CN111677493B - Drilling data processing method - Google Patents
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- CN111677493B CN111677493B CN201910179700.4A CN201910179700A CN111677493B CN 111677493 B CN111677493 B CN 111677493B CN 201910179700 A CN201910179700 A CN 201910179700A CN 111677493 B CN111677493 B CN 111677493B
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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
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- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
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Abstract
A method of drilling data processing, comprising: step one, acquiring drilling data of a target well at the current moment and for a preset time period; step two, determining whether a risk exists in the target well at the current moment according to drilling data by using a preset risk identification model, wherein if the risk exists, the step three is executed; and thirdly, determining a risk prevention and control scheme of the target well according to the determined risk type and drilling data of the target well at the current moment by using a preset risk prevention and control model. According to the method, the data are mined and analyzed by utilizing a big data analysis technology, a risk identification model, a risk prevention and control model and a high-efficiency drilling model are established through research, real-time analysis of the drilling data of the target well is realized through integrated application of the models, the drilling risk can be timely and accurately identified and predicted, prevention and control are carried out on the risk, and meanwhile, safe and high-efficiency drilling is realized through real-time drilling optimization on the basis of risk prevention and control.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration and development, in particular to a drilling data processing method.
Background
With the continuous deep exploration and development, the difficulty of oil and gas exploration and development is higher. The geological conditions faced by the oil and gas exploration at the present stage are increasingly complex, the burial depth of a reservoir is increased, the complex risk conditions faced by the drilling engineering are more and more, the drilling problem is increasingly serious, and the cost required for treating the drilling risk and accidents is higher and higher. Particularly, in exploratory well exploration, drilling risks are frequent due to unknown geological conditions, and the drilling cost is high.
Achieving safe and efficient drilling is a primary goal of the drilling industry. The identification and control of various complex situations and risk accidents in the drilling operation process are all the time important in industry research, and because the risk identification and control need to comprehensively consider the influences of factors such as geological links, drilling tool combinations, engineering parameters and the like, the existing fuzzy evaluation method based on key parameter mathematical calculation, sensor data symptom discrimination or Bayesian network and the like has low accuracy and poor real-time performance for risk identification, and an optimal coping scheme cannot be provided for the identified risk.
Disclosure of Invention
To solve the above problems, the present invention provides a drilling data processing method, including:
step one, acquiring drilling data of a target well at the current moment and for a preset time period;
step two, determining whether the risk exists at the current moment of the target well according to the drilling data by using a preset risk identification model, wherein if the risk exists, the step three is executed;
and thirdly, determining a risk prevention and control scheme of the target well according to the determined risk type of the target well at the current moment and the drilling data by using a preset risk prevention and control model.
According to one embodiment of the invention, if it is determined in the second step that there is no risk at the current time of the target well, a fourth step is performed, in which,
and determining an optimal drilling scheme of the target well at the current moment according to the drilling data of the current moment and the previous preset time length by using a preset efficient drilling model.
In the fourth step, according to an embodiment of the present invention, the risk identification model is further used to determine whether the target well is at risk when drilling with an optimal drilling scheme, where,
if no risk exists, adopting the optimal drilling scheme to drill;
and if the risk exists, the optimal drilling scheme of the target well at the current moment is redetermined by using a preset efficient drilling model.
According to one embodiment of the invention, the method further comprises:
and fifthly, executing the risk prevention and control scheme, re-acquiring the drilling data of the target well at the current moment and for the preset time, and re-judging whether the target well is at risk or not after executing the risk prevention and control scheme.
According to one embodiment of the invention, in said step four,
and if the target well after the risk prevention and control scheme is executed still has risk, re-executing the step three to re-determine the risk prevention and control scheme of the target well.
According to one embodiment of the present invention, if there is no risk in the target well after the risk prevention scheme is performed, the fourth step is performed to determine an optimal drilling scheme for the target well using the drilling data of the target well after the risk prevention scheme is performed.
According to one embodiment of the invention, the drilling data includes any one or more of the following:
real-time logging data, logging while drilling data, and static data.
According to one embodiment of the invention, the method further comprises:
a preset risk identification model construction step, namely acquiring drilling history data, extracting drilling risk data from the drilling history data, analyzing big data based on the drilling risk data, analyzing data characteristics of various drilling risks in the occurrence time and the previous preset time period, and building the preset risk identification model.
According to one embodiment of the invention, the method further comprises:
and a preset risk prevention and control model construction step, wherein drilling risk processing case data is extracted from the drilling history data, big data analysis is carried out based on the drilling risk processing case data, various drilling risk processing schemes, processes and processing effects are analyzed, and the preset risk prevention and control model is built and obtained.
According to one embodiment of the invention, the method further comprises:
and a preset efficient drilling model construction step, wherein normal drilling construction data are extracted from the drilling history data, big data analysis is carried out based on the normal drilling construction data, drilling speed rules under different stratum and different engineering parameter combinations are analyzed, and the preset efficient drilling model is built.
Aiming at the problems that the conventional methods based on key parameter mathematical calculation, sensor data symptom discrimination or Bayesian network fuzzy evaluation and the like have low drilling risk identification accuracy and poor real-time performance, and an optimal treatment scheme cannot be given to the identified risks, the drilling data processing method provided by the invention provides a new technical thought.
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 description will briefly explain the drawings required in the embodiments or the description of the prior art:
FIG. 1 is a flow chart of an implementation of a method of drilling data processing in accordance with one embodiment of the present invention;
FIG. 2 is a schematic flow diagram of an implementation of constructing a risk identification model according to one embodiment of the invention;
FIG. 3 is a schematic diagram of an implementation flow of constructing a risk prevention and control model according to one embodiment of the invention;
FIG. 4 is a schematic diagram of an implementation flow of constructing an efficient drilling model in accordance with one embodiment of the present invention.
Detailed Description
The following will describe embodiments of the present invention in detail with reference to the drawings and examples, thereby solving the technical problems by applying technical means to the present invention, and realizing the technical effects can be fully understood and implemented accordingly. It should be noted that, as long as no conflict is formed, each embodiment of the present invention and each feature of each embodiment may be combined with each other, and the formed technical solutions are all within the protection scope of the present invention.
In the following description, meanwhile, for the 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 in the specific manner described herein.
Additionally, the steps illustrated in the flowcharts 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 flowcharts, in some cases the steps illustrated or described may be performed in an order other than that herein.
The patent application with the application number of 201710056408.4 discloses an intelligent efficient drilling automatic control system and an intelligent efficient drilling automatic control method. The automatic control system includes: the drilling machine comprises a drilling machine driving parameter detection module, a rotary drilling parameter detection module, a drilling machine control module, a drilling machine pump station, a drilling machine gyrator and a drilling tool. The system can automatically detect the torque T, the thrust F1 and the pulling force F2 in the drilling process, the drilling depth L, the drilling rod rotation speed omega and the drilling speed v, automatically transmit the detected key parameters to a drilling machine control module, optimize the drilling rod rotation speed and the drilling speed, and transmit the optimization result to a drilling machine pump station to drive a drilling machine rotator to drill.
The system can realize real-time detection of the stress condition of the drilling tool in the hole, and automatically adjusts the rotation speed omega and the drilling speed v of the drilling tool in time based on the stress change condition of the drilling tool in the hole, thereby ensuring that the drilling tool is in the optimal working condition state in the hole, reducing the in-hole accidents such as drilling sticking, breaking drilling, drilling gas combustion and the like, and being beneficial to realizing safe and efficient drilling. However, the system and method lack identification and prevention of risk and cannot be applied to drilling engineering.
Aiming at the problems in the prior art, the invention provides a novel drilling data processing method. Fig. 1 shows a schematic flow chart of the implementation of the method in this embodiment.
As shown in fig. 1, the method for processing drilling data provided in this embodiment preferably obtains the current time of the target well and the drilling data of the previous preset time period in step S101. Specifically, in the present embodiment, the drilling data acquired in step S101 by the method preferably includes: real-time logging data, logging while drilling data, and static data.
For example, the real-time logging data may include data such as weight on bit, rotational speed, and/or displacement, while drilling data may include data such as natural gamma data and/or sonic jet lag, while static data may include data such as formation horizon, lithology, drilling tool assembly, and/or drilling fluid type.
Of course, in other embodiments of the present invention, the drilling data acquired by the method in step S101 may include only one or more of the above listed items, other non-listed items, or a combination of one or more of the above listed items and other non-listed items, and the present invention is not limited thereto.
In this embodiment, the above-described preset time period is preferably configured to be 1 minute. Of course, in other embodiments of the present invention, the specific value of the preset duration may be configured to other reasonable values according to actual needs, and the present invention is not limited thereto. For example, in one embodiment of the present invention, the preset time period may be configured to be other reasonable values within [30s,5min ].
As shown in fig. 1, in this embodiment, after obtaining the drilling data of the target well, the method preferably uses a preset risk identification model in step S102 to determine whether there is a risk at the current moment of the target well according to the drilling data of the target well obtained in step S101.
In this embodiment, the preset risk identification model for determining whether the target well is at risk is preferably constructed based on a big data analysis technique. Fig. 2 is a schematic implementation flow chart of constructing the preset risk identification model in the embodiment.
As shown in fig. 2, in this embodiment, when constructing the risk identification model, the method first acquires drilling history data in step S201. By using the acquired drilling history data, the method can build a database for obtaining big data. Specifically, in this embodiment, the drilling history data acquired in step S201 of the method preferably includes various static data and real-time data such as seismic exploration data, rock mechanics data, drilling design of completed wells, well daily reports, well completion reports, logging data, and logging data of an oilfield block where the target well is located.
Of course, in other embodiments of the present invention, the drilling history data obtained in step S201 of the method may also include other reasonable data according to actual needs, and the present invention is not limited thereto.
After obtaining the drilling history data, the method extracts drilling risk data from the drilling history data in step S202, performs big data analysis based on the drilling risk data extracted in step S202 in step S203, and analyzes data characteristics of various drilling risks in the occurrence time and the previous preset time period, thereby establishing and obtaining the required preset risk identification model.
Specifically, in the present embodiment, the drilling risk data extracted from the drilling history data in step S202 by the method preferably includes geological data and engineering data including risk type, occurrence stratum level, lithology, drilling tool combination, weight on bit, displacement, drilling fluid density, and the like. Of course, in other embodiments of the present invention, the drilling risk data extracted by the method may also contain other reasonable data, and the present invention is not limited thereto.
In this embodiment, the preset time period adopted in the big data analysis in step S203 is equal to the preset time period referred to in step S101.
Through big data analysis, the preset risk identification model constructed by the method can identify whether the target well has risks or not by analyzing the real-time logging data, logging while drilling data and the data characteristics of static data of the target well.
As shown in fig. 1 again, in this embodiment, if the method determines in step S102 that there is a risk in the target well at the current moment by using the preset risk identification model, the method will execute step S103. In step S103, the method uses a preset risk prevention and control model to determine a risk prevention and control scheme of the target well according to the risk type of the target well at the current moment determined by using the preset risk identification model in step S102 and the drilling data acquired in step S101.
In this embodiment, the preset risk prevention and control model for determining the risk prevention and control scheme corresponding to the risk characteristics of the target well at the current moment is preferably also constructed based on big data analysis technology. Fig. 3 is a schematic implementation flow chart of the preset risk prevention and control model in the embodiment.
As shown in fig. 3, in this embodiment, when constructing the risk prevention and control model, the method first acquires drilling history data in step S301. The specific principle and process of implementing step S301 in this method are the same as those of step S201, and the details of step S301 will not be described here again. Of course, if the risk identification model and the risk prevention and control model are constructed uniformly, the method only needs to perform the well drilling historical data acquisition process once.
After obtaining the drilling history data, the method extracts drilling risk processing case data from the drilling history data in step S302, performs big data analysis based on the drilling risk processing case data extracted in step S302 in step S303, and analyzes various drilling risk processing schemes, processes and processing effects, thereby establishing and obtaining a preset risk prevention and control model.
Of course, in other embodiments of the present invention, the method may also use other reasonable ways to construct the risk prevention and control model according to actual needs, and the present invention is not limited thereto.
In this embodiment, the drilling risk processing case data extracted in step S302 preferably includes not only geological and engineering data such as risk type, occurrence stratum level, lithology, drilling tool combination, weight on bit, etc., but also related data such as processing method, adjustment parameter item, adjustment range, processing effect, etc.
It should be noted that, in other embodiments of the present invention, the drilling risk processing case data extracted in step S202 may include only one or some of the above listed items, or may include other reasonable items not listed, and the present invention is not limited thereto.
As shown in fig. 1 again, in this embodiment, after determining the risk prevention and control scheme corresponding to the current time of the target well by using the preset risk prevention and control model, the method preferably executes the risk prevention and control scheme in step S104, and re-acquires the current time of the target well and the drilling data of the preset duration before in step S105. It is noted that the current time in step S105 preferably refers to a time at which it is re-determined whether or not the target well is at risk after the risk prevention scheme is executed.
In step S106, the method uses the drilling data of the current time of the target well acquired in step S105 to re-determine whether there is a risk at the current time of the target well. In this embodiment, the implementation principle and implementation process of the step S106 are the same as those of the step S102, so that detailed descriptions of the specific contents of the step S106 are omitted here.
If the risk prevention and control scheme determined in step S103 is performed, the target well still has risk, and then the method returns to step S103 to redetermine the risk prevention and control scheme by using the preset risk prevention and control model. If it is determined in step S102 or step S106 that there is no risk in the target well, the method preferably proceeds to step S107.
As shown in fig. 1, in the embodiment, the method preferably uses a preset efficient drilling model in step S107 to determine an optimal drilling scheme of the target well at the current moment according to the drilling data of the current moment and the previous preset duration. It should be noted that, if it is determined in step S102 that the target well is not at risk, the current time in step S107 is preferably the current time in step S101, and if it is determined in step S102 that the target well is faulty, the current time in step S107 is preferably the current time in step S105 (i.e., the time when the drilling data of the target well is re-acquired to determine whether the target well is at risk after the risk prevention scheme is executed).
In this embodiment, the preset efficient drilling model for determining the optimal drilling scheme of the target well at the current moment is preferably also constructed based on the big data analysis technology. Fig. 4 is a schematic implementation flow chart of constructing the preset efficient drilling model in the embodiment.
As shown in fig. 4, in this embodiment, when constructing the efficient drilling model, the method first acquires drilling history data in step S401. The specific principle and process of implementing step S401 in this method are the same as those of step S201, and the details of step S401 will not be described here again. Of course, if the risk identification model and the efficient drilling model are constructed uniformly, the method only needs to perform the well drilling history data acquisition process once.
After obtaining the drilling history data, the method extracts drilling normal drilling construction data from the drilling history data in step S402, performs big data analysis based on the normal drilling construction data extracted in step S402 in step S403, and analyzes drilling speed rules under different stratum and different engineering parameter combinations, thereby establishing and obtaining a required preset efficient drilling model. Of course, in other embodiments of the present invention, the method may also use other reasonable manners to construct the preset efficient drilling model according to actual needs, and the present invention is not limited thereto.
In this embodiment, the normal drilling construction data extracted in step S402 of the method preferably includes: engineering data such as stratum level, lithology, drilling tool combination, weight on bit, displacement, well depth, drilling fluid density and the like of drilling. Of course, in other embodiments of the present invention, the normal drilling construction data extracted in step S402 may include only one or some of the above listed items, and may include other reasonable items not listed, which is not limited thereto.
Of course, in other embodiments of the present invention, the method may also use other reasonable ways to construct the preset efficient drilling model according to actual needs, and the present invention is not limited thereto.
As shown in fig. 1, after obtaining the optimal drilling scheme of the target well at the current moment, the method may reuse the preset risk identification model in step S108 to determine whether the target well has a risk according to the obtained optimal drilling scheme, that is, determine whether the target well has a risk when drilling with the optimal drilling scheme.
If there is no risk in drilling the target well using the optimal drilling scheme obtained in step S107, the method also uses the optimal drilling scheme to perform the drilling operation in step S109. If the target well is at risk when drilled using the optimal drilling scheme obtained in step S107, the method returns to step S107 to re-determine the optimal drilling scheme (e.g., secondary optimal drilling scheme) for the target well at the current time.
In order to show the reliability and effectiveness of the well drilling data processing method provided by the invention, the method is experimentally applied to the northwest XX well drilling operation of the northwest block key well in northwest oilfield.
In the first stage, firstly, various static and real-time data such as seismic exploration data, rock mechanical data, well drilling design of completed well drilling, well drilling daily report, well completion report, well logging and the like of northwest blocks of northwest oil fields are collected, and a database of the northwest oil fields is established.
Then, a big data analysis technology is applied to research data characteristics when risks occur, drilling risk rules are developed, and a risk identification model is established; researching risk treatment cases and establishing a risk prevention and control model; and (3) researching normal drilling data, exploring the association relation among parameters affecting the drilling speed, and establishing a drilling optimization model.
In the well drilling operation of the northbound XX well, static and real-time data such as geology, logging and the like are acquired in real time and transmitted into a database, corresponding data are input into a risk identification model, a risk prevention and control model and a drilling optimization model, automatic well drilling risk identification, prevention and control and high-efficiency well drilling optimization are carried out, and a prevention and control scheme and an optimization scheme are pushed to the northbound XX well site in real time to guide well drilling construction.
After the well drilling operation of the northbound XX well is completed, the well drilling risk and the well drilling operation time are counted and compared with the northbound block average value, and the fact that the well drilling risk does not occur in the northbound XX well (the northbound block average risk of 3.2 times of well drilling per well) and the well drilling time is 162 days (the northbound block average time of 208 days) is found, the well drilling efficiency is obviously improved, and the application effect of the method is proved to be good.
According to the method, aiming at the problems that the drilling risk identification accuracy is low, the real-time performance is poor, and an optimal treatment scheme cannot be given to the identified risk by the existing methods based on key parameter mathematical calculation, sensor data symptom discrimination or Bayesian network fuzzy evaluation and the like, a new technical thought is provided.
It is to be understood that the disclosed embodiments are not limited to the specific structures or process steps disclosed herein, but are intended to extend to equivalents of these features as would be understood by one of ordinary skill in the relevant arts. 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 intended to illustrate the principles of the invention in one or more applications, it will be apparent to those skilled in the art that various modifications in form, use and details of implementation may be made without departing from the principles and concepts of the invention. Accordingly, the invention is defined by the appended claims.
Claims (8)
1. A method of drilling data processing, the method comprising:
step one, acquiring drilling data of a target well at the current moment and for a preset time period;
step two, determining whether the risk exists at the current moment of the target well according to the drilling data by using a preset risk identification model, wherein if the risk exists, the step three is executed; determining a risk prevention and control scheme of the target well according to the determined risk type of the target well at the current moment and the drilling data by using a preset risk prevention and control model;
and if the step two judges that the risk does not exist at the current moment of the target well, executing a step four, in the step four,
determining an optimal drilling scheme of the target well at the current moment according to the drilling data of the current moment and the previous preset time length by using a preset efficient drilling model;
the method further comprises the steps of: a preset risk identification model construction step, namely acquiring drilling history data, extracting drilling risk data from the drilling history data, analyzing big data based on the drilling risk data, analyzing data characteristics of various drilling risks in the occurrence time and the previous preset time period, and building the preset risk identification model.
2. The method of claim 1, wherein in step four, the risk of the target well being at risk while drilling with an optimal drilling scheme is also determined using the pre-set risk identification model, wherein,
if no risk exists, adopting the optimal drilling scheme to drill;
and if the risk exists, the optimal drilling scheme of the target well at the current moment is redetermined by using a preset efficient drilling model.
3. The method of claim 1, wherein the method further comprises:
and fifthly, executing the risk prevention and control scheme, re-acquiring the drilling data of the target well at the current moment and for the preset time, and re-judging whether the target well is at risk or not after executing the risk prevention and control scheme.
4. The method of claim 3, wherein, in the fourth step,
and if the target well after the risk prevention and control scheme is executed still has risk, re-executing the step three to re-determine the risk prevention and control scheme of the target well.
5. The method of claim 4, wherein if there is no risk for the target well after performing the risk prevention and control scheme, performing step four to determine an optimal drilling scheme for the target well using the drilling data for the target well after performing the risk prevention and control scheme.
6. The method of claim 1, wherein the drilling data comprises any one or more of the following:
real-time logging data, logging while drilling data, and static data.
7. The method of claim 6, wherein the method further comprises:
and a preset risk prevention and control model construction step, wherein drilling risk processing case data is extracted from the drilling history data, big data analysis is carried out based on the drilling risk processing case data, various drilling risk processing schemes, processes and processing effects are analyzed, and the preset risk prevention and control model is built and obtained.
8. The method of claim 6 or 7, wherein the method further comprises:
and a preset efficient drilling model construction step, wherein normal drilling construction data are extracted from the drilling history data, big data analysis is carried out based on the normal drilling construction data, drilling speed rules under different stratum and different engineering parameter combinations are analyzed, and the preset efficient drilling model is built.
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| US20110161133A1 (en) * | 2007-09-29 | 2011-06-30 | Schlumberger Technology Corporation | Planning and Performing Drilling Operations |
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| CN104806226B (en) * | 2015-04-30 | 2018-08-17 | 北京四利通控制技术股份有限公司 | intelligent drilling expert system |
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