CN112360341B - Machine learning-based sliding directional drilling tool face state identification method - Google Patents
Machine learning-based sliding directional drilling tool face state identification method Download PDFInfo
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- CN112360341B CN112360341B CN202011032164.4A CN202011032164A CN112360341B CN 112360341 B CN112360341 B CN 112360341B CN 202011032164 A CN202011032164 A CN 202011032164A CN 112360341 B CN112360341 B CN 112360341B
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B7/00—Special methods or apparatus for drilling
- E21B7/04—Directional drilling
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B7/00—Special methods or apparatus for drilling
- E21B7/04—Directional drilling
- E21B7/10—Correction of deflected boreholes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
Abstract
The invention discloses a machine learning-based sliding directional drilling tool face state identification method, which comprises the steps of collecting tool face angles and acquisition moments of sliding directional drilling operation, constructing a discrete state model of a tool face, normalizing the tool face angles to obtain a normalized tool face angle sequence, carrying out unique hot coding on marked states according to combinations of the tool face angles to obtain state codes corresponding to each tool face sequence, using the tool face angle sequence and the corresponding state codes as training data, and training a preset BP neural network to obtain a tool face state identification model; and in the sliding directional drilling operation process, obtaining a normalized tool face angle sequence at the current moment, inputting the normalized tool face angle sequence into a tool face state identification model to obtain a state code, and determining the state of the current equipment. The invention can accurately identify the states of tool face error data, stable state, change direction, change speed and the like, and realizes the real-time monitoring of the tool face state.
Description
Technical Field
The invention belongs to the technical field of petroleum and gas drilling, and particularly relates to a machine learning-based sliding directional drilling tool face state identification method.
Background
Along with the exploitation of a large amount of oil and gas resources, the problems of low resource utilization rate, high exploitation difficulty and the like are more and more obvious. Particularly for the development of unconventional hydrocarbon reservoirs, it is often necessary to utilize sliding directional well techniques to drill to the desired horizon. FIG. 1 is a schematic diagram of a horizontal well drilling system. Currently, sliding directional drilling has been developed as the primary method of directional well trajectory control.
During sliding directional drilling, it is necessary to adjust the toolface (i.e., control the drilling toolface angle) at any time to maintain stable and dynamic control of the toolface to ensure that it can drill according to a predetermined trajectory. Fig. 2 is a schematic view of a toolface angle. As shown in fig. 2, the toolface angle is the angle of the deflecting toolface during sliding directional drilling and is divided into an inclinometer magnetometer face and an inclinometer face (unless otherwise specified, the toolface angle is usually referred to as the inclinometer face). The sliding directional drilling process is essentially an adjustment process for the toolface angle.
An important basis for adjusting the toolface control parameters is the current state of the toolface. The toolface conditions typically include erroneous data, steady state, rotational direction, rate of change, etc., and accurate identification of these conditions has a significant impact on the directional drilling process and benefits. However, due to the restriction of technologies such as sensors and data transmission, the real-time measurement and synchronous monitoring of the tool face angle cannot be achieved at present, and the state of the tool face is difficult to identify due to the delay or distortion in the information transmission process.
Currently, in an actual production process, in order to help an orientation engineer to make a tool face state judgment, a history tool face angle is recorded by a specific instrument panel (as shown in fig. 2 (c)). Engineers identify the current tool face state according to experience so as to guide the field to carry out directional operation, the individual factor difference is large, and the operation efficiency is different from person to person. Meanwhile, due to the influence of factors such as bit pressure, drilling tool combination, texture environment and the like, the change condition of the underground tool surface is complex, and data are discrete and nonlinear. The manual state identification is difficult and the accuracy is not high, so that the actual drilling track is easy to deviate from the preset drilling track.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a method for identifying the state of a sliding directional drilling tool face based on machine learning, and provides a discrete state model of the sliding directional drilling tool face.
In order to realize the purpose, the sliding directional drilling tool face state identification method based on the BP neural network comprises three stages: (1) establishing a tool surface discrete state model (S1-S3); (2) performing Onehot coding on various states to construct a state identification BP network (S4-S6); (3) performing online state identification (S7) by applying the BP network obtained by training, and specifically comprising the following steps:
s1: collecting tool face data of the sliding directional drilling operation, wherein the tool face data comprises a tool face angle and a collection time;
s2: extracting a plurality of tool face angle sequences from the tool face data, wherein each tool face angle sequence comprises tool face angles at continuous L acquisition moments;
s3: and carrying out state labeling on each tool face angle sequence, wherein the state labeling comprises error data, a stable state, a change direction and a change speed, and the specific method comprises the following steps:
judging error data of the tool face corner sequence, if no error data exists, marking the error data as 0, and if the error data exists, marking the error data as a serial number of the error data in the tool face corner sequence;
judging the stability of the tool face angle sequence, if the tool face angle sequence is unstable, marking the tool face angle sequence as 0, otherwise, marking the tool face angle sequence as 1;
judging the change direction of the tool face angle in the tool face angle sequence, if the change direction is anticlockwise change, marking the change direction as 0, and if the change direction is clockwise change, marking the change direction as 1;
dividing the change speed into M grades in advance, calculating the change speed of the tool face angle in the tool face angle sequence to obtain a corresponding grade, and marking the corresponding grade as the change speed grade of the tool face angle sequence;
s4: normalizing each tool face angle into data in a range of [0,1] to obtain a normalized tool face angle sequence;
s5: performing OneHot coding on the marked states according to the combination of the marked states to obtain state codes corresponding to each tool face sequence;
s6: constructing a BP neural network model, taking the normalized tool face angle sequence as input and the corresponding state code as expected output, and training the BP neural network model to obtain a tool face state identification network;
s7: in the process of the sliding directional drilling operation, tool face angles at the current time and the first L-1 times form a tool face angle sequence, normalization is carried out according to the method in the step S4, then the tool face angle sequence after normalization is input into the tool face state identification network trained in the step S6, and state codes are obtained, so that the error data, the stable state, the change direction and the change speed state of the tool face are determined.
Further, the judgment rule of the error data in step S3 is as follows:
sequentially judging each tool face angle in the tool face angle sequence, and acquiring the tool face angle with the acquisition time t
Calculating the face angle of the tool when determining the error data
The last correct tool face angle
Difference of (2)
If it is not
If the value is less than the preset threshold value, the face angle of the tool
If the data is correct, otherwise, respectively calculating the tool face angle at the next acquisition time t +1
And
if a difference is present between
Then judge the tool face angle with the collection time t
Recording the serial number of the error data, otherwise, the error data is correct data; and stopping when the tool face angle obtained by judgment is error data, otherwise, continuously judging the next tool face angle until the whole tool face angle sequence is judged.
Further, the stability determination rule in step S3 is as follows: and for the tool face angle sequence, respectively calculating the interval between adjacent tool faces and the interval between the tool face at the end of the sequence and the rest tool faces, if a certain interval is greater than a preset threshold value, judging that the tool face angle sequence is unstable, and if not, judging that the tool face angle sequence is stable.
Further, the normalization in step S5 is normalized using Min-Max normalization.
The invention relates to a method for identifying the state of a sliding directional drilling tool face based on machine learning, which comprises the steps of collecting tool face angles and acquisition moments of sliding directional drilling operation, constructing a discrete state model of the tool face, namely extracting a plurality of tool face angle sequences, carrying out state marking on each tool face angle sequence, wherein the state marking comprises error data, a stable state, a change direction and a change speed, then normalizing the tool face angles to obtain a normalized tool face angle sequence, carrying out unique hot coding on the marked states according to the combination of the marked states to obtain a state code corresponding to each tool face sequence, taking the normalized tool face angle sequence as input, taking the corresponding state code as expected output, and training a preset BP neural network to obtain a tool face state identification model; and in the sliding directional drilling operation process, obtaining a normalized tool face angle sequence at the current moment, inputting the normalized tool face angle sequence into a trained tool face state identification model to obtain a state code, and determining the state of the current equipment.
The invention adopts the BP neural network, can learn the state characteristics of the tool surface based on a large amount of historical data, avoids the limitation and difficulty of a tool surface physical model, is a convenient and effective tool surface state identification method, can identify the states of tool surface error data, stable state, change direction, change speed and the like, and can reduce the influence of human factors on the tool surface state judgment. The method is verified by the field operation process, has high accuracy and can meet the requirement of on-line automatic identification. The method has strong universality and popularization, and the established discrete state model of the sliding directional drilling tool face can be suitable for various mainstream MWD instruments at present.
Drawings
FIG. 1 is a schematic view of a horizontal well drilling system;
FIG. 2 is a schematic view of a toolface angle;
fig. 3 is a flowchart of an embodiment of a sliding directional drilling tool face state identification method based on a BP neural network according to the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
Examples
FIG. 1 is a flow chart of an embodiment of a machine learning based method for identifying a tool face state of a sliding directional drilling tool according to the present invention. As shown in fig. 1, the method for identifying the tool face state of the sliding directional drilling based on machine learning according to the present invention can be divided into three stages, specifically as follows:
the first stage is as follows: establishing tool surface discrete state model
S301: tool face data collection:
tool face data for a sliding directional drilling operation is collected, including the tool face angle and the time of acquisition.
S302: tool face data serialization:
and extracting a plurality of tool face angle sequences from the tool face data, wherein each tool face angle sequence comprises tool face angles at continuous L acquisition moments. The specific value of L can be set according to actual needs, generally, the value range of L is 5-10, and L is 5 in this embodiment.
S303: tool surface data state labeling:
and carrying out state labeling on each tool face angle sequence, wherein the state labeling comprises error data, a stable state, a change direction and a change speed, and the specific method comprises the following steps:
and judging error data of the tool face corner sequence, marking the tool face corner sequence as 0 if no error data exists, and marking the tool face corner sequence as a serial number of the error data in the tool face corner sequence if the error data exists. Generally, only at most one error data needs to be considered, and the judgment rule of the error data in the embodiment is as follows:
sequentially judging each tool face angle in the tool face angle sequence, and acquiring the tool face angle with the acquisition time t
Calculating the face angle of the tool when determining the error data
The last correct tool face angle
Difference of (2)
If it is not
Less than a predetermined threshold, e.g. 50 deg., the toolface angle
If the data is correct, otherwise, respectively calculating the tool face angle at the next acquisition time t +1
And
if a difference is present between
Then judge the tool face angle with the collection time t
And recording the serial number of the error data, otherwise, recording the correct data. And stopping when the tool face angle obtained by judgment is error data, otherwise, continuously judging the next tool face angle until the whole tool face angle sequence is judged.
And (5) performing stability judgment on the tool face angle sequence, and marking the tool face angle sequence as 0 if the tool face angle sequence is unstable, otherwise, marking the tool face angle sequence as 1. The stability determination rule in this embodiment is as follows: and for the tool face angle sequence, respectively calculating the interval between adjacent tool faces and the interval between the tool face at the end of the sequence and the rest tool faces, if a certain interval is greater than a preset threshold value, judging that the tool face angle sequence is unstable, and if not, judging that the tool face angle sequence is stable.
And judging the change direction of the tool face angle in the tool face angle sequence, and marking the change direction as 0 if the change direction is anticlockwise change, and marking the change direction as 1 if the change direction is clockwise change.
Dividing the change speed into M grades in advance, calculating the change speed of the tool face angle in the tool face angle sequence to obtain a corresponding grade, and marking the corresponding grade as the change speed grade of the tool face angle sequence.
And a second stage: training a tool face state identification network:
s304: tool face angle normalization:
the data in the range of [0,1] is normalized for each toolface angle, resulting in a sequence of normalized toolface angles. In this embodiment, Min-Max normalization is used.
S305: and (3) state marking coding:
and performing OneHot (one-hot) coding on the marked states according to the combination of the marked states to obtain the state code corresponding to each tool face sequence. Since there are 4 kinds of marks, among which there are L +1 kinds of values for the error data mark, 2 kinds of values for the stable state mark, 2 kinds of values for the change direction mark, and M kinds of values for the change speed mark, there are (L +1) × 2 × 2 × M kinds in total for the mark combination, and the dimension of the state code is (L +1) × 2 × M.
S306: training a tool face state identification network:
and constructing a tool face state identification network, taking the normalized tool face angle sequence as input and the corresponding state code as expected output, and training the BP neural network to obtain the tool face state identification network. The BP neural network is a common deep learning model, and the specific principle thereof is not described herein. The BP neural network in the embodiment comprises an input layer, a hidden layer and an output layer, wherein the number of neurons in the input layer is L; the number of the neurons of the output layer is the dimension of one-hot coding, namely (L +1) multiplied by 2 multiplied by M, and the activation function is Softmax; the hidden layer is composed of a plurality of fully connected layers.
And a third stage: online identifying the tool face state:
s307: recognizing the state of the tool surface:
in the process of the sliding directional drilling operation, tool face angles at the current time and the first L-1 times form a tool face angle sequence, normalization is carried out according to the method in the step S304, then the tool face angle sequence after normalization is input into the tool face state identification network trained in the step S306, and state codes are obtained, so that the state of the tool face is determined.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.
Claims (2)
1. A method for identifying the state of a sliding directional drilling tool face based on machine learning is characterized by comprising the following steps:
s1: collecting tool face data of the sliding directional drilling operation, wherein the tool face data comprises a tool face angle and a collection time;
s2: extracting a plurality of tool face angle sequences from the tool face data, wherein each tool face angle sequence comprises tool face angles at continuous L acquisition moments;
s3: and carrying out state labeling on each tool face angle sequence, wherein the state labeling comprises error data, a stable state, a change direction and a change speed, and the specific method comprises the following steps:
judging error data of the tool face corner sequence, if no error data exists, marking the error data as 0, and if the error data exists, marking the error data as a serial number of the error data in the tool face corner sequence; the judgment rule of the error data is as follows:
sequentially judging each tool face angle in the tool face angle sequence, and acquiring the tool face angle with the acquisition time t
Calculating the face angle of the tool when determining the error data
The last correct tool face angle
Difference of (2)
If it is not
If the value is less than the preset threshold value, the face angle of the tool
If the data is correct, otherwise, respectively calculating the tool face angle at the next acquisition time t +1
And
if a difference is present between
Then judge the tool face angle with the collection time t
Recording the serial number of the error data, otherwise, the error data is correct data; stopping when the tool face angle obtained by judgment is error data, otherwise, continuously judging the next tool face angle until the whole tool face angle sequence is judged;
judging the stability of the tool face angle sequence, if the tool face angle sequence is unstable, marking the tool face angle sequence as 0, otherwise, marking the tool face angle sequence as 1; the stability determination rule is as follows: for the tool face angle sequence, respectively calculating the interval between adjacent tool faces and the interval between the tool face at the end of the sequence and the rest tool faces, if a certain interval is greater than a preset threshold value, judging that the tool face angle sequence is unstable, otherwise, judging that the tool face angle sequence is stable;
judging the change direction of the tool face angle in the tool face angle sequence, if the change direction is anticlockwise change, marking the change direction as 0, and if the change direction is clockwise change, marking the change direction as 1;
dividing the change speed into M grades in advance, calculating the change speed of the tool face angle in the tool face angle sequence to obtain a corresponding grade, and marking the corresponding grade as the change speed grade of the tool face angle sequence;
s4: normalizing each tool face angle into data in a range of [0,1] to obtain a normalized tool face angle sequence;
s5: performing OneHot coding on the marked states according to the combination of the marked states to obtain state codes corresponding to each tool face sequence;
s6: constructing a BP neural network model, taking the normalized tool face angle sequence as input and the corresponding state code as expected output, and training the BP neural network model to obtain a tool face state identification network;
s7: in the process of the sliding directional drilling operation, tool face angles at the current moment and the first L-1 moments form a tool face angle sequence, normalization is carried out according to the method in the step S4, then the tool face angle sequence after normalization is input into the tool face identification network trained in the step S6, and state codes are obtained, so that the error data, the stable state, the change direction and the change speed state of the tool face are determined.
2. The method of claim 1, wherein the normalization in step S5 is normalized using Min-Max normalization.
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Effective date of registration: 20220929 Address after: 646000 No. 19, 5 section of wine Valley Road, Luzhou high tech Zone, Sichuan Patentee after: Sichuan Wisdom Star Technology Co.,Ltd. Address before: 611731, No. 2006, West Avenue, Chengdu hi tech Zone (West District, Sichuan) Patentee before: University of Electronic Science and Technology of China |