CN114016998A - Prediction method and device for logging encountering block - Google Patents
Prediction method and device for logging encountering block Download PDFInfo
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Abstract
The invention provides a prediction method and a device of a logging encounter block, wherein the prediction method of the logging encounter block comprises the following steps: obtaining logging data of a to-be-predicted encounter card of a target block, wherein the logging data comprises: the number of times of well descending of the SLAM cable, the number of times of well descending of the PCL cable, the well diameter, the well deviation and the mud density; and predicting the logging encounter card of the target block by using the logging data and a pre-established machine learning model. The method can accurately predict the problem of blockage during the logging construction process, reduce complex risks and provide technical support for the drilling construction operation of the directional well.
Description
Technical Field
The invention relates to the technical field of petroleum and natural gas exploration, in particular to a prediction method and a prediction device of a logging encounter card.
Background
The well logging technology is related technology for obtaining various petroleum geology and engineering technical data after petroleum drilling to the designed well depth, and is used as a necessary means for well completion and original data development of oil fields. According to geological and geophysical conditions, a comprehensive logging method is reasonably selected, so that the tasks of researching a drilling geological profile in detail, detecting useful mineral products, providing data required for calculating reserves in detail, such as the effective thickness, the porosity, the hydrocarbon saturation, the permeability and the like of an oil layer, researching the technical conditions of drilling and the like can be achieved. At present, the logging obstruction jam is caused by a plurality of reasons, the logging obstruction jam cannot be accurately predicted, the method is usually judged by manual experience, a system forming method is not provided, and the method has important significance for successfully logging in a certain area and reducing construction risks.
Disclosure of Invention
Aiming at the problems in the prior art, the method and the device for predicting the logging encounter jam can accurately predict the encounter jam problem and provide reference for the construction operation of directional well drilling. In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, the present invention provides a method for predicting a logging encounter jam, comprising:
obtaining logging data of a to-be-predicted encounter card of a target block, wherein the logging data comprises: the number of times of well descending of the SLAM cable, the number of times of well descending of the PCL cable, the well diameter, the well deviation and the mud density;
and predicting the logging encounter card of the target block by using the logging data and a pre-established machine learning model.
In one embodiment, the machine learning model is a feed-forward deep machine learning model, and the step of building the machine learning model includes:
collecting historical data of the target block logging data;
generating a label of the feedforward deep machine learning model according to historical encounter card data and historical data corresponding to the historical encounter card data;
and training an initial model of the feedforward deep machine learning model according to historical data with the signal-to-noise ratio larger than a preset value and the label to generate the machine learning model.
In one embodiment, the training an initial model of the feedforward deep machine learning model according to historical data with a signal-to-noise ratio greater than a preset value and a label to generate the machine learning model includes:
and generating a connection weight of the visible layer bias and a connection weight of the hidden layer bias according to the historical encounter card data, the historical data corresponding to the historical encounter card data, the visible layer bias and the hidden layer bias in the feedforward deep machine learning model so as to train the initial model.
In an embodiment, the generating connection weights of visible layer biases and connection weights of hidden layer biases according to historical encounter card data, historical data corresponding to the historical encounter card data, and visible layer biases and hidden layer biases in the feedforward deep machine learning model to train the initial model includes:
setting the initial value of the visible layer bias, the initial value of the hidden layer bias and the initial value of the connection weight as three random numbers smaller than a preset numerical value;
inputting the historical data into the initial model;
performing the following iterative operations until the prediction error of the initial model is smaller than a preset error to obtain the deep belief network model;
training the initial model to n-1 layers from bottom to top layer by layer, and inputting a training result to the nth layer of the initial model to obtain a training result of the nth layer; wherein n is the total number of training layers of the feedforward deep machine learning model;
calculating the prediction error according to the training result of the nth layer and the historical data;
and when the prediction error is larger than a preset error, optimizing the initial layer to the layer 1 from top to bottom layer by layer according to the prediction error.
In one embodiment, before the machine learning model is established, the method further includes:
filtering the historical data;
normalizing the historical data;
removing outliers in the historical data.
In one embodiment, the historical encounter card data includes: riser pressure, hook load, wellhead pressure, outlet flow, pump stroke, inlet flow, while drilling, bottom hole annulus pressure, total hydrocarbons, C1 data, and operating condition data;
the working condition data comprises: overflow, loss and normal.
In a second aspect, the present invention provides a logging encounter stuck tool prediction device, comprising:
the logging data acquisition unit is used for acquiring logging data of an obstacle card to be predicted of a target block, and the logging data comprises: the number of times of well descending of the SLAM cable, the number of times of well descending of the PCL cable, the well diameter, the well deviation and the mud density;
and the encounter block prediction unit is used for predicting the logging encounter block of the target block by using the logging data and a pre-established machine learning model.
In one embodiment, the machine learning model is a feed-forward deep machine learning model, and the prediction apparatus for logging encounter-block further includes: a model building unit for building the machine learning model, the model building unit comprising:
the historical data collection module is used for collecting historical data of the target block logging data;
the label generating module is used for generating a label of the feedforward deep machine learning model according to historical encounter card data and corresponding historical data;
and the model training module is used for training the initial model of the feedforward deep machine learning model according to the historical data with the signal-to-noise ratio larger than a preset value and the label so as to generate the machine learning model.
In one embodiment, the model training module comprises:
and the initial model training module is used for generating the connection weight of the visible layer bias and the connection weight of the hidden layer bias according to the historical encounter card data, the historical data corresponding to the historical encounter card data, the visible layer bias and the hidden layer bias in the feedforward deep machine learning model so as to train the initial model.
In one embodiment, the initial model training module comprises:
a random number setting module, configured to set an initial value of the visible layer bias, an initial value of the hidden layer bias, and an initial value of the connection weight as three random numbers smaller than a preset value, respectively;
a historical data input module for inputting the historical data into the initial model;
the iteration operation module is used for carrying out the following iteration operation until the prediction error of the initial model is smaller than a preset error, so as to obtain the deep belief network model;
training the initial model to n-1 layers from bottom to top layer by layer, and inputting a training result to the nth layer of the initial model to obtain a training result of the nth layer; wherein n is the total number of training layers of the feedforward deep machine learning model;
calculating the prediction error according to the training result of the nth layer and the historical data;
and when the prediction error is larger than a preset error, optimizing the initial layer to the layer 1 from top to bottom layer by layer according to the prediction error.
In one embodiment, the logging encounter card predicting device further comprises:
the data filtering unit is used for filtering the historical data;
the data normalization unit is used for normalizing the historical data;
an abnormal value removing unit for removing an abnormal value in the history data.
In one embodiment, the historical encounter card data includes: riser pressure, hook load, wellhead pressure, outlet flow, pump stroke, inlet flow, while drilling, bottom hole annulus pressure, total hydrocarbons, C1 data, and operating condition data;
the working condition data comprises: overflow, loss and normal.
In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for predicting a logging encounter block when executing the program.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a method of predicting a logging encounter block.
As can be seen from the above description, the method and apparatus for predicting a logging encounter block according to the embodiment of the present invention first obtain logging data of a target block to be predicted encounter block, where the logging data includes: the number of times of well descending of the SLAM cable, the number of times of well descending of the PCL cable, the well diameter, the well deviation and the mud density; and predicting the logging encounter card of the target block by using the logging data and a pre-established machine learning model. The method can accurately predict the problem of blockage during the logging construction process, reduce complex risks and provide technical support for the drilling construction operation of the directional well.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of a first configuration of a logging encounter card prediction system according to an embodiment of the present disclosure;
FIG. 2 is a second schematic diagram of a system for predicting a logging encounter block according to an embodiment of the present disclosure;
FIG. 3 is a first flowchart of a method for predicting a logging encounter block according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating step 300 according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating step 303 according to an embodiment of the present invention;
FIG. 6 is a schematic flow chart of step 3031 in an embodiment of the present invention;
FIG. 7 is a second flowchart of a method for predicting a logging encounter block according to an embodiment of the present invention;
FIG. 8 is a schematic flow chart of a method for predicting a logging encounter block in an embodiment of the present invention;
FIG. 9 is a block diagram of a first configuration of a logging encounter prediction device in accordance with an embodiment of the present invention;
FIG. 10 is a block diagram of a second configuration of a logging encounter card prediction device in accordance with an embodiment of the present invention;
FIG. 11 is a schematic structural diagram of a model building unit according to an embodiment of the present invention;
FIG. 12 is a block diagram of a model training module according to an embodiment of the present invention;
FIG. 13 is a block diagram of an initial model training module according to an embodiment of the present invention;
FIG. 14 is a block diagram of a third configuration of a logging encounter prediction device in accordance with an embodiment of the present invention;
fig. 15 is a schematic structural diagram of an electronic device in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The application provides a prediction system of a logging encounter card, which comprises a prediction device of the logging encounter card, and referring to fig. 1, the prediction device can be a server a1, the server a1 can be in communication connection with a plurality of logging data receiving terminals B1, the server a1 can also be in communication connection with a plurality of databases respectively, or as shown in fig. 2, the databases can also be arranged in the server a 1. The drilling data receiving end B1 is used for receiving data such as riser pressure, hook load, wellhead pressure, outlet flow, pump stroke, inlet flow, drilling time, bottom hole annular pressure, total hydrocarbons, C1 data, working conditions and the like in the drilling process. After the server A1 collects the logging data, the logging data is predicted in real time, and the prediction result is displayed to the user through the client C1.
It is understood that the logging data receiving end B1 may be a sensor, which may be a pressure sensor, a flow sensor, a displacement sensor, a gas sensor, etc., and the client C1 may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..
In practical applications, part of the prediction of logging encounter-encounter cards may be performed on the side of the server a1 as described above, i.e., the architecture shown in fig. 1 or fig. 2, or all operations may be performed in the client C1 device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. If all the operations are completed in the client device, the client device may further include a processor for performing operations such as processing of the drilling overflow and loss condition prediction result.
The client C1 device may have a communication module (i.e., a communication unit) to communicate with a remote server for data transmission. The server may include a server on the prediction side of the logging encounter card, or may include a server on an intermediate platform in other implementations, such as a server on a third party server platform communicatively linked to the prediction server of the logging encounter card. The server may comprise a single computer device, or may comprise a server cluster formed by a plurality of servers, or a server structure of a distributed device.
The server and client devices may communicate using any suitable network protocol, including network protocols not yet developed at the filing date of this application. The network protocols may include, for example, TCP/IP protocol, UDP/IP protocol, HTTP protocol, HTTPS protocol, and the like. Of course, the network Protocol may also include, for example, an RPC Protocol (Remote Procedure Call Protocol) used above the above Protocol, a REST Protocol (Representational State Transfer Protocol), and the like.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The embodiment of the present invention provides a specific implementation of a prediction method for a logging encounter block, and referring to fig. 3, the method specifically includes the following steps:
step 100: and acquiring logging data of the block to be predicted encountering card of the target block.
It is understood that the log data in step 100 includes: SLAM wireline downhole times, PCL downhole times, hole diameter, well deviation, and mud density. Specifically, statistical data of actual logging operation on site of the target block is collected, and preparation is made for main control factors of logging jam. The method mainly comprises the steps of meeting the depth of a block, locating the slurry density of the block, locating the well deviation by the block, putting the SLAM cable into the well, putting the PCL into the well and the like.
Step 200: and predicting the logging encounter card of the target block by using the logging data and a pre-established machine learning model.
It is understood that a machine learning model refers to the use of an algorithm to parse data, learn from it, and then make decisions or predictions about something, which may improve the performance of a particular algorithm in empirical learning, computer algorithms that may be automatically improved by experience, typically using data or past experience as a performance criterion for an optimization algorithm.
The drifting treatment caused by blockage and blockage in the logging process is one of important factors influencing the logging time effectiveness. Because of complicated petroleum geological conditions, the exploration and development are often carried out by adopting a small target area, large inclination and large displacement directional well technology, meanwhile, in recent years, in order to improve the exploration efficiency, shorten the engineering period and accelerate the drilling progress, if a well bore and slurry are not well treated, the complex well bore environments such as unstable well wall, poor well bore track and the like can be caused, and the probability of blockage and blocking in the logging construction is greatly increased. The logging jam condition of the target work area can be accurately predicted through the convenience of machine learning and based on a large amount of historical data.
As can be seen from the above description, in the prediction method for logging an encounter card in a target block, logging data of the encounter card to be predicted in the target block is first obtained, where the logging data includes: the number of times of well descending of the SLAM cable, the number of times of well descending of the PCL cable, the well diameter, the well deviation and the mud density; and predicting the logging encounter card of the target block by using the logging data and a pre-established machine learning model. The method can accurately predict the problem of blockage during the logging construction process, reduce complex risks and provide technical support for the drilling construction operation of the directional well.
In one embodiment, the machine learning model is a feed-forward deep machine learning model, and the method for predicting the logging encounter-block further includes: step 300: and establishing the machine learning model. Referring to fig. 4, step 300 further comprises:
step 301: collecting historical data of the target block logging data;
specifically, the number of times of well descending of the SLAM cable, the number of times of well descending of the PCL, the well diameter, the well deviation and the mud density corresponding to the passing encountered block of the target block are collected.
Step 302: generating a label of the feedforward deep machine learning model according to historical encounter card data and historical data corresponding to the historical encounter card data;
step 303: and training an initial model of the feedforward deep machine learning model according to historical data with the signal-to-noise ratio larger than a preset value and the label to generate the machine learning model.
In the steps 301 to 303, the logging data with high signal-to-noise ratio is selected as sample data, the SLAM cable well descending times, the PCL well descending times, the well diameter, the well deviation and the mud density are calibrated as model training constraint conditions, and the sample and the label are trained by adopting a feedforward deep learning network to obtain a configured machine learning model.
In one embodiment, referring to fig. 5, step 303 further comprises:
step 3031: and generating a connection weight of the visible layer bias and a connection weight of the hidden layer bias according to the historical encounter card data, the historical data corresponding to the historical encounter card data, the visible layer bias and the hidden layer bias in the feedforward deep machine learning model so as to train the initial model.
In one embodiment, referring to fig. 6, step 3031 further includes:
step 30311: setting the initial value of the visible layer bias, the initial value of the hidden layer bias and the initial value of the connection weight as three random numbers smaller than a preset numerical value;
step 30312: inputting the historical data into the initial model;
step 30313: performing the following iterative operations until the prediction error of the initial model is smaller than a preset error to obtain the deep belief network model;
training the initial model to n-1 layers from bottom to top layer by layer, and inputting a training result to the nth layer of the initial model to obtain a training result of the nth layer; wherein n is the total number of training layers of the feedforward deep machine learning model;
calculating the prediction error according to the training result of the nth layer and the historical data;
and when the prediction error is larger than a preset error, optimizing the initial layer to the layer 1 from top to bottom layer by layer according to the prediction error.
In steps 30311 to 30313, the initial value of the connection weight, the initial value of the visible layer bias, and the hidden layer bias may be set to the same random number, or may be set to three different random numbers. Substituting the preprocessed training data set into the initial model, training layer by layer from bottom to top to obtain parameters of each layer such as connection weight, visible layer bias, hidden layer bias and the like until n-1 layers are trained, inputting the training result of the n-1 layer into the machine learning model of the n (top) layer, comparing the training result of the n layer with the working condition data (actual data) in the training data set, and obtaining the corresponding error. And then, transmitting the error layer by layer from top to bottom, finely adjusting the connection weight, the visible layer offset and the hidden layer offset in each layer according to the error of each layer until the layer 1 is reached, then training layer by layer upwards by utilizing training data, and repeating … … circularly until the error is smaller than the preset error to obtain the deep belief network model.
In one embodiment, referring to fig. 7, before step 300, the method for predicting a logging encounter-block further comprises:
step 400: filtering the historical data;
step 500: normalizing the historical data;
step 600: removing outliers in the historical data.
It can be understood that the historical data needs to be preprocessed, including filtering, normalization and other processing, to remove unexpected values, and the historical data is normalized to be processed into a format required by the feedforward deep machine learning model.
In one embodiment, the historical encounter card data includes: riser pressure, hook load, wellhead pressure, outlet flow, pump stroke, inlet flow, while drilling, bottom hole annulus pressure, total hydrocarbons, C1 data, and operating condition data;
the working condition data comprises: overflow, loss and normal.
To further illustrate the present invention, the present invention provides a specific application example of the prediction method of the logging encounter block by taking the K block as an example, and specifically includes the following contents, see fig. 8.
The K block R layer is under-compacted, and the conditions of well wall collapse, large well diameter expansion rate (commonly called as 'big belly', the well diameter expansion rate is more than 0.15 and is 'big belly') and the like can occur when drilling parameters are improper; the S layer and the L layer mainly use mud shale, and the mud shale expands when meeting water, so that necking, drilling sticking and the like can be formed; the SS layer is mainly formed by sand-mud-rock interbedded layers, and the drilling parameters are improper, so that the diameter of the sugarcoated haws on a stick can be formed. And in well logging construction, the problem of blockage in directional well logging is found to be serious (52%).
S1: and establishing a feedforward deep machine learning model.
It will be appreciated that the goal of the feed forward network is to approximate a function. For example, for a classifier, y ═ f (x) maps the input x to a class y. The feed forward network defines a mapping y ═ f (x; θ) and learns the values of the parameters θ so that it can get the best functional approximation. In the feedforward neural network, parameters are propagated unidirectionally from an input layer to an output layer, and the feedforward deep learning network generally comprises hidden layer bias, visible layer bias, convolution layer, pooling layer and full-connection layer.
S2: and acquiring logging data of the block K to be predicted encountering card in real time.
The log data in step S2 includes: SLAM wireline downhole times, PCL downhole times, hole diameter, well deviation, and mud density.
S3: the log data in step S2 is preprocessed.
In specific implementation, the preprocessing in S3 includes the following steps: and (3) carrying out operations such as filtering, normalization and abnormal value removal on the training data set and the test data set to be processed into a format required by the feedforward deep machine learning model, wherein the data is in a range of [0,1 ].
Preferably, the normalization method selected in step S3 is: the data is divided by the largest of the values in the set of data.
xi' -certain data after normalization, xi-normalizing certain data before processing, xj-normalizing all data before processing.
S4: and predicting the logging encounter card of the K block by using logging data and a feedforward type deep machine learning model.
Specifically, the logging data preprocessed by step S3 is input into the feed-forward deep machine learning model generated by step S1 to predict the K-block logging encounter stuck condition.
As can be seen from the above description, in the prediction method for logging an encounter card in a target block, logging data of the encounter card to be predicted in the target block is first obtained, where the logging data includes: the number of times of well descending of the SLAM cable, the number of times of well descending of the PCL cable, the well diameter, the well deviation and the mud density; and predicting the logging encounter card of the target block by using the logging data and a pre-established machine learning model. The method can accurately predict the problem of blockage during the logging construction process, reduce complex risks and provide technical support for the drilling construction operation of the directional well.
Based on the same inventive concept, the embodiment of the present application further provides a prediction device of a logging encounter-block, which can be used to implement the method described in the above embodiment, such as the following embodiments. Because the principle of the prediction device for the logging encounter card to solve the problem is similar to the prediction method for the logging encounter card, the implementation of the prediction device for the logging encounter card can be implemented by referring to the implementation of the prediction method for the logging encounter card, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
The embodiment of the invention provides a specific implementation mode of a prediction device of a logging encounter card, which can realize a prediction method of the logging encounter card, and referring to fig. 9, the prediction device of the logging encounter card specifically comprises the following contents:
the logging data acquiring unit 10 is configured to acquire logging data of an obstacle to be predicted of a target block, where the logging data includes: the number of times of well descending of the SLAM cable, the number of times of well descending of the PCL cable, the well diameter, the well deviation and the mud density;
and an encounter block prediction unit 20, configured to predict a logging encounter block of the target block by using the logging data and a pre-established machine learning model.
In an embodiment, referring to fig. 10, the machine learning model is a feed-forward deep machine learning model, and the prediction apparatus for logging encounter-block further includes: a model building unit 30 for building the machine learning model, see fig. 11, the model building unit 30 comprising:
a historical data collecting module 301, configured to collect historical data of the target block logging data;
a label generation module 302, configured to generate a label of the feed-forward deep machine learning model according to historical encounter card data and historical data corresponding to the encounter card data;
the model training module 303 is configured to train an initial model of the feedforward deep machine learning model according to the historical data and the label, where the signal-to-noise ratio is greater than a preset value, to generate the machine learning model.
In one embodiment, referring to fig. 12, the model training module 303 includes:
an initial model training module 3031, configured to generate a connection weight of a visible layer bias and a connection weight of a hidden layer bias according to historical encounter card data, historical data corresponding to the historical encounter card data, and the visible layer bias and the hidden layer bias in the feed-forward deep machine learning model, so as to train the initial model.
In one embodiment, referring to fig. 13, the initial model training module 3031 includes:
a random number setting module 30311, configured to set the initial value of the visible layer bias, the initial value of the hidden layer bias, and the initial value of the connection weight as three random numbers smaller than a preset value;
a historical data input module 30312, configured to input the historical data into the initial model;
an iterative operation module 30313, configured to perform the following iterative operations until a prediction error of the initial model is smaller than a preset error, so as to obtain the deep belief network model;
training the initial model to n-1 layers from bottom to top layer by layer, and inputting a training result to the nth layer of the initial model to obtain a training result of the nth layer; wherein n is the total number of training layers of the feedforward deep machine learning model;
calculating the prediction error according to the training result of the nth layer and the historical data;
and when the prediction error is larger than a preset error, optimizing the initial layer to the layer 1 from top to bottom layer by layer according to the prediction error.
In one embodiment, referring to fig. 14, the logging encounter stuck tool prediction apparatus further comprises:
a data filtering unit 40 for filtering the history data;
a data normalization unit 50, configured to normalize the history data;
an abnormal value removing unit 60 for removing an abnormal value in the history data.
In one embodiment, the historical encounter card data includes: riser pressure, hook load, wellhead pressure, outlet flow, pump stroke, inlet flow, while drilling, bottom hole annulus pressure, total hydrocarbons, C1 data, and operating condition data;
the working condition data comprises: overflow, loss and normal.
As can be seen from the above description, the prediction apparatus for a logging encounter block according to the embodiment of the present invention first obtains logging data of a target block to be predicted encounter block, where the logging data includes: the number of times of well descending of the SLAM cable, the number of times of well descending of the PCL cable, the well diameter, the well deviation and the mud density; and predicting the logging encounter card of the target block by using the logging data and a pre-established machine learning model. The method can accurately predict the problem of blockage during the logging construction process, reduce complex risks and provide technical support for the drilling construction operation of the directional well.
The apparatuses, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or implemented by a product with certain functions. A typical implementation device is an electronic device, which may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
In a typical example, the electronic device specifically includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the steps of the method for predicting a logging encounter obstruction card, the steps including:
step 100: obtaining logging data of a to-be-predicted encounter card of a target block, wherein the logging data comprises: the number of times of well descending of the SLAM cable, the number of times of well descending of the PCL cable, the well diameter, the well deviation and the mud density;
step 200: and predicting the logging encounter card of the target block by using the logging data and a pre-established machine learning model.
Referring now to FIG. 15, shown is a schematic diagram of an electronic device 600 suitable for use in implementing embodiments of the present application.
As shown in fig. 15, the electronic apparatus 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate works and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM)) 603. In the RAM603, various programs and data necessary for the operation of the system 600 are also stored. The CPU601, ROM602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted as necessary on the storage section 608.
In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, an embodiment of the present invention includes a computer-readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the steps of the method for predicting a logging encounter obstruction card described above, the steps comprising:
step 100: obtaining logging data of a to-be-predicted encounter card of a target block, wherein the logging data comprises: the number of times of well descending of the SLAM cable, the number of times of well descending of the PCL cable, the well diameter, the well deviation and the mud density;
step 200: and predicting the logging encounter card of the target block by using the logging data and a pre-established machine learning model.
In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (14)
1. A prediction method of logging encounter-block card is characterized by comprising the following steps:
obtaining logging data of a to-be-predicted encounter card of a target block, wherein the logging data comprises: the number of times of well descending of the SLAM cable, the number of times of well descending of the PCL cable, the well diameter, the well deviation and the mud density;
and predicting the logging encounter card of the target block by using the logging data and a pre-established machine learning model.
2. The method of predicting a logging encounter-block as in claim 1, wherein the machine learning model is a feed-forward deep machine learning model, and the step of building the machine learning model comprises:
collecting historical data of the target block logging data;
generating a label of the feedforward deep machine learning model according to historical encounter card data and historical data corresponding to the historical encounter card data;
and training an initial model of the feedforward deep machine learning model according to historical data with the signal-to-noise ratio larger than a preset value and the label to generate the machine learning model.
3. The method for predicting a logging encounter-block as claimed in claim 2, wherein the training of the initial model of the feedforward deep machine learning model according to the historical data of the snr greater than a preset value and the label to generate the machine learning model comprises:
and generating a connection weight of the visible layer bias and a connection weight of the hidden layer bias according to the historical encounter card data, the historical data corresponding to the historical encounter card data, the visible layer bias and the hidden layer bias in the feedforward deep machine learning model so as to train the initial model.
4. The method for predicting a logging encounter card of claim 3, wherein the training the initial model by generating the connection weights of the visible layer bias and the connection weights of the hidden layer bias according to the historical encounter card data, the historical data corresponding to the historical encounter card data, the visible layer bias and the hidden layer bias in the feedforward deep machine learning model comprises:
setting the initial value of the visible layer bias, the initial value of the hidden layer bias and the initial value of the connection weight as three random numbers smaller than a preset numerical value;
inputting the historical data into the initial model;
performing the following iterative operations until the prediction error of the initial model is smaller than a preset error to obtain the deep belief network model;
training the initial model to n-1 layers from bottom to top layer by layer, and inputting a training result to the nth layer of the initial model to obtain a training result of the nth layer; wherein n is the total number of training layers of the feedforward deep machine learning model;
calculating the prediction error according to the training result of the nth layer and the historical data;
and when the prediction error is larger than a preset error, optimizing the initial layer to the layer 1 from top to bottom layer by layer according to the prediction error.
5. The method of predicting a logging encounter obstruction card of claim 2, further comprising, prior to establishing the machine learning model:
filtering the historical data;
normalizing the historical data;
removing outliers in the historical data.
6. The method of predicting a well logging encounter jam of claim 2, wherein the historical encounter jam data includes: riser pressure, hook load, wellhead pressure, outlet flow, pump stroke, inlet flow, while drilling, bottom hole annulus pressure, total hydrocarbons, C1 data, and operating condition data;
the working condition data comprises: overflow, loss and normal.
7. A logging encounter stuck tool prediction device, comprising:
the logging data acquisition unit is used for acquiring logging data of an obstacle card to be predicted of a target block, and the logging data comprises: the number of times of well descending of the SLAM cable, the number of times of well descending of the PCL cable, the well diameter, the well deviation and the mud density;
and the encounter block prediction unit is used for predicting the logging encounter block of the target block by using the logging data and a pre-established machine learning model.
8. The apparatus for predicting a logging encounter obstruction card of claim 7, wherein the machine learning model is a feed-forward deep machine learning model, further comprising: a model building unit for building the machine learning model, the model building unit comprising:
the historical data collection module is used for collecting historical data of the target block logging data;
the label generating module is used for generating a label of the feedforward deep machine learning model according to historical encounter card data and corresponding historical data;
and the model training module is used for training the initial model of the feedforward deep machine learning model according to the historical data with the signal-to-noise ratio larger than a preset value and the label so as to generate the machine learning model.
9. The device for predicting a logging encounter obstruction card of claim 8, wherein the model training module comprises:
and the initial model training module is used for generating the connection weight of the visible layer bias and the connection weight of the hidden layer bias according to the historical encounter card data, the historical data corresponding to the historical encounter card data, the visible layer bias and the hidden layer bias in the feedforward deep machine learning model so as to train the initial model.
10. The apparatus for predicting a logging encounter obstruction card of claim 9, wherein the initial model training module comprises:
a random number setting module, configured to set an initial value of the visible layer bias, an initial value of the hidden layer bias, and an initial value of the connection weight as three random numbers smaller than a preset value, respectively;
a historical data input module for inputting the historical data into the initial model;
the iteration operation module is used for carrying out the following iteration operation until the prediction error of the initial model is smaller than a preset error, so as to obtain the deep belief network model;
training the initial model to n-1 layers from bottom to top layer by layer, and inputting a training result to the nth layer of the initial model to obtain a training result of the nth layer; wherein n is the total number of training layers of the feedforward deep machine learning model;
calculating the prediction error according to the training result of the nth layer and the historical data;
and when the prediction error is larger than a preset error, optimizing the initial layer to the layer 1 from top to bottom layer by layer according to the prediction error.
11. The well logging encounter stuck card prediction device of claim 9, further comprising:
the data filtering unit is used for filtering the historical data;
the data normalization unit is used for normalizing the historical data;
an abnormal value removing unit for removing an abnormal value in the history data.
12. The well logging encounter block prediction device of claim 9 wherein the historical encounter block data comprises: riser pressure, hook load, wellhead pressure, outlet flow, pump stroke, inlet flow, while drilling, bottom hole annulus pressure, total hydrocarbons, C1 data, and operating condition data;
the working condition data comprises: overflow, loss and normal.
13. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method of predicting a well logging encounter block of any of claims 1 to 6.
14. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of predicting a logging encounter-block according to any of claims 1 to 6.
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