content and economic limit daily oil production of the wells to be screened and shut down; The shut-in wells to be screened are screened according to the economic limit water content and the economic limit daily oil production to obtain target shut-in wells; The resumption potential of the target shut-in well is predicted by using a machine learning algorithm to determine the resumption potential of the shut-in well; wherein the resumption potential of the shut-in well is composed of the main control characteristics and weights that affect the size of the resumption potential of the shut-in well.

2. The method according to claim 1, characterized in that Determining the economic limit water content of the wells to be screened and shut down includes: The economic limit moisture content is calculated using the following formula; Among them, f w represents the economic limit water content, C represents the item acquisition cost, T represents the item payment cost, P represents the crude oil sales value attribute value, R represents the crude oil commodity rate, S represents the oil production time rate, q L Indicates daily liquid production.

3. The method according to claim 2, characterized in that Determining the economic limit daily oil production of the well to be shut down, including: The economic limit daily oil production is calculated using the following formula; q o =q L (1-f w ); Among them, q o Indicates the economic limit daily oil production.

4. The method according to claim 1, characterized in that: The machine learning algorithm is used to predict the production resumption potential of the shut-in wells and determine the production resumption potential of the shut-in wells, including: The shut-in well production index and remaining oil distribution potential of the target shut-in well are processed using the extreme gradient lifting model to obtain the shut-in well resumption potential.

5. The method according to claim 4, characterized in that The shut-in well production index and remaining oil distribution potential of the target shut-in well are processed using the extreme gradient lifting model to obtain the shut-in well resumption potential, including: Dividing the shut-in well production index and the remaining oil distribution potential of the target shut-in well to determine training data and test data; Training the extreme gradient boosting model to be trained based on the training data to obtain the extreme gradient boosting model; Based on the extreme gradient improvement model, the production resumption potential of the test data is predicted to obtain the production resumption potential of the shut-in wells.

6. The method according to claim 1, characterized in that According to the shut-in well production index and engineering operation information, the shut-in wells in the target oil field are screened to obtain the shut-in wells to be screened, including: According to the shut-in well production index and engineering operation information, the shut-in wells with extremely high water content and the shut-in wells with low production and low efficiency in the shut-in wells in the target oil field are screened to obtain the shut-in wells to be screened.

7. The method according to claim 1, characterized in that The shut-in wells to be screened are screened according to the economic limit water cut and the economic limit daily oil production to obtain target shut-in wells, including: The shut-in wells to be screened, whose water content before shut-in is less than the economic limit water content and whose daily oil production before shut-in is greater than the economic limit daily oil production, are taken as target shut-in wells.

8. A device for determining the production resumption potential of a shut-in well, characterized in that: include: Parameter acquisition module, used to obtain shut-in well production indicators and engineering operation information of the target oil field; A shut-in well screening module, used to screen the shut-in wells in the target oil field according to the shut-in well production index and engineering operation information, to obtain the shut-in wells to be screened; A limit information determination module, used to determine the economic limit water content and economic limit daily oil production of the well to be screened and shut down; A target shut-in well screening module is used to screen the shut-in wells to be screened according to the economic limit water cut and the economic limit daily oil production to obtain the target shut-in wells; The shut-in well resumption potential determination module is used to use a machine learning algorithm to predict the resumption potential of the target shut-in well and determine the resumption potential of the shut-in well; wherein the shutdown well resumption potential is composed of the main control characteristics and weights that affect the size of the shutdown well resumption potential.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a method for determining the potential for resumption of production of a shut-in well as described in any one of claims 1-7.

10. A computer-readable medium, characterized in that The computer-readable medium stores computer instructions, and the computer instructions are used to enable a processor to implement a method for determining the production resumption potential of a shut-in well as described in any one of claims 1-7 when executed.

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