CN111598366A - Real-time drilling auxiliary decision-making method and system - Google Patents
Real-time drilling auxiliary decision-making method and system Download PDFInfo
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Abstract
A real-time drilling assistant decision-making method and a system thereof are provided, wherein the method comprises the following steps: acquiring actual measurement data of a drilling well to be analyzed at a drilling end, and transmitting the actual measurement data to a cloud server; calculating key parameters affecting the drilling efficiency and risk in real time in the well according to the actually measured data by using a cloud server; determining a drilling risk state under the condition of the current site construction parameters by using a cloud server according to distribution data and/or change condition data of the key parameters in the well; and determining different parameter adjusting scheme sets by using different risk identification and analysis models according to different drilling risk states under the current site construction parameter conditions by using a cloud server, and sending the parameter adjusting scheme sets to the drilling end. The method can generate an alternative speed-up/wind control parameter regulation scheme in real time and transmit the scheme to a drilling site, so that drilling personnel are assisted to make real-time drilling decision by combining actual conditions, and safe and efficient drilling is realized.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration and development, in particular to a real-time drilling auxiliary decision-making method and system.
Background
Along with the continuous deepening of exploration and development, the oil and gas exploration and development difficulty is higher and higher, the geological condition is more and more complicated, the reservoir burial depth is increased, and the complex risk condition faced by the drilling engineering is more and more, so that the cost required for processing the drilling risk and the accident is higher and higher, and the realization of safe and efficient drilling is the primary target of the drilling industry.
At present, key parameters such as formation pressure, shaft pressure, friction torque and the like directly related to drilling efficiency and drilling risk cannot be directly measured by using a sensor in the drilling process, and only a traditional calculation method can be used for calculating based on other sensor data in a drilling site. Because a large amount of data interaction and iterative computation are involved in the computation process, the performance of the industrial personal computer on the drilling site cannot meet the computation requirement, and the variation trend of the key parameters cannot be obtained through real-time computation and provided for drilling personnel to make decisions. Therefore, parameter regulation and control after drilling site construction and risk occurrence mainly depend on the prior drilling experience of a few personnel such as site drillers and drilling captain, and the dependence on personnel is too strong to avoid errors in the decision-making process.
The techniques and methods currently provided in patents and literature fall into two main categories. One is to use grey correlation, decision trees and other methods to identify risk signs based on drilling site sensor data changes, and the other is to identify single risk by calculating some key parameter in wellbore pressure or friction torque. Generally, current methods and techniques focus on identifying and predicting drilling risks, but cannot provide field personnel with parameter adjustment alternatives required for efficient drilling and risk prevention and control, and cannot directly assist drilling personnel in making decisions.
Disclosure of Invention
In order to solve the above problems, the present invention provides a real-time drilling aid decision method, comprising:
the method comprises the steps of firstly, acquiring measured data of a well to be analyzed at a well drilling end, and transmitting the measured data to a cloud server;
secondly, calculating key parameters affecting the drilling efficiency and risk in real time in the well by using a cloud server according to the measured data;
thirdly, determining a drilling risk state under the condition of the current site construction parameters by using a cloud server according to the distribution data and/or the change condition data of the key parameters in the well;
and step four, determining different parameter adjusting scheme sets by using different risk identification analysis models according to different drilling risk states under the current site construction parameter conditions by using the cloud server, and sending the parameter adjusting scheme sets to the drilling end.
According to one embodiment of the invention, the key parameters affecting drilling efficiency and risk include any one or several of the following:
formation pressure, wellbore cleanliness, friction, torque, and rate of penetration.
According to one embodiment of the invention, in said step four,
if the drilling risk state under the condition of the current site construction parameters is a normal state, determining a speed-up parameter-adjusting scheme set according to the key parameters by adopting a speed-up parameter-adjusting analysis model;
and if the drilling risk state under the condition of the current site construction parameters is a risk state, determining a wind control parameter adjustment scheme set according to the key parameters by adopting a wind control parameter adjustment analysis model.
According to one embodiment of the invention, the step of determining the set of upshifting parameters comprises:
step a, generating a first preset number of initial speed-up and parameter-regulation schemes by using a parameter-regulation scheme generation model to obtain an initial speed-up and parameter-regulation scheme set;
step b, based on the adjusted parameters in each initial speed-up parameter scheme in the initial speed-up parameter scheme set, re-executing the step three, and determining the drilling risk state under each initial speed-up parameter scheme;
and c, determining the speed-increasing parameter scheme set according to the drilling risk state under each initial speed-increasing parameter scheme.
According to an embodiment of the present invention, in the step c, an initial speed-up adjustment scheme in which the drilling risk state is a normal state is extracted, and the speed-up adjustment scheme set is formed using the extracted initial speed-up adjustment scheme.
According to one embodiment of the invention, the step of determining the set of wind control parameters comprises:
d, generating a second preset number of wind control parameter adjusting schemes by using the parameter adjusting scheme generation model to obtain an initial wind control parameter adjusting scheme set;
step e, based on the adjusted parameters in each initial wind control parameter scheme in the initial wind control parameter scheme set, re-executing the step three, and determining the drilling risk state under each initial wind control parameter scheme;
and f, determining the wind control parameter scheme set according to the drilling risk state under each initial wind control parameter scheme.
The invention also provides a real-time drilling aid decision-making system, which comprises:
the actual measurement data acquisition device is arranged at the drilling end and is used for acquiring actual measurement data of the drilling well to be analyzed;
and the cloud server is in communication connection with the measured data acquisition device and is used for receiving the measured data uploaded by the measured data acquisition device, calculating distribution data and/or change condition data of key parameters influencing drilling efficiency and risk in the well in real time according to the measured data, determining a drilling risk state under the condition of current site construction parameters according to the distribution data and/or change condition data of the key parameters in the well, and determining different parameter adjusting scheme sets by adopting different risk identification and analysis models according to different drilling risk states under the condition of the current site construction parameters.
According to one embodiment of the invention, the system further comprises:
and the human-computer interaction device is arranged at the drilling end, is in communication connection with the cloud server, and is used for receiving the parameter adjusting scheme set sent by the cloud server and outputting the parameter adjusting scheme set to a user.
According to one embodiment of the invention, if the drilling risk state under the condition of the current site construction parameters is a normal state, the cloud server is configured to determine a speed-up parameter scheme set according to the key parameters by adopting a speed-up parameter analysis model;
and if the drilling risk state under the condition of the current site construction parameters is a risk state, the cloud server is configured to determine a wind control parameter scheme set according to the key parameters by adopting a wind control parameter analysis model.
According to an embodiment of the present invention, the cloud server is configured to determine the set of speed-up parameters according to the following steps:
generating a first preset number of initial speed-up and parameter-regulation schemes by using a parameter-regulation scheme generation model to obtain an initial speed-up and parameter-regulation scheme set;
based on the adjusted parameters in each initial speed-up parameter scheme in the initial speed-up parameter scheme set, re-executing the third step, and determining the drilling risk state under each initial speed-up parameter scheme;
and determining the speed-up regulation parameter scheme set according to the drilling risk state under each initial speed-up regulation parameter scheme.
The existing methods and technologies focus on identification and prediction of drilling risks, but the existing methods and technologies cannot provide parameter adjustment alternatives required by efficient drilling and risk prevention and control for field personnel, and cannot directly help drilling personnel to make decisions. The real-time drilling auxiliary decision-making method provided by the invention provides a new technical thought, based on the cloud computing technology, by applying the high efficiency of distributed computing and parallel computing, the key parameters directly related to the drilling efficiency and risk are computed and analyzed in real time, and the alternative speed-up/wind control parameter regulation scheme is generated in real time and fed back and transmitted to a drilling site, so that drilling personnel are assisted to make real-time drilling decision by combining with the actual situation, and safe and efficient drilling is realized.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required in the description of the embodiments or the prior art:
FIG. 1 is a schematic block diagram of a real-time drilling aid decision-making system according to one embodiment of the present invention;
FIG. 2 is a schematic flow chart of an implementation of a real-time drilling assistance decision method according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a distribution of a real-time drilling assistance decision making system according to one embodiment of the present invention;
fig. 4 is a flow chart illustrating an implementation of determining a set of pacing parameters according to an embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without some of these specific details or with other methods described herein.
Additionally, the steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions and, although a logical order is illustrated in the flow charts, in some cases, the steps illustrated or described may be performed in an order different than here.
Patent document CN201610197209.0 discloses a drilling risk prediction method. The method first obtains measured data of a borehole to be analyzed in a raw data obtaining step, wherein the measured data includes raw data of a plurality of influencing parameters. Subsequently, the method processes the measured data in the feature vector determination step to obtain the feature vector of the measured data. In the step of determining the correlation coefficient, the method respectively calculates the correlation coefficient between each element of the feature vector of the measured data and each fault type according to the feature vector of the measured data and a preset drilling risk judgment matrix. Finally, in the risk prediction step, the method can calculate the correlation degree between the measured data and each fault type according to the correlation coefficient, and judge whether the well to be analyzed has risks according to the correlation degree.
The method analyzes and predicts whether the drilling has risks based on the relevance, but cannot provide a specific parameter adjusting scheme required by risk control for field engineering personnel, and cannot provide an efficient drilling scheme in a risk-free state.
Patent document CN201410005736.8 discloses a real-time optimization method for drilling parameters and efficiency. In the implementation process of the method, firstly, drilling coring is carried out, and the drilling mode is judged according to the density and the property of the drilling fluid and the formation pore pressure by utilizing the established rock strength model. If the gas drilling is carried out, the uniaxial compressive strength can be directly used, otherwise, the rock strength under the condition of the confining pressure at the bottom of the well is calculated, the mechanical specific energy of the drill bit is calculated by using logging information, and the calculated mechanical specific energy of the drill bit is compared with the rock strength under the confining pressure at the bottom of the well.
The method can only optimize two engineering parameters of the bit pressure and the rotating speed, and the drilling accident is judged based on the critical failure point of the parameters, so that the adaptive risk range is limited, and an optimized parameter adjusting scheme can not be provided according to the risk condition.
Aiming at the problems in the prior art, the invention provides a novel real-time drilling auxiliary decision-making method and a system, the method and the system perform efficient real-time synchronous calculation on key parameters directly influencing drilling efficiency and risk on a rear cloud service platform by applying a cloud computing technology, and then form a plurality of parameter adjusting alternative schemes through parallel calculation based on a risk identification rule so as to help field personnel to perform drilling construction decision-making.
Fig. 1 shows a schematic structural diagram of a real-time drilling assistant decision-making system provided by the present embodiment, and fig. 2 shows a schematic implementation flow diagram of a real-time drilling assistant decision-making method provided by the present embodiment. The structure, operation principle and operation process of the system are further described in conjunction with fig. 1 and 2.
As shown in fig. 1, the real-time drilling assistant decision-making system provided by the present embodiment preferably includes: the device comprises an actual measurement data acquisition device 101, a cloud server 102 and a human-computer interaction device 103. The measured data obtaining device 101 is disposed at a drilling end, and is capable of obtaining measured data of a drilling well to be analyzed in step S201, and transmitting the obtained measured data to the cloud server 102 in communication connection therewith in step S202.
Specifically, as shown in fig. 3, in this embodiment, the measured data obtaining device 101 may be integrated in a field industrial personal computer, and since the field industrial personal computer is connected to the field sensor, the field industrial personal computer may also receive a related signal transmitted by the field sensor during the construction process. The field industrial personal computer performs data acquisition on the signals transmitted by the sensor according to actual needs, and the measured data can be obtained.
For example, the sensor data acquired by the measured data acquisition device 101 may include: torque data, total pool volume data, flow data, hook load data, open hole dead time of the drilling tool, weight on bit, mechanical specific energy value, downhole cyclic equivalent density, and the like. Of course, in other embodiments of the present invention, the sensor data acquired by the measured data acquiring device 101 may include only one or some of the items listed above, may include other reasonable items not listed, or may be a combination of one or some of the items listed above and other reasonable items not listed, according to actual needs, and the present invention is not limited thereto.
In addition, in this embodiment, according to actual needs, the measured data acquired by the measured data acquiring device 101 may include static data such as drilling tool assembly data, drilling fluid rheological data, and the like, in addition to the sensor data.
The cloud server 102 is in communication connection with the measured data acquiring device 101 to receive the measured data uploaded by the measured data acquiring device 101. Specifically, in this embodiment, the measured data obtaining device 101 and the cloud server 102 preferably perform data communication through a satellite wireless network.
Of course, in other embodiments of the present invention, the data communication mode between the measured data obtaining device 101 and the cloud server 102 may also adopt other reasonable modes. For example, in an embodiment of the present invention, the measured data obtaining apparatus 101 further uses a mobile communication network (e.g. a 3G network or a 4G network) or the internet to perform data communication with the cloud server 102.
In this embodiment, after receiving the measured data transmitted by the measured data obtaining device 101, the cloud server 102 may calculate, in step S203, distribution data and/or change data of key parameters in the well, which affect drilling efficiency and risk, in real time according to the measured data, and determine, in step S204, a drilling risk state under the condition of the current site construction parameters according to the calculated distribution data and/or change data of the key parameters in the well. After the drilling risk state under the current site construction parameter condition is obtained, the cloud server 102 determines different parameter adjusting scheme sets by using different risk identification and analysis models according to the different drilling risk states under the current site construction parameter condition in step S205.
Specifically, in the present embodiment, the drilling risk state under the current site construction parameter condition preferably includes a normal state and a risk state. The normal state indicates that drilling construction can not occur according to the current site construction parameters, and the risk state indicates that drilling construction can occur according to the current site construction parameters.
In this embodiment, the key parameters affecting the drilling efficiency and risk calculated by the cloud server 102 preferably include: formation pressure, wellbore cleanliness, friction, torque, and rate of penetration. Of course, in other embodiments of the present invention, the key parameters that affect the drilling efficiency and risk calculated by the cloud server 102 may include only one or some of the above listed items, or may include other reasonable items that are not listed, and the present invention is not limited thereto.
The cloud server 102 preferably utilizes a self-deployed real-time calculation program to calculate, in real time, distribution data and/or change condition data of key parameters affecting drilling efficiency and risk in the well according to the measured data transmitted by the measured data acquisition device 101, and utilizes a self-deployed risk identification program to determine a drilling risk state under the current site construction parameter condition according to the calculated distribution data and/or change condition data of the key parameters in the well.
It should be noted that the key parameter calculation method and the risk identification rule used by the cloud server 102 to determine the drilling risk state may be a mature technology used in the prior art, and therefore detailed descriptions of the specific principle and process of determining the drilling risk state by the cloud server 102 are omitted here.
The real-time drilling assistant decision-making system provided by the embodiment utilizes the high-efficiency calculation efficiency of the cloud server to realize a large amount of data interaction and iterative computation amount related in the key parameter calculation process, and can effectively solve the problem that the real-time requirement cannot be met due to the fact that the calculation efficiency of a single computer is too low in the prior art.
In this embodiment, if the drilling risk state under the current site construction parameter condition is a normal state, the cloud server 102 is configured to determine the speed-up parameter scheme set according to the calculated key parameter by using the speed-up parameter analysis model. And if the drilling risk state under the current site construction parameter condition is a risk state, the cloud server 102 is configured to determine a wind control parameter scheme set according to the calculated key parameters by using a wind control parameter analysis model.
Fig. 4 is a schematic flow chart illustrating an implementation process of determining a speed-up parameter set in the present embodiment.
As shown in fig. 4, in this embodiment, the cloud server 102 preferably generates a first preset number of initial speed-up parameter solutions by using a parameter solution generation model in step S401, so as to obtain an initial speed-up parameter solution set. Subsequently, the cloud server 102 re-executes step S203 and step S204 in step S402 based on the adjusted parameters in each initial acceleration parameter set in the initial acceleration parameter set generated in step S401, so as to re-determine the drilling risk status under each initial acceleration parameter set. Finally, the cloud server 102 determines the set of acceleration parameters according to the drilling risk status of each initial acceleration parameter determined in step S402 in step S403.
Specifically, in step S403, the cloud server 102 preferably extracts an initial speed-up parameter of the drilling risk state as a normal state, and forms the speed-up adjustment scheme set by using the extracted initial speed-up parameter.
For example, if the drilling can still be performed normally according to the current construction parameters (the drilling risk state under the current site construction parameter condition is a normal state), the cloud server 102 may invoke the parallel computing service provided by the cloud computing server itself, and generate N parameter adjusting schemes (i.e., an initial speed-up parameter adjusting scheme set) by using the parameter adjusting scheme generating program. After obtaining the parallel 1-N parameter adjustment schemes, the parallel computing service provided in the cloud server 102 will still call the real-time computing program, so as to adjust the corresponding parameter items based on each initial speed-up parameter scheme. Based on the adjusted parameter items, the cloud server 102 may perform calculation analysis on key parameters such as wellbore pressure, drilling rate, and the like again.
The cloud server 102 determines the drilling risk state corresponding to each obtained initial speed-up parameter scheme, and extracts the initial speed-up parameter scheme with the drilling risk state being a normal state, so that a required speed-up parameter scheme set can be obtained. At this time, the number of the speed-up parameter sets included in the speed-up parameter set may refer to specific values of 1 to N. In this embodiment, the speed-up and speed-regulation scheme preferably includes: and adjusting the distribution and change conditions of key parameters such as drilling parameter items, adjustment values, adjusted wellbore pressure and the like.
When determining the wind control parameter set, the cloud server 102 preferably generates a second preset number of wind control speed-up parameter sets by using the parameter generation model, so as to obtain an initial wind control parameter set. Subsequently, the cloud server 102 may re-determine the drilling risk state under each initial wind control parameter scheme based on the adjusted parameter in each initial wind control parameter scheme in the obtained set of initial wind control parameter schemes. Then, the cloud server 102 determines the set of speed-up parameter solutions according to the drilling risk status under each initial wind control parameter solution.
In this embodiment, the implementation principle and the implementation process of the cloud server 102 for determining the wind control and speed regulation parameter set are similar to the content of the acceleration speed regulation parameter set determined by the cloud server, and therefore details of the content are not repeated here.
The human-computer interaction device 103 is also arranged at the drilling end and is in communication connection with the cloud server 102. The cloud server 102 transmits the parameter adjusting scheme set generated by itself to the human-computer interaction device 103 in step S206, so that the human-computer interaction device 103 outputs the received parameter adjusting scheme set to the user in step S207. Therefore, the drilling site personnel can make decisions on drilling construction according to the actual situation.
It should be noted that in other embodiments of the present invention, the drilling assistance decision-making system may not be configured with a human-computer interaction device according to actual needs, and the present invention is not limited thereto.
In order to verify the availability and reliability of the real-time drilling assistant decision-making method and the system provided by the embodiment, the method is applied to the drilling operation of a certain well in the Topu table block of the northwest oil field, meanwhile, the drilling team of the well performs the drilling operation in the block for the first time, and the experience of drilling personnel in mastering various conditions of the block is less.
In the implementation process, a cloud computing service platform (namely a cloud server) is built at a rear information center, the existing server is applied to build and form distributed computing and parallel computing capacity, and a real-time computing program, a risk identification program and a parameter adjusting scheme generating program of key parameters such as shaft pressure are deployed on the basis.
In the well drilling operation, partial static data such as field sensor data, drilling fluid parameters and the like are collected in real time and transmitted to a database by a satellite network installed and deployed on the field. And then sending the obtained measured data to a cloud computing service platform through a data service and data interface.
After the measured data enter the cloud computing service platform, the cloud computing server platform calls a real-time computing program to calculate the distribution and change conditions of key parameters such as pressure, friction resistance and torque of a shaft in the well in real time based on the high-efficiency computing capability of the cloud computing service platform. And after the calculation result is judged by the risk identification program, the cloud computing server platform calls a parameter adjusting scheme generating program according to the specific drilling state to form N alternative parameter adjusting schemes. The cloud computing service platform can simultaneously carry out real-time computation and analysis on the N alternative parameter adjusting schemes based on the parallel computing capability of the cloud computing service platform, finally form N speed-up or wind control parameter adjusting schemes, and transmit the schemes to a well site industrial personal computer, so that intuitive parameter adjusting items, parameter adjusting values and key parameter change trends are provided for field personnel, and the field personnel are assisted to carry out drilling decision by combining with actual conditions.
After the well drilling operation is completed, the fact that a drilling team lacks drilling experience of a Topu platform block is found through statistics, but by applying the method, the well has no drilling risk (the drilling risk statistical result of the peripheral adjacent well is 1.7 times per well), the drilling time is 127 days (the drilling time statistical result of the peripheral adjacent well is 146 days per well), the drilling efficiency is remarkably improved, the application effect of the technical method is proved to be good, and the effect of real-time decision assistance is achieved.
The existing methods and technologies focus on identification and prediction of drilling risks, but the existing methods and technologies cannot provide parameter adjustment alternatives required by efficient drilling and risk prevention and control for field personnel, and cannot directly help drilling personnel to make decisions. The real-time drilling auxiliary decision-making method provided by the invention provides a new technical thought, based on the cloud computing technology, by applying the high efficiency of distributed computing and parallel computing, the key parameters directly related to the drilling efficiency and risk are computed and analyzed in real time, and the alternative speed-up/wind control parameter regulation scheme is generated in real time and fed back and transmitted to a drilling site, so that drilling personnel are assisted to make real-time drilling decision by combining with the actual situation, and safe and efficient drilling is realized.
It is to be understood that the disclosed embodiments of the invention are not limited to the particular structures or process steps disclosed herein, but extend to equivalents thereof as would be understood by those skilled in the relevant art. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.
Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrase "one embodiment" or "an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment.
While the above examples are illustrative of the principles of the present invention in one or more applications, it will be apparent to those of ordinary skill in the art that various changes in form, usage and details of implementation can be made without departing from the principles and concepts of the invention. Accordingly, the invention is defined by the appended claims.
Claims (10)
1. A real-time drilling aid decision-making method, the method comprising:
the method comprises the steps of firstly, acquiring measured data of a well to be analyzed at a well drilling end, and transmitting the measured data to a cloud server;
secondly, calculating key parameters affecting the drilling efficiency and risk in real time in the well by using a cloud server according to the measured data;
thirdly, determining a drilling risk state under the condition of the current site construction parameters by using a cloud server according to the distribution data and/or the change condition data of the key parameters in the well;
and step four, determining different parameter adjusting scheme sets by using different risk identification analysis models according to different drilling risk states under the current site construction parameter conditions by using the cloud server, and sending the parameter adjusting scheme sets to the drilling end.
2. The method of claim 1, wherein the key parameters affecting drilling efficiency and risk comprise any one or more of:
formation pressure, wellbore cleanliness, friction, torque, and rate of penetration.
3. The method according to claim 1 or 2, wherein in step four,
if the drilling risk state under the condition of the current site construction parameters is a normal state, determining a speed-up parameter-adjusting scheme set according to the key parameters by adopting a speed-up parameter-adjusting analysis model;
and if the drilling risk state under the condition of the current site construction parameters is a risk state, determining a wind control parameter adjustment scheme set according to the key parameters by adopting a wind control parameter adjustment analysis model.
4. The method of claim 3, wherein the step of determining a set of upshifting parameters comprises:
step a, generating a first preset number of initial speed-up and parameter-regulation schemes by using a parameter-regulation scheme generation model to obtain an initial speed-up and parameter-regulation scheme set;
step b, based on the adjusted parameters in each initial speed-up parameter scheme in the initial speed-up parameter scheme set, re-executing the step three, and determining the drilling risk state under each initial speed-up parameter scheme;
and c, determining the speed-increasing parameter scheme set according to the drilling risk state under each initial speed-increasing parameter scheme.
5. The method of claim 4, wherein in the step c, an initial speed-up recipe in which the drilling risk state is a normal state is extracted, and the set of speed-up recipes is formed using the extracted initial speed-up recipe.
6. The method according to any one of claims 3 to 5, wherein the step of determining a set of wind control parameters comprises:
d, generating a second preset number of wind control parameter adjusting schemes by using the parameter adjusting scheme generation model to obtain an initial wind control parameter adjusting scheme set;
step e, based on the adjusted parameters in each initial wind control parameter scheme in the initial wind control parameter scheme set, re-executing the step three, and determining the drilling risk state under each initial wind control parameter scheme;
and f, determining the wind control parameter scheme set according to the drilling risk state under each initial wind control parameter scheme.
7. A real-time drilling aid decision making system, the system comprising:
the actual measurement data acquisition device is arranged at the drilling end and is used for acquiring actual measurement data of the drilling well to be analyzed;
and the cloud server is in communication connection with the measured data acquisition device and is used for receiving the measured data uploaded by the measured data acquisition device, calculating distribution data and/or change condition data of key parameters influencing drilling efficiency and risk in the well in real time according to the measured data, determining a drilling risk state under the condition of current site construction parameters according to the distribution data and/or change condition data of the key parameters in the well, and determining different parameter adjusting scheme sets by adopting different risk identification and analysis models according to different drilling risk states under the condition of the current site construction parameters.
8. The system of claim 7, wherein the system further comprises:
and the human-computer interaction device is arranged at the drilling end, is in communication connection with the cloud server, and is used for receiving the parameter adjusting scheme set sent by the cloud server and outputting the parameter adjusting scheme set to a user.
9. The system of claim 7 or 8,
if the drilling risk state under the condition of the current site construction parameters is a normal state, the cloud server is configured to determine a speed-up parameter scheme set according to the key parameters by adopting a speed-up parameter analysis model;
and if the drilling risk state under the condition of the current site construction parameters is a risk state, the cloud server is configured to determine a wind control parameter scheme set according to the key parameters by adopting a wind control parameter analysis model.
10. The system of claim 9, wherein the cloud server is configured to determine the set of accelerated speed regulation parameters according to the following steps:
generating a first preset number of initial speed-up and parameter-regulation schemes by using a parameter-regulation scheme generation model to obtain an initial speed-up and parameter-regulation scheme set;
based on the adjusted parameters in each initial speed-up parameter scheme in the initial speed-up parameter scheme set, re-executing the third step, and determining the drilling risk state under each initial speed-up parameter scheme;
and determining the speed-up regulation parameter scheme set according to the drilling risk state under each initial speed-up regulation parameter scheme.
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| CN201910126789.8A Pending CN111598366A (en) | 2019-02-20 | 2019-02-20 | Real-time drilling auxiliary decision-making method and system |
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