CN113279747A - System and method for allocating drilling mud formula and performance parameters - Google Patents
System and method for allocating drilling mud formula and performance parameters Download PDFInfo
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- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B21/00—Methods or apparatus for flushing boreholes, e.g. by use of exhaust air from motor
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B21/00—Methods or apparatus for flushing boreholes, e.g. by use of exhaust air from motor
- E21B21/06—Arrangements for treating drilling fluids outside the borehole
- E21B21/068—Arrangements for treating drilling fluids outside the borehole using chemical treatment
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- G05D11/135—Controlling ratio of two or more flows of fluid or fluent material characterised by the use of electric means by sensing at least one property of the mixture
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Abstract
The invention provides a system and a method for allocating drilling mud formula and performance parameters, which comprises a drilling stratum information module, an on-site drilling detection device, a hydrographic well drilling field expert system, a machine learning prediction model and a control system; forming an automatic drilling mud formulation system; the on-site drilling detection device comprises a drilling parameter detection device, a well body structure detection device and a mud performance parameter detection device; the drilling stratum information module, the drilling parameter detection device and the well body structure detection device are used for transmitting data to the expert system in the hydrographic well drilling field to judge the change condition of the stratum, and the drilling mud formula of the corresponding stratum is provided to be used as the input parameter of the control system; the machine learning prediction model comprises a mud performance parameter prediction model and a mud formula additive dosage prediction model. The invention can provide the corresponding slurry formula of the stratum according to the actual condition of the in-situ stratum, thereby greatly increasing the working benefit of in-situ drilling.
Description
Technical Field
The invention relates to the technical field of hydrographic water well drilling engineering, in particular to a system and a method for allocating drilling mud formula and performance parameters.
Background
Hydrographic water well drilling is one of the important means for exploring and exploiting groundwater resources. Particularly, the water consumption of the Qinghai provinces in plateau areas is increased year by year along with the accelerated progress of industrialization and urbanization of the Qinghai provinces in recent years. Though the mountains melt snow caused by global warming, the current increasingly serious water demand of Qinghai province is relieved to a certain extent; but the problems need to be solved fundamentally and the hydrologic drilling is also relied on to develop underground water resources for solving the problems. A large amount of underground water resources are developed and utilized, the underground water level is lowered year by year, the well is built more and more deeply, and the drilling of the stratum is more and more complicated. Therefore, not only a large amount of new drilling technologies and new methods need to be developed, but also the new technologies and new methods need to be popularized and applied to actual engineering in time. The quality of the mud formulation during construction determines to a large extent the speed and quality of the drilling operation, and engineers are increasingly conscious of the various complications that arise during drilling operations that may be directly or indirectly related to the drilling fluid employed. Therefore, designing a reasonable mud formulation is the key to successful hydrographic water well drilling operations and reducing drilling costs.
However, as far as the popularization degree of the technical level of drilling mud in China is concerned, most of engineering construction processes pay less attention to the design and the adoption of the mud, the design and the treatment agent of the mud are usually carried out by engineering technicians by virtue of own engineering experience, and only after drilling is stopped, a specialist is thought to be contacted to adjust the mud on site. With the development of computer science, the related technology in the field of artificial intelligence is introduced into the design of mud formulas. Various effective methods (expert experience, engineering experience, theoretical analysis, numerical calculation, experimental simulation and field monitoring) can be integrated into an intelligent system based on knowledge, and the problem which is difficult to solve by one method can be solved by another method. In the fields of solid mineral exploration, engineering construction drilling, hydrographic water well drilling and the like, the acquisition and establishment of the expert experience and the expert knowledge base of drilling mud are complex and tedious, the development of design software is laggard, and the researched and developed results are not widely applied and popularized. Therefore, the existing research results are inherited, innovation is developed, and a system and a method for allocating drilling mud formula and performance parameters, which can be used in a drilling field, are developed, so that effective assistance is provided for field technicians to correctly carry out mud optimization design, and the method is a very significant matter.
Disclosure of Invention
In view of the above, the invention provides a system and a method for allocating drilling mud formula and performance parameters, which solve the technical problem that the prior art can not provide high-performance drilling mud formula in real time in the drilling process of a hydrographic water well.
A system for formulating drilling mud formulations and performance parameters, comprising: the system comprises a drilling stratum information module, an on-site drilling detection device, a hydrographic water well drilling field expert system, a machine learning prediction model and a control system;
the in situ drilling detection device comprises: the device comprises a drilling parameter detection device, a well body structure detection device and a slurry performance parameter detection device;
the hydrographic water well drilling field expert system includes: the system comprises an input unit, an expert reasoning module, an output unit and a whole-process informatization man-machine interface;
the machine learning prediction model comprises: a mud performance parameter prediction model and a mud formula additive dosage prediction model;
the whole process informatization human-computer interface comprises a prediction model database and an expert database;
the drilling stratum information module, the drilling parameter detection device and the well body structure detection device are all connected with an input unit in the hydrographic well drilling field expert system, an output unit in the hydrographic well drilling field expert system is connected with the control system and a mud performance parameter prediction model in the machine learning prediction model;
the whole-process informatization man-machine interface is used for establishing the prediction model database and the expert database, and completing the operations of adding, modifying, searching and deleting the databases in real time according to the actual drilling condition of on-site drilling;
and the output parameters of the mud performance parameter prediction model in the machine learning prediction model are compared with the mud performance parameter detection device, the obtained difference value is transmitted to the mud formula additive dosage prediction model, and the mud formula additive dosage prediction model is connected with the control system.
Further, the drilling formation information module is to: during drilling, formation information, including geological formations, rock properties, and subsurface fluid conditions, is acquired in real time.
Furthermore, the drilling parameter detection device is used for detecting drilling parameters including bit pressure, rotating speed, pump pressure and drilling speed in real time in the drilling process;
the well structure detection device is used for detecting well structure parameters in real time in the drilling process, and comprises the detection of the depth of a well section and the underground water-containing condition.
Furthermore, the mud performance parameter detection device is used for detecting mud circulation once in real time in the drilling process, and returning field mud performance parameters after sand removal, including density, pH value, apparent viscosity, plastic viscosity and dynamic shear force.
Further, the input unit is used for inputting a basic data source to the expert reasoning module; the basic data source comprises formation information, drilling parameters detected and uploaded by the drilling parameter detection device and well structure parameters detected and uploaded by the well structure detection device.
Further, the expert reasoning module is used for reading the basic data source input by the input unit, obtaining the stratum condition, the drilling characteristic and the drilling well structure, further obtaining the stratum category of the drilling stratum and judging the change condition of the drilling stratum.
Further, the output unit is used for outputting a corresponding formation drilling mud formula; the corresponding formation drilling mud formula comprises the components and the content of a mud additive, and the formation drilling mud formula is conveyed to the control system to prepare a corresponding mud formula; meanwhile, the corresponding formation drilling mud formula is used as an input parameter of the mud performance parameter prediction model.
Furthermore, the mud performance parameter prediction model is used for receiving a corresponding formation drilling mud formula conveyed by an expert system in the field of hydrographic water well drilling, predicting to obtain mud formula performance prediction parameters including density, pH value, apparent viscosity, plastic viscosity and dynamic shear force, and comparing the mud formula performance prediction parameters with field mud performance parameters obtained by real-time detection of the field mud performance parameter detection device.
Further, the mud formula additive dosage prediction model is used for obtaining a difference value parameter between a mud formula performance prediction parameter obtained by the prediction of the mud performance parameter prediction model and a field mud performance parameter obtained by the real-time detection of the field mud performance parameter detection device, wherein the difference value parameter comprises density, a pH value, an apparent viscosity, a plastic viscosity and a dynamic shear force, and the type and the content of the additive needing to be regulated in dosage are obtained by prediction, and the additive is conveyed to the control system to correct the performance of the mud treated on site in time.
A method of formulating drilling mud and performance parameters for use in any one of the systems of formulating drilling mud and performance parameters described herein, comprising the steps of:
s1, uploading the drilling parameters, the well section depth, the underground water containing condition and the formation information acquired by the input unit to an expert reasoning module in the expert system in the field of hydrographic water well drilling;
s2, the expert reasoning module obtains a mud formula of the corresponding stratum through a reasoning mode based on rules, the mud formula comprises the types and the contents of the additives, the types and the contents of the additives are transmitted to the control system, and the on-site drilling mud is prepared;
s3, inputting the slurry formula obtained in the step S2 into the machine learning prediction model, establishing a slurry performance parameter prediction model, and outputting to obtain corresponding slurry performance parameters including density, PH value, apparent viscosity, plastic viscosity and dynamic shear force;
s4, comparing the density, the PH value, the apparent viscosity, the plastic viscosity and the dynamic shear force of the slurry performance parameters obtained in the step S3 with the field slurry performance parameters detected by the slurry performance parameter detection device, if at least one difference parameter is larger than a set threshold value, executing the step S6, otherwise executing the step S5;
s5, the expert reasoning module utilizes the stratum condition, the drilling parameter and the well structure to reasoning and judge the change condition of the stratum, if the stratum changes, the step S2 is executed again to obtain a new mud formula, otherwise, the mud detected on site in the step S4 is directly used to continue to finish the drilling work;
s6, inputting the difference parameters of the slurry performance parameters, such as density, PH value, apparent viscosity, plastic viscosity and dynamic shear force, obtained in the step S4 into the slurry formula additive dosage prediction model in the machine learning prediction model, establishing the slurry formula additive dosage prediction model, outputting the difference parameters to obtain the type and content of the additive needing dosage adjustment, transmitting the output information to the control system, correcting the slurry processed on site in time, and continuing to complete the drilling work.
The technical scheme provided by the invention has the beneficial effects that: (1) the invention effectively solves the problems that the mud preparation result has great difference or the ideal effect cannot be achieved due to the difference of the technical level and the working environment of different designers in the field drilling work of the hydrological water well; (2) the invention can provide the corresponding mud formula of the stratum according to the actual condition of the on-site stratum, and further realize the mud dosage treatment after the mud circulation treatment, thereby adjusting the drilling mud performance in time, improving the utilization rate of the mud, simultaneously greatly increasing the working benefit of on-site drilling and drilling, reducing unnecessary manpower and material resources, and providing scientific decision for the mud formula required by the hydrographic well drilling engineering.
Drawings
FIG. 1 is a system architecture diagram of the present invention;
FIG. 2 is an architecture diagram of a hydrographic water well drilling field expert system of an embodiment of the present invention;
FIG. 3 is an architecture diagram of a mud property parameter prediction model according to an embodiment of the present invention;
FIG. 4 is a block diagram of a mud formulation additive dosage prediction model according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be further described with reference to the accompanying drawings.
Referring to FIG. 1, a system for formulating drilling mud and performance parameters is provided, comprising: the system comprises a drilling stratum information module, an on-site drilling detection device, a hydrographic water well drilling field expert system, a machine learning prediction model and a control system;
the in situ drilling detection device comprises: the device comprises a drilling parameter detection device, a well body structure detection device and a slurry performance parameter detection device;
the hydrographic water well drilling field expert system includes: the system comprises an input unit, an expert reasoning module, an output unit and a whole-process informatization man-machine interface;
the machine learning prediction model comprises: a mud performance parameter prediction model and a mud formula additive dosage prediction model;
the whole process informatization human-computer interface comprises a prediction model database and an expert database;
the drilling stratum information module, the drilling parameter detection device and the well body structure detection device in the on-site drilling detection device are all connected with the input unit in the hydrographic water well drilling field expert system, and the output unit in the hydrographic water well drilling field expert system is connected with the control system and a mud performance parameter prediction model in the machine learning prediction model;
and the output parameters of the mud performance parameter prediction model in the machine learning prediction model are compared with the mud performance parameter detection device in the on-site drilling detection device, the obtained difference is transmitted to the mud formula additive dosage prediction model in the machine learning prediction model, and the mud formula additive dosage prediction model is connected with the control system.
The drilling formation information module is to: during the drilling process, acquiring formation information in real time, wherein the formation information comprises geological structures, rock properties, underground fluid conditions and the like;
the drilling parameter detection device is used for: in the drilling process, drilling parameters including bit pressure, rotating speed, pump pressure, drilling speed and the like are detected in real time;
the well structure detection device is used for: detecting well structure parameters in real time during the drilling process, wherein the parameters comprise the depth of a well section and the water containing condition in the well;
the mud performance parameter detection device is used for: in the drilling process, detecting the circulation of the slurry once in real time, and returning field slurry performance parameters after sand removal, wherein the field slurry performance parameters comprise density, PH value, apparent viscosity, plastic viscosity and dynamic shear force;
the input unit is used for: inputting a basic data source to the expert reasoning module; the basic data source comprises the formation information, the drilling parameters detected and uploaded by the drilling parameter detection device in the on-site drilling detection device, and the well structure parameters detected and uploaded by the well structure detection device in the on-site drilling detection device;
the expert reasoning module is used for: reading the basic data source input by the input unit, obtaining stratum conditions, drilling characteristics and a drilling well body structure, further obtaining stratum types of the drilling stratum, and judging the change condition of the drilling stratum;
the output unit is used for: outputting a corresponding formation drilling mud formula; the corresponding formation drilling mud formula comprises the components and the content of a mud additive, and is conveyed to the control system to prepare a corresponding mud formula; simultaneously, the corresponding formation drilling mud formula is used as an input parameter of the mud performance parameter prediction model;
the mud performance parameter prediction model is used for: receiving the corresponding formation drilling mud formula conveyed by the expert system in the field of hydrographic water well drilling, predicting to obtain mud formula performance prediction parameters, wherein the mud formula performance prediction parameters comprise density, PH value, apparent viscosity, plastic viscosity and dynamic shear force, and comparing the mud formula performance prediction parameters with the field mud performance parameters obtained by the field mud performance parameter detection device through real-time detection;
the mud formulation additive dosage prediction model is used for: obtaining a difference value parameter of a slurry formula performance prediction parameter obtained by predicting the slurry performance parameter prediction model and a field slurry performance parameter obtained by detecting the field slurry performance parameter detection device in real time, wherein the difference value parameter comprises density, a PH value, an apparent viscosity, a plastic viscosity and a dynamic shear force, predicting to obtain the type and the content of an additive needing to be added and adjusted, and conveying the type and the content to the control system to correct the performance of the slurry treated on the field in time;
referring to fig. 2, the input unit of the expert system in the field of hydrographic water well drilling is the formation condition, drilling parameters and well structure of the drilled formation, and the expert reasoning module includes: the system comprises an inference machine, an expert database and an expert rule base, wherein an output unit is a slurry formula.
A method of formulating drilling mud and performance parameters for use in any one of the systems of formulating drilling mud and performance parameters described herein, comprising the steps of:
s1, the drilling parameter detection device and the well body structure detection device in the on-site drilling detection device are connected with the input unit in the expert system in the field of hydrographic water well drilling, and the input unit acquires the drilling parameters detected by the drilling parameter detection device, the well section depth and the underground water-containing condition detected by the well body structure detection device and formation information; uploading the data to the expert reasoning module in the expert system in the field of hydrographic water well drilling;
s2, the expert reasoning module in the hydrographic water well drilling field expert system obtains a mud formula of a corresponding stratum through a rule-based reasoning mode by utilizing the obtained basic data including stratum information (geological structure, rock property, underground fluid condition and the like), drilling parameters (drilling pressure, rotating speed, pumping pressure, drilling speed and the like) of a drilling stratum, the mud formula comprises the types and the contents of additives, the types and the contents of the additives are transmitted to the control system to prepare drilling mud, and the expert rules are expressed by a production rule.
The expert rule and reasoning mechanism is logically represented as follows:
IF: formation and rock properties and subterranean fluid conditions and pressure and velocity and pump and rate and well bore structures
THEN-type of drilling fluid-type of formation mud and additives-type and content-bentonite content + Na2CO3Content + CaCl2Content + HEC content + LV-CMC content + … …
Referring to fig. 3, the input parameters of the mud performance parameter prediction model are the types and contents of the additives of the mud formula obtained in step S2, the types and contents of the additives include bentonite, soda ash, barite, etc., the output parameters are mud performance parameters, and the output parameters are compared with the field mud performance parameters detected by the mud performance parameter detection device.
S3, inputting the slurry formula obtained in the step S2 into the slurry performance parameter prediction model in the machine learning prediction model, establishing a slurry performance parameter prediction model, and outputting to obtain corresponding slurry performance parameters including density, PH value, apparent viscosity, plastic viscosity and dynamic shear force;
s4, comparing the density, the PH value, the apparent viscosity, the plastic viscosity and the dynamic shear force of the slurry performance parameters obtained in the step S3 with the prediction parameters by using a slurry performance parameter detection device, and executing the step S6 if at least one difference parameter is greater than a set threshold value, or executing the step S5;
the mud performance parameter detection device detects that the mud is recycled in step S2 once and returns to the mud subjected to sand removal treatment;
s5, the expert reasoning module utilizes the stratum condition, the drilling parameter and the well structure to reasoning and judge the change condition of the stratum, if the stratum changes, the step S2 is executed again to obtain a new mud formula, otherwise, the mud detected on site in the step S4 is directly used to continue to finish the drilling work;
referring to fig. 4, the input parameters of the mud formulation additive dosage prediction model are the mud formulation performance parameter density obtained in step S4, including the difference between density, PH value, apparent viscosity, plastic viscosity and dynamic shear force, and the output parameters are the types and contents of additives to be adjusted in dosage, including the types and contents of bentonite, soda ash, barite and other additives, and are uploaded to the control system;
s6, inputting parameters into the mud formula additive dosage prediction model in the machine learning prediction model, wherein the input parameters are the difference parameters of the mud performance parameters such as density, PH value, apparent viscosity, plastic viscosity and dynamic shear force obtained in the step S4, establishing the mud formula additive dosage prediction model, outputting the mud formula additive dosage prediction model to obtain the type and content of the additive needing dosage adjustment, transmitting output information to the control system, correcting the mud treated on site in time, and continuing to complete the drilling work.
The input parameter of the mud formula additive dosage prediction model is a difference parameter of mud formula performance parameters, and the output parameter is the type and content of the additive of the mud formula dosage; the two prediction models are forward-pushed to obtain a mud formula performance parameter model, backward-pushed to obtain a mud formula additive addition model, finally the backward-pushed result of the mud formula additive addition model is input into the mud formula performance parameter model, and the results of the two models are compared to verify the accuracy of the models.
The whole process informatization man-machine interface is used for: the establishment of the prediction model database and the expert database can facilitate field drilling personnel to complete the operations of adding, modifying, searching and deleting the database in real time according to the actual drilling condition of field drilling, so that the designed system is closer to the actual drilling work of the hydrographic well.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A system for formulating drilling mud formulations and performance parameters, comprising: the system comprises a drilling stratum information module, an on-site drilling detection device, a hydrographic water well drilling field expert system, a machine learning prediction model and a control system;
the in situ drilling detection device comprises: the device comprises a drilling parameter detection device, a well body structure detection device and a slurry performance parameter detection device;
the hydrographic water well drilling field expert system includes: the system comprises an input unit, an expert reasoning module, an output unit and a whole-process informatization man-machine interface;
the machine learning prediction model comprises: a mud performance parameter prediction model and a mud formula additive dosage prediction model;
the whole process informatization human-computer interface comprises a prediction model database and an expert database;
the drilling stratum information module, the drilling parameter detection device and the well body structure detection device are all connected with an input unit in the hydrographic well drilling field expert system, an output unit in the hydrographic well drilling field expert system is connected with the control system and a mud performance parameter prediction model in the machine learning prediction model;
the whole-process informatization man-machine interface is used for establishing the prediction model database and the expert database, and completing the operations of adding, modifying, searching and deleting the databases in real time according to the actual drilling condition of on-site drilling;
and the output parameters of the mud performance parameter prediction model in the machine learning prediction model are compared with the mud performance parameter detection device, the obtained difference value is transmitted to the mud formula additive dosage prediction model, and the mud formula additive dosage prediction model is connected with the control system.
2. The system of claim 1, wherein the drilling formation information module is configured to obtain formation information including geological formations, rock properties, and subterranean fluid conditions in real time during the drilling process.
3. The system for formulating drilling mud and performance parameters of claim 1 wherein said drilling parameter sensing device is adapted to sense drilling parameters including weight-on-bit, rotational speed, pump pressure, and rate-of-penetration in real time during the drilling process;
the well structure detection device is used for detecting well structure parameters in real time in the drilling process, and comprises the detection of the depth of a well section and the underground water-containing condition.
4. The system of claim 1 wherein the mud performance parameter monitoring device is configured to monitor the mud circulation once during drilling and return the degritted on-site mud performance parameters including density, PH, apparent viscosity, plastic viscosity, and dynamic shear.
5. The system for drilling mud formulation and performance parameter deployment according to claim 1, wherein said input unit is configured to input a source of base data to said expert inference module; the basic data source comprises formation information, drilling parameters detected and uploaded by the drilling parameter detection device and well structure parameters detected and uploaded by the well structure detection device.
6. The system for formulating drilling mud as claimed in claim 1, wherein said expert inference module is adapted to read the basic data sources inputted by said input unit, obtain the formation conditions, drilling characteristics, drilling well structure, and further obtain the formation type of the drilled formation, and determine the change of the drilled formation.
7. The system for drilling mud formulation and performance parameter adjustment according to claim 1, wherein said output unit is adapted to output a corresponding formation drilling mud formulation; the corresponding formation drilling mud formula comprises the components and the content of a mud additive, and the formation drilling mud formula is conveyed to the control system to prepare a corresponding mud formula; meanwhile, the corresponding formation drilling mud formula is used as an input parameter of the mud performance parameter prediction model.
8. The system for formulating drilling mud as set forth in claim 1, wherein said mud property parameter prediction model is adapted to receive a corresponding formation drilling mud formulation delivered by an expert system in the field of hydrographic water well drilling, predict and obtain mud formulation property prediction parameters including density, PH, apparent viscosity, plastic viscosity and dynamic shear force, and compare the predicted mud formulation property parameters with the field mud property parameters obtained by real-time detection of said field mud property parameter detection device.
9. The system for formulating drilling mud as set forth in claim 1 wherein the mud formulation additive dosage prediction model is adapted to obtain a difference between a mud formulation performance prediction parameter predicted by the mud performance parameter prediction model and an on-site mud performance parameter measured in real time by the on-site mud performance parameter measurement device, wherein the difference comprises density, PH, apparent viscosity, plastic viscosity, and dynamic shear force, and to predict the type and amount of additive to be dosed and adjusted, and to deliver the additive to the control system to correct the on-site treated mud performance in time.
10. A method of drilling mud formulation and performance parameter deployment for use in a system for drilling mud formulation and performance parameter deployment as claimed in any one of claims 1 to 9, comprising the steps of:
s1, uploading the drilling parameters, the well section depth, the underground water containing condition and the formation information acquired by the input unit to an expert reasoning module in the expert system in the field of hydrographic water well drilling;
s2, the expert reasoning module obtains a mud formula of the corresponding stratum through a reasoning mode based on rules, the mud formula comprises the types and the contents of the additives, the types and the contents of the additives are transmitted to the control system, and the on-site drilling mud is prepared;
s3, inputting the slurry formula obtained in the step S2 into the machine learning prediction model, establishing a slurry performance parameter prediction model, and outputting to obtain corresponding slurry performance parameters including density, PH value, apparent viscosity, plastic viscosity and dynamic shear force;
s4, comparing the density, the PH value, the apparent viscosity, the plastic viscosity and the dynamic shear force of the slurry performance parameters obtained in the step S3 with the field slurry performance parameters detected by the slurry performance parameter detection device, if at least one difference parameter is larger than a set threshold value, executing the step S6, otherwise executing the step S5;
s5, the expert reasoning module utilizes the stratum condition, the drilling parameter and the well structure to reasoning and judge the change condition of the stratum, if the stratum changes, the step S2 is executed again to obtain a new mud formula, otherwise, the mud detected on site in the step S4 is directly used to continue to finish the drilling work;
s6, inputting the difference parameters of the slurry performance parameters, such as density, PH value, apparent viscosity, plastic viscosity and dynamic shear force, obtained in the step S4 into the slurry formula additive dosage prediction model in the machine learning prediction model, establishing the slurry formula additive dosage prediction model, outputting the difference parameters to obtain the type and content of the additive needing dosage adjustment, transmitting the output information to the control system, correcting the slurry processed on site in time, and continuing to complete the drilling work.
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