CN104806226B - intelligent drilling expert system - Google Patents
intelligent drilling expert system Download PDFInfo
- Publication number
- CN104806226B CN104806226B CN201510219785.6A CN201510219785A CN104806226B CN 104806226 B CN104806226 B CN 104806226B CN 201510219785 A CN201510219785 A CN 201510219785A CN 104806226 B CN104806226 B CN 104806226B
- Authority
- CN
- China
- Prior art keywords
- drilling
- well
- model
- data
- parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Classifications
-
- 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
- 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
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Geology (AREA)
- Mining & Mineral Resources (AREA)
- Physics & Mathematics (AREA)
- Environmental & Geological Engineering (AREA)
- Fluid Mechanics (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Earth Drilling (AREA)
Abstract
The present invention provides a kind of intelligent drilling expert system, including:Spot sensor detecting system, intelligent expert system and executing agency;Form automated closed-loop drilling well regulator control system.The present invention acquires the data of entire drilling process by spot sensor;Then, the data collected feeding computer is subjected to processing monitoring, forecast, analysis, explanation, control etc..Most importantly, multi-disciplinary, inter-trade intelligence and the Multimode Control model such as the control of wellbore hydraulics that the present invention researches and develops, borehole wall stability control, frictional resistance and moment of torsion control, drilling speed and cost control, drilling complexity and accident control, it can realize real-time, it is early to find, early forecast, with explanation and automatic control function, not only accurate information is provided for drilling engineering, moreover, having the advantages that reduce drilling cost, improving bit speed, prevent contingency, accurate oil and gas discovery.
Description
Technical field
The invention belongs to oil drilling exploration and development technique fields, and in particular to a kind of intelligent drilling expert system.
Background technology
Oil-well rig is by lifting system, rotary system, the circulatory system, power-equipment, ancillary equipment and rig substructure
It is a set of huge and complicated drilling well factory Deng composition, petroleum exploration & development times is completed according to drilling engineering and technological requirement
Business.
Control system and monitoring detection instrument are to embody the core system of oil-well rig technical merit, since drilling machine is machine
Multi-specialized more of tool, hydraulic pressure, gas control, automatically controlled and geology, chemistry, mathematics, geometry, the mechanics of materials, hydrodynamics etc.
The assembly of section, during drilling machine production is mating, same set of drilling machine up to tens producers it is mating, each specialized factory is because special
Industry barrier, there is a serious shortage of multi-disciplinary, inter-trade to be uniformly coordinated, and causes a whole set of drill control technical merit not high and repeats to match
Set and waste, and identical data different majors equipment detects different data, driller can not know which be it is correct, less
Effectively it can instruct and be precisely controlled equipment safety and be efficiently completed drilling well task.
In addition, in terms of actual well drilled process requirements angle, each profession local techniques have been reached same world level.Such as
The application of AC frequency conversion electric driving control system can accurately control winch rotating speed, tourist bus displacement, turntable torque, drilling well completely
Pumpage and pump pressure etc.;Drilling instrument can realize measuring multiple parameters and display;Downhole drilling, rotary steering, geosteering
It is also applied successfully Deng world Advanced Drilling Technology.
But either vertical well, inclined shaft, horizontal well or extended reach well, it is all by electric-control system control to execute equipment
What the equipment such as boring winch, turntable, top drive, slush pump and solid controlling throttling well control processed were realized, in practice of construction, same well
Body structure, due to the difference of driller personal experience, and due to substantially disconnecting in practical applications between each profession and technology
, the phenomenon that thus causing different drillers that can operate out different-effect, occurs.
Therefore, how from drilling technology requirement, reinforce overall plan design and research, it is especially integrated, automatic
Change, the design and research of intelligent control system, is a key subjects of Petroleum Machinery Manufacturing Industry.
Invention content
In view of the defects existing in the prior art, the present invention provides a kind of intelligent drilling expert system, can effectively solve above-mentioned
Problem.
The technical solution adopted by the present invention is as follows:
The present invention provides a kind of intelligent drilling expert system, including:Spot sensor detecting system, intelligent expert system and
Executing agency;The output end of the spot sensor detecting system is connect with the input terminal of the intelligent expert system;The intelligence
The output end of energy expert system is connect with the input terminal of the executing agency, and automated closed-loop drilling well regulator control system is consequently formed;
Wherein, the spot sensor detecting system is used for:In drilling well overall process, drilling well detection data is acquired in real time,
And the collected drilling well detection data is uploaded to the intelligent expert system in real time;
The intelligent expert system is used for:Receive the drilling well testing number that the spot sensor detecting system uploads
According to, and intellectual analysis is carried out to the drilling well detection data in real time, to optimize situ of drilling well technique as target, generate existing to drilling well
The regulation and control instruction of field device, and the regulation and control instruction is issued to corresponding executing agency;
The executing agency is used for:Receive the regulation and control instruction that the intelligent expert system issues, and to corresponding existing
Field device sends the regulation and control instruction, and then controls the working condition of the field device, to optimize situ of drilling well technical process.
Preferably, the spot sensor detecting system includes ground transaucer detecting system and downhole sensor detection system
System;
The ground transaucer detecting system includes winch state sensor detection subsystem, automatic bit feed state sensor
Detect subsystem, turntable state sensor detection subsystem, top drive operating status sensor detection subsystem, mud pump operation shape
State sensor detects operation monitor and detection of subsystem, mud solid phase and volume trend state sensor detection subsystem, MCC
System, well control device detection subsystem, standpipe pressure detection subsystem and annular pressure detect subsystem;
The downhole sensor detecting system includes drill bit bit pressure detection subsystem, torque-on-bit detection subsystem, drill bit
Subsystem is detected at Rotating speed measring subsystem, bottom pressure detection subsystem, bottom hole temperature (BHT) detection subsystem and bit orientation angle.
Preferably, the winch state sensor detection subsystem is for detecting following information:Winch operating status, alarm
State, tourist bus position, hook weigh and winch motor electric current;
The automatic bit feed state sensor detection subsystem is for detecting following information:It send and bores operating status, alarm shape
State, tourist bus position, hook weigh and send brill current of electric;
The turntable state sensor detection subsystem is for detecting following information:Rotary speed, turns the limitation of turntable torque
Disk drilling torque and turntable electric current;
Operating status sensor detection subsystem is driven for detecting following information in the top:Drive operating status, alarm shape in top
State, drilling torque, torque wrench moment, Top Drive RPM, arrange parameter is driven on top and electric current is driven on top;
The slush pump operating status sensor detection subsystem is for detecting following information:Pump impulse, pump pressure, discharge capacity and turn
Speed;
The mud solid phase and volume trend state sensor detection subsystem are for detecting following information:Mud entrance is close
Degree, slurry outlet density, mud entrance viscosity, slurry outlet viscosity, mud entrance flow, mud-outlet-flow, mud entrance
Temperature, slurry outlet temperature, the detection of oil-containing gassiness, mud pit liquid level, mud pit total volume, mud entrance conductivity and mud
Slurry outlet conductivity;
The operation monitor and detection subsystem of the MCC is for detecting following information:Hydraulic station pump, winch hydraulic power source, stirring
Machine, replenishment pump, aggravates pump, mixing pump, deaerator, vibrating screen, centrifugal pump and shear pump at charge pump.
Preferably, the intelligent expert system includes:Input unit, expert model, output unit and overall process are information-based
Man-machine interface;
The input unit is used to input basic data source to the expert model;The basic data source includes and drilling well
The relevant well bore project data in scene, geological environment data, device attribute data, offset well data and the scene sensing
The drilling well detection data that device detecting system is acquired and uploaded in real time;
The expert model includes wellbore hydraulics computation model, well drilling pipe column mechanics model, hole stability point
Analyse model, drilling risk prediction and diagnostic model, borehole track computation model, rate of penetration and cost forecast model and with bore ground
Stressor layer prediction model;
Wherein, the wellbore hydraulics computation model is used for:Read the basic data that the input unit is inputted
Source obtains drilling well pump characteristics, drill column structure, drill bit classification, property of drilling fluid, drilling fluid in pipe and the flow regime of annular space;
And the consumption of drilling recycle stream dynamic pressure in real time is calculated and optimizes in conjunction with hydrodynamics basic theories and determines drilling well waterpower target component,
And then generation is to the control instruction of drilling process;
The well drilling pipe column mechanics model is used for:Read the currently practical drill assembly that the input unit is inputted
Data, obtain the drilling tool attribute in the well drilling pipe column of actual use, including drilling tool classification, drilling tool length, drilling tool internal diameter outer diameter value,
The antitorque value of drilling tool tension, drilling tool service life and drilling tool class information;And when real-time drilling is calculated the drilling tool mechanics number
According to optimization determines drilling machinery limit restraint Protection parameters, and then generates the control instruction to drilling process;
The hole stability analysis model is used for:The derived data source that the input unit is inputted is read, is bored
Well liquid annular space stratum real-time pressure equilibrium response, it is automatic calculate identification formation lithology and Different Strata and lithology can
Boring property stability feature, optimizing drilling mud and hydraulic parameters;
The drilling risk prediction and diagnostic model are used for:Pre-established various drilling risk models;By the input unit
The basic data source inputted is input to the drilling risk model, the drilling risk model is run, to live drilling well
Operating mode carries out risk profile;Wherein, the drilling risk model includes:Well kick risk model, blowout risk model, leakage risk
Model and bit freezing risk model;
The operational process of the well kick risk model, the blowout risk model and the leakage risk model is:Pass through
The basic data source, the situation of change of data and drilling liquid parameter when obtaining gas detection, boring, by being examined to the gas
Data and the situation of change of drilling liquid parameter are analyzed when surveying, boring, and predict relevant risk;
The bit freezing risk model includes:The sub- risk model of the viscous suction bit freezing of pressure difference, the sub- risk model of sand setting bit freezing, cave in card
Awl risk model, the sub- risk model of well wall dropping block bit freezing, turn on pump suppress the sub- risk model of leakage bit freezing, the sub- risk of junk bit freezing in well
Model, the sub- risk model of undergauge bit freezing, the sub- risk model of key-seating sticking and the sub- risk model of balling-up sticking;
The borehole track computation model is used for:Obtain true stratigraphic section complete data, including formation lithology and close
Degree, reservoir characteristics and reference lamina, pneumatic jack, oil reservoir, interlayer, oily bed rock and its depth, formation fluid depth and Fluid pressure, stream
Volume property, the three-dimensional wellbore trajectory of real brill, drill string and its real-time working condition of each lens and drill bit, downhole drill dynamic operation condition;It is right
The stratigraphic section complete data carries out comprehensive analysis and integration, and interpretation process obtains the technical parameter of well section optimization to be drilled
And decision, and with planned well path structure geology and compared with the engineering model moment, generate the control instruction to drilling process;
The rate of penetration and cost forecast model are used for:It predicts real-time bit pressure, and bit pressure rotating speed is optimized;Specifically
For:It is associated with the mathematic(al) mode of drilling process basic law and set optimization aim, establishes drilling object function;It is basic herein
On, with Artificial Intelligence Controlling Theory and various linear, nonlinear programming approach, in the case where determining various constraintss,
Every drilling parameter of optimization object function, and then generate the control instruction to drilling process;
It is described to be used for brill prediction of formation pressure model:To bottom pressure control and the control of mud line manifold system automation;
Specially:Analysis in real time calculate termination of pumping, stop boring, pull out of hole, it is lower bore, the bottom pressure under drilling condition, it is flat with bottom pressure
Weighing apparatus is theoretical, establishes bottom pressure Controlling model, to optimizing drilling and the speed that makes a trip, and then generates the control to drilling process
System instruction, control well invade, overflow, well kick, blowout, the generation of leakage, well slough drilling failure;
The output unit is for exporting the control instruction to drilling process that the expert model is generated;It is additionally operable to defeated
Go out warning message, casing programme, drill assembly, ground environment, borehole track, mechanical parameter, hydraulic parameters, mud solid phase letter
Breath;
The overall process informationization man-machine interface is used for:Show the real-time analog picture of drilling process and various drilling well wind
Danger prompt.
Preferably, the intelligent expert system further includes:It bores front simulation module and is summarized back after boring and look into module;
The operational process for boring front simulation module includes the following steps:
S1, reading and the relevant basic data of situ of drilling well, including:Well bore project data, geological environment data, equipment category
Property data and offset well data;
S2, is based on the basic data, and simulation creates virtual emulation well;
S3, the virtual emulation well loads pending drilling technology parameter, and chooses a kind of pre-stored expert model,
The drilling technology parameter is input to the expert model and is run, the 1st drilling well appraisal report is obtained;Wherein, it the described 1st bores
Well appraisal report is used to carry out scientific evaluation to the engineering design of the virtual emulation well, complexity and risk, cost and efficiency;
S4 is based on the 1st drilling well appraisal report, adjusts the drilling technology parameter, and recycles and carry out S3, thus constantly
The drilling technology parameter is optimized and changes well, obtains best drilling technology parameter;The drilling technology parameter is referred to
Lead live drilling process;
It is summarized back after the brill and looks into the operational process of module and include the following steps:
S5, after drilling engineering, the virtual emulation well corresponding with situ of drilling well is to selected expert's mould
Type imports offset well complete report, and inputs the history real-time detector data in drilling well all-work to the expert model, described
After expert model operation, the 2nd drilling well appraisal report is obtained, and the 2nd drilling well appraisal report is deposited as offset well supplemental characteristic
Storage.
Preferably, the field device that the executing agency is driven includes:Lifting system, the circulatory system, is moved rotary system
Power equipment, drive apparatus, control system and ancillary equipment.
Preferably, the lifting system includes that winch, automatic bit feed winch, traveling system, rig tool, bank head machinery are set
It is standby;
The rotary system includes turntable, tap and top drive;
The circulatory system includes drilling pump, high pressure pipe joint, circulating tank, solid control equipment, Deal With Drilling Fluid and storage facilities;
The power-equipment includes diesel generating set, Gas Generator Set and motor;
The drive apparatus include slow down and vehicle, transfer, steering, reversing, speed change, bending moment machine driving, hydraulic power,
Hydraulic drive and electric-driving installation;
The control system includes SCR/VFD electric driving control systems, hydraulic control system and pneumatic control system;
The ancillary equipment includes gas supply supply equipment, auxiliary generating plant, hoisting aids equipment and blowout control equipment.
Preferably, the rate of penetration and cost forecast model, are specifically used for:
Step 10:Construct the parameter optimization multiple objective function of PDC drill bit drilling;
This step is specially:According to drilling speed object function and every meter of drilling cost object function, binary objective optimization is utilized
Theory constructs following parameter optimization multiple objective function:
MaxF (x)=α1f1(x)-α2f2(x)
Wherein:
F(x):Object function;
f1(x) it is rate of penetration target, unit m/h;
f2(x) it is every meter of drilling cost target, unit is member/m;
α1,α2For reflect policymaker's wish each object function weight,
Step 20:The constraints for determining the parameter optimization multiple objective function is established PDC according to the constraints and is bored
Head drilling parameter Optimized model;
Wherein, the constraints includes:Bit pressure constraints, rotating speed constraints, bit wear constraints, drilling depth
Constraints, rate of penetration constraints and drilling cost constraints;
Step 30:According to the real-time detection parameters of drilling, real-time regression Duffing equation parameter;
Step 40:According to the PDC drill bit drilling parameter Optimized model, the drilling parameter of PDC drill bit is determined, realize drilling
The multiple-objection optimization of parameter.
Preferably, step 10 is specially:
The parameter optimization multiple objective function established is the object function of a broad sense;Work as weight α1=0, α2When=1, mesh
Scalar functions become every meter of drilling cost least model;Work as weight α1=1, α2When=0, object function becomes rate of penetration maximum norm
Type;
PDC drill bit equation for drilling rate and wear equation are the bases of PDC drill bit drilling parameter objective optimization;I.e.:It establishes following
PDC drill bit ROP mode and the equation that is combined of quaternary ROP mode:
In formula:
vpeFor drilling speed, m/h;
K be and the relevant coefficient of colligation of drillability of rock factor;
WsFor than bit pressure, kN/cm;
N is drill speed, r/min;
D is drill bit than water power, W/cm2;
E is the truth of a matter of natural logrithm, is an irrational number, is approximately equal to 2.71828;
Δ p is bottom hole pressure difference, Mpa;
a1,b1,c1,d1The respectively influence coefficient of bit pressure, rotating speed, water power and bottom hole pressure difference to drilling speed;
Assuming that bit breakage is mainly to occur in the form of bit wear, then damaged according to bit life equation and linear accumulation
Wound is theoretical, and the bit wear equation that a certain interval or a certain microbedding section can be obtained is:
In formula:
φ is internal friction angle of rock, degree;
T is rotating hours, h;
hfFor bit wear amount, dimensionless;
Parameter a, b, c, d is bit life equation coefficient;
N is rotating speed to be optimized, rpm;
W is bit pressure to be optimized, kN;
By integral, drill bit total drilling time t can be obtainedfFor:
tf=a φbWcedN(hf2-hf1)
hf2, hf1The respectively bit wear amount at current time and previous moment, dimensionless;
Total footage HfFor:
Drilling speed is:
Every meter of drilling cost is:
In formula:
CpmFor every meter of drilling cost, member/m;
CbFor drill bit cost, member/only;
CrFor drilling machine operating cost;
ttTo make a trip, making up a joint the time;
Multiple objective function expression formula is:
It determines constraints, drilling parameter is analysed in depth, the parameter calculated in above-mentioned equation is returned, according to application
The improved simulated annealing genetic algorithm based on real coding solves PDC drill bit drilling parameter Model for Multi-Objective Optimization,
Generally determine PDC drill bit drilling parameter.
Preferably, described that drilling parameter is analysed in depth, the parameter calculated in above-mentioned equation is returned, is changed according to application
Into simulated annealing genetic algorithm based on real coding solve PDC drill bit drilling parameter Model for Multi-Objective Optimization, specifically
For:
Step1:Evolutionary generation counter initializes:t←0.
Step2:Randomly generate initial population P (t);
Step3:Evaluate the adaptive value of population P (t);
Step4:Individual intersection operates:P’(t)←Crossover[P(t)];
Step5:Individual variation operates:P”(t)←Mutation[P’(t)];
Step6:Individual simulated annealing operation:P”’(t)←SimulatedAnnealing[P”(t)];
Step7:Evaluate the adaptive value of population P " ' (t);
Step8;Individual choice replicates operation:P(t+1)←Reproduction[P(t)U P”’(t)];
Step9:End condition judges;
If being unsatisfactory for end condition,:T ← t+1 goes to Step4, continues evolutionary process:If meeting end condition,:
Current optimum individual is exported, algorithm terminates.
Beneficial effects of the present invention are as follows:
Intelligent drilling expert system provided by the invention has the following advantages:
Intelligent drilling expert system provided by the invention acquires the data of entire drilling process by spot sensor, packet
All equipment related with drilling well and drilling engineering data of ground and underground are included, such as:Hoisting system of rig, follows at rotary system
The state and operation data of loop system, power-equipment, ancillary equipment etc.;Then, by the data collected be sent into computer into
Row, which is handled, to be monitored, forecasts, analyzes, explains, controls etc..Wherein, it is most important that, the control of wellbore hydraulics that the present invention researches and develops,
The multi-disciplinary, inter-bank such as borehole wall stability control, frictional resistance and moment of torsion control, drilling speed and cost control, drilling complexity and accident control
Industry intelligence and Multimode Control model, can realize real-time, it is early find, early forecast, with explaining and automatic control function, not only for
Drilling engineering provides accurate information, moreover, system can automatically make quick decision, having reduces drilling cost, improves and bore
Well speed prevents the advantages of contingency, accurate oil and gas discovery.
Description of the drawings
Fig. 1 is the structural schematic diagram of intelligent drilling expert system provided by the invention;
Fig. 2 is the flow chart provided by the invention to the optimization of PDC drill bit drilling parameter.
Specific implementation mode
Below in conjunction with attached drawing, the present invention is described in detail:
As shown in Figure 1, the present invention provides a kind of intelligent drilling expert system, make drilling technology to information-based, intelligence, far
Process automation direction is developed and is strided forward.Under the pressure of low oil price market, controls drilling risk, optimization drilling parameter, reduces
Drilling cost improves drilling efficiency, is oil drill equipment update technology, has filled up industry blank.For oil drilling
Company greatly improves the market competitiveness, while being also that petroleum exploration in China exploitation and energy security are made that contribution.
Specifically, including:Spot sensor detecting system, intelligent expert system and executing agency;The spot sensor
The output end of detecting system is connect with the input terminal of the intelligent expert system;The output end of the intelligent expert system with it is described
The input terminal of executing agency connects, and automated closed-loop drilling well regulator control system is consequently formed.Above-mentioned part described in detail below:
(1) spot sensor detecting system
Wherein, the spot sensor detecting system is used for:In drilling well overall process, drilling well detection data is acquired in real time,
And the collected drilling well detection data is uploaded to the intelligent expert system in real time;
In the present invention, spot sensor detecting system includes ground transaucer detecting system and downhole sensor detection system
System;
(1) ground transaucer detecting system
The ground transaucer detecting system includes winch state sensor detection subsystem, automatic bit feed state sensor
Detect subsystem, turntable state sensor detection subsystem, top drive operating status sensor detection subsystem, mud pump operation shape
State sensor detects operation monitor and detection of subsystem, mud solid phase and volume trend state sensor detection subsystem, MCC
System, well control device detection subsystem, standpipe pressure detection subsystem and annular pressure detect subsystem;
The winch state sensor detection subsystem is for detecting following information:Winch operating status, alarm condition, trip
Truck position, hook weigh and winch motor electric current;
The automatic bit feed state sensor detection subsystem is for detecting following information:It send and bores operating status, alarm shape
State, tourist bus position, hook weigh and send brill current of electric;
The turntable state sensor detection subsystem is for detecting following information:Rotary speed, turns the limitation of turntable torque
Disk drilling torque and turntable electric current;
Operating status sensor detection subsystem is driven for detecting following information in the top:Drive operating status, alarm shape in top
State, drilling torque, torque wrench moment, Top Drive RPM, arrange parameter is driven on top and electric current is driven on top;
The slush pump operating status sensor detection subsystem is for detecting following information:Pump impulse, pump pressure, discharge capacity and turn
Speed;
The mud solid phase and volume trend state sensor detection subsystem are for detecting following information:Mud entrance is close
Degree, slurry outlet density, mud entrance viscosity, slurry outlet viscosity, mud entrance flow, mud-outlet-flow, mud entrance
Temperature, slurry outlet temperature, the detection of oil-containing gassiness, mud pit liquid level, mud pit total volume, mud entrance conductivity and mud
Slurry outlet conductivity;
The operation monitor and detection subsystem of the MCC is for detecting following information:Hydraulic station pump, winch hydraulic power source, stirring
Machine, replenishment pump, aggravates pump, mixing pump, deaerator, vibrating screen, centrifugal pump and shear pump at charge pump.
(2) downhole sensor detecting system
The downhole sensor detecting system includes drill bit bit pressure detection subsystem, torque-on-bit detection subsystem, drill bit
Subsystem is detected at Rotating speed measring subsystem, bottom pressure detection subsystem, bottom hole temperature (BHT) detection subsystem and bit orientation angle.
(2) intelligent expert system
The intelligent expert system is used for:Receive the drilling well testing number that the spot sensor detecting system uploads
According to, and intellectual analysis is carried out to the drilling well detection data in real time, to optimize situ of drilling well technique as target, generate existing to drilling well
The regulation and control instruction of field device, and the regulation and control instruction is issued to corresponding executing agency;
The intelligent expert system includes:The information-based man-machine boundary of input unit, expert model, output unit and overall process
Face;
The input unit is used to input basic data source to the expert model;The basic data source includes and drilling well
The relevant well bore project data in scene, geological environment data, device attribute data, offset well data and the scene sensing
The drilling well detection data that device detecting system is acquired and uploaded in real time;
The expert model includes wellbore hydraulics computation model, well drilling pipe column mechanics model, hole stability point
Analyse model, drilling risk prediction and diagnostic model, borehole track computation model, rate of penetration and cost forecast model and with bore ground
Stressor layer prediction model;
Wherein, the wellbore hydraulics computation model is used for:Read the basic data that the input unit is inputted
Source obtains drilling well pump characteristics, drill column structure, drill bit classification, property of drilling fluid, drilling fluid in pipe and the flow regime of annular space;
And the consumption of drilling recycle stream dynamic pressure in real time is calculated and optimizes in conjunction with hydrodynamics basic theories and determines drilling well waterpower target component,
And then generation is to the control instruction of drilling process;
The well drilling pipe column mechanics model is used for:Read the currently practical drill assembly that the input unit is inputted
Data, obtain the drilling tool attribute in the well drilling pipe column of actual use, including drilling tool classification, drilling tool length, drilling tool internal diameter outer diameter value,
The antitorque value of drilling tool tension, drilling tool service life and drilling tool class information;And when real-time drilling is calculated the drilling tool mechanics number
According to optimization determines drilling machinery limit restraint Protection parameters, and then generates the control instruction to drilling process;
The hole stability analysis model is used for:The derived data source that the input unit is inputted is read, is bored
Well liquid annular space stratum real-time pressure equilibrium response, it is automatic calculate identification formation lithology and Different Strata and lithology can
Boring property stability feature, optimizing drilling mud and hydraulic parameters;
The drilling risk prediction and diagnostic model are used for:Pre-established various drilling risk models;By the input unit
The basic data source inputted is input to the drilling risk model, the drilling risk model is run, to live drilling well
Operating mode carries out risk profile;Wherein, the drilling risk model includes:Well kick risk model, blowout risk model, leakage risk
Model and bit freezing risk model;
Specifically, the present invention, to various drilling risk modeling analysis, analysis occurs drilling failure and complicated reason, formulates
It has and copes with engineering abnormal accident and complicated scheme, accomplishes early discovery, early prevention, early processing.System model covers all drilling wells
Accident and complexity.Include the bit freezing etc. of well kick, blowout, leakage and various properties.Well kick, blowout, leakage belong to head of liquid
It is unable to caused by equilibrium strata pressure, is shown in conjunction with gas detection, data when brill, the source that the variation of drilling liquid parameter judges,
And reliable kill-job density can be provided according to programs such as kill-jobs.Bit freezing include pressure difference it is viscous inhale bit freezing, sand setting bit freezing, the bit freezing that caves in,
Well wall dropping block bit freezing, turn on pump suppress leakage bit freezing, junk bit freezing, undergauge bit freezing, key-seating sticking, balling-up sticking in well.
The operational process of the well kick risk model, the blowout risk model and the leakage risk model is:Pass through
The basic data source, the situation of change of data and drilling liquid parameter when obtaining gas detection, boring, by being examined to the gas
Data and the situation of change of drilling liquid parameter are analyzed when surveying, boring, and predict relevant risk;
The bit freezing risk model includes:The sub- risk model of the viscous suction bit freezing of pressure difference, the sub- risk model of sand setting bit freezing, cave in card
Awl risk model, the sub- risk model of well wall dropping block bit freezing, turn on pump suppress the sub- risk model of leakage bit freezing, the sub- risk of junk bit freezing in well
Model, the sub- risk model of undergauge bit freezing, the sub- risk model of key-seating sticking and the sub- risk model of balling-up sticking;
The borehole track computation model is used for:Obtain true stratigraphic section complete data, including formation lithology and close
Degree, reservoir characteristics and reference lamina, pneumatic jack, oil reservoir, interlayer, oily bed rock and its depth, formation fluid depth and Fluid pressure, stream
Volume property, the three-dimensional wellbore trajectory of real brill, drill string and its real-time working condition of each lens and drill bit, downhole drill dynamic operation condition;It is right
The stratigraphic section complete data carries out comprehensive analysis and integration, and interpretation process obtains the technical parameter of well section optimization to be drilled
And decision, and with planned well path structure geology and compared with the engineering model moment, generate the control instruction to drilling process;
The rate of penetration and cost forecast model are used for:It predicts real-time bit pressure, and bit pressure rotating speed is optimized;Specifically
For:It is associated with the mathematic(al) mode of drilling process basic law and set optimization aim, establishes drilling object function;It is basic herein
On, with Artificial Intelligence Controlling Theory and various linear, nonlinear programming approach, in the case where determining various constraintss,
Every drilling parameter of optimization object function, and then generate the control instruction to drilling process;
It is described to be used for brill prediction of formation pressure model:To bottom pressure control and the control of mud line manifold system automation;
Specially:Analysis in real time calculate termination of pumping, stop boring, pull out of hole, it is lower bore, the bottom pressure under drilling condition, it is flat with bottom pressure
Weighing apparatus is theoretical, establishes bottom pressure Controlling model, to optimizing drilling and the speed that makes a trip, and then generates the control to drilling process
System instruction, control well invade, overflow, well kick, blowout, the generation of leakage, well slough drilling failure;
The output unit is for exporting the control instruction to drilling process that the expert model is generated;It is additionally operable to defeated
Go out warning message, casing programme, drill assembly, ground environment, borehole track, mechanical parameter, hydraulic parameters, mud solid phase letter
Breath;
The overall process informationization man-machine interface is used for:Show the real-time analog picture of drilling process and various drilling well wind
Danger prompt.
The intelligent expert system further includes:It bores front simulation module and is summarized back after boring and look into module;
The operational process for boring front simulation module includes the following steps:
S1, reading and the relevant basic data of situ of drilling well, including:Well bore project data, geological environment data, equipment category
Property data and offset well data;
S2, is based on the basic data, and simulation creates virtual emulation well;
S3, the virtual emulation well loads pending drilling technology parameter, and chooses a kind of pre-stored expert model,
The drilling technology parameter is input to the expert model and is run, the 1st drilling well appraisal report is obtained;Wherein, it the described 1st bores
Well appraisal report is used to carry out scientific evaluation to the engineering design of the virtual emulation well, complexity and risk, cost and efficiency;
S4 is based on the 1st drilling well appraisal report, adjusts the drilling technology parameter, and recycles and carry out S3, thus constantly
The drilling technology parameter is optimized and changes well, obtains best drilling technology parameter;The drilling technology parameter is referred to
Lead live drilling process;
It is summarized back after the brill and looks into the operational process of module and include the following steps:
S5, after drilling engineering, the virtual emulation well corresponding with situ of drilling well is to selected expert's mould
Type imports offset well complete report, and inputs the history real-time detector data in drilling well all-work to the expert model, described
After expert model operation, the 2nd drilling well appraisal report is obtained, and the 2nd drilling well appraisal report is deposited as offset well supplemental characteristic
Storage.
That is, the control of drilling well overall process function monitoring is divided into:It bores real time monitoring in front simulation, brill, summarized back after brill
Look into function.
A well can be beaten according to the input data simulation in addition to real-time detection by boring front simulation, be set to the engineering of this mouthful of well
Meter, complicated and risk, cost and efficiency etc. carry out scientific evaluation;It summarizes back and looks into after brill, for the various history of drilling well overall process
Data are further summarized, are analyzed, and report is generated, and are stored in database, be can be used as offset well data and are provided more optimized number to drilling well in future
According to guidance;Real time monitoring is patent core of the present invention in brill, is mainly calculated by wellbore hydraulics, well drilling pipe column calculates, wellbore is steady
Qualitative analysis, drilling risk prediction and diagnosis, borehole track calculating, rate of penetration and forecasting of cost, with boring, strata pressure etc. is soft
Part model support is completed.Each control and functional mode strictly require to set out with site technique, integrate the reason of different industries and profession
By science and professional technique, includes the operating instruction and experience of scene optimization, carry out scientific and reasonable analysis and calculating, completely
Take precautions against and prevented various artificial erroneous judgements, maloperation, give arbitrary and impracticable direction, high risk, poor efficiency the problems such as.
Therefore, by intelligent drilling expert system, pass through casing programme, drill assembly, ground environment, borehole track
(mark), the informationization for taking rock situation etc., the real-time processing and analysis of drilling parameter, realize the visualizing monitor to drilling process,
Risk automatic control alarm, optimized drilling instruction output etc..
(3) executing agency
The executing agency is used for:Receive the regulation and control instruction that the intelligent expert system issues, and to corresponding existing
Field device sends the regulation and control instruction, and then controls the working condition of the field device, to optimize situ of drilling well technical process.
The field device that executing agency is driven includes:Lifting system, rotary system, the circulatory system, power-equipment, transmission
Equipment, control system and ancillary equipment.
Wherein, the lifting system includes that winch, automatic bit feed winch, traveling system, rig tool, bank head machinery are set
It is standby;
The rotary system includes turntable, tap and top drive;
The circulatory system includes drilling pump, high pressure pipe joint, circulating tank, solid control equipment, Deal With Drilling Fluid and storage facilities;
The power-equipment includes diesel generating set, Gas Generator Set and motor;
The drive apparatus include slow down and vehicle, transfer, steering, reversing, speed change, bending moment machine driving, hydraulic power,
Hydraulic drive and electric-driving installation;
The control system includes SCR/VFD electric driving control systems, hydraulic control system and pneumatic control system;
The ancillary equipment includes gas supply supply equipment, auxiliary generating plant, hoisting aids equipment and blowout control equipment.
In addition, for common different bite types during current actual well drilled, in conjunction with the use of live drilling equipment
Situation, it is proposed that different prediction models come in drilling process rate of penetration and cost predict.Below with PDC drill bit
For, introduce a kind of specific prediction model of rate of penetration and cost forecast model.
As shown in Fig. 2, being predicted rate of penetration and cost to be a kind of by optimizing to PDC drill bit drilling parameter
Flow chart.
Step 1:Construct the multiple objective function of PDC drill bit drilling parameter optimization
Step 2:The constraints for determining parameter optimization multiple objective function establishes PDC drill bit drilling ginseng according to constraints
Number Optimized model.
Step 3:According to the real-time detection parameters of drilling, real-time regression Duffing equation parameter.
Step 4:The drilling parameter that PDC drill bit is determined according to above-mentioned Optimized model realizes the multiple-objection optimization of drilling parameter.
Wherein step 1 further comprises:
According to drilling well actual conditions, it is based on two object functions:Drilling speed and every meter of drilling cost, utilize binary objective optimization
The object function of theory building drilling parameter optimization.The object function of drilling parameter optimization is:
MaxF (x)=α1f1(x)-α2f2(x)
Wherein:
f1(x) it is rate of penetration target, unit m/h;
f2(x) it is every meter of drilling cost target, unit is member/m;
α1,α2For reflect policymaker's wish each object function weight,
In step 2, the constraints includes:Bit pressure constraints, rotating speed constraints, bit wear constrain item
Part, drilling depth constraints, rate of penetration constraints and drilling cost constraints.
It is worth noting that the drilling parameter multi-goal optimizing function established is the object function of a broad sense.Hold power
Weight α1=0, α2When=1, object function reforms into every meter of drilling cost least model;Work as weight α1=1, α2When=0, target letter
Number has reformed into rate of penetration maximum model.
PDC drill bit equation for drilling rate and wear equation are the bases of PDC drill bit drilling parameter objective optimization.It is innovated in this implementation
Property the method that is combined of PDC drill bit ROP mode and quaternary ROP mode, learn from other's strong points to offset one's weaknesses, be conducive to calculate and optimization.
In formula:
V is drilling speed, m/h;
K is and the relevant coefficient of colligation of factors such as the drillability of rock;
WsFor than bit pressure, kN/cm;
N is drill speed, r/min;
D is drill bit than water power, W/cm2;
Δ p is bottom hole pressure difference, Mpa;
a1,b1,c1,d1The respectively influence coefficient of bit pressure, rotating speed, water power and bottom hole pressure difference to drilling speed.
Assuming that bit breakage is mainly to occur in the form of bit wear, then according to bit life equation and linear accumulation
Defect theory, can be obtained a certain interval or full well section (according to formation parameter principle of invariance, is divided into multiple layers by a certain microbedding section
Section or multiple microbedding sections) bit wear equation be:
In formula:
φ is internal friction angle of rock, degree;
T is rotating hours, h;
hfFor bit wear amount, dimensionless;
Parameter a, b, c, d is bit life equation coefficient;
N is rotating speed to be optimized, rpm;
W is bit pressure to be optimized, kN.
By integral, drill bit total drilling time t can be obtainedfFor:
tf=a φbWcedN(hf2-hf1)
Total footage HfFor:
Drilling speed is:
Every meter of drilling cost is:
In formula:
CpmFor every meter of drilling cost, member/m;
CbFor drill bit cost, member/only;
CrFor drilling machine operating cost;
ttTo make a trip, making up a joint the time.
Multiple objective function expression formula is:
It determines constraints, drilling parameter is analysed in depth, the parameter calculated in above-mentioned equation is returned, according to application
The improved simulated annealing genetic algorithm based on real coding solves PDC drill bit drilling parameter Model for Multi-Objective Optimization, solution
The certainly poor problem of genetic algorithm local search ability, to determine PDC drill bit drilling parameter on the whole.
The realization thought of this method is as follows:
Step1:Evolutionary generation counter initializes:t←0.
Step2:Randomly generate initial population P (t).
Step3:Evaluate the adaptive value of population P (t).
Step4:Individual intersection operates:P’(t)←Crossover[P(t)].
Step5:Individual variation operates:P”(t)←Mutation[P’(t)].
Step6:Individual simulated annealing operation:P”’(t)←SimulatedAnnealing[P”(t)].
Step7:Evaluate the adaptive value of population P " ' (t).
Step8;Individual choice replicates operation:P(t+1)←Reproduction[P(t)U P”’(t)].
Step9:End condition judges.
If being unsatisfactory for end condition,:T ← t+1 goes to Step4, continues evolutionary process:If meeting end condition,:
Current optimum individual is exported, algorithm terminates.
In specific implementation, intelligent drilling expert system provided by the invention is made of system hardware and software.
(1) hardware implements explanation
Including:1GPS/3G antennas, 2 displays, 3 explosion-resistant enclosures, 4 power supplys, 5 system master making sheet and spot sensor,
The compositions such as CAN acquisition modules.Whole integrated explosion-proof design, is installed in driller control house of drilling rig.If drilling engineer,
Head pusher, platform manager host computer display terminal, are connect by communication cable with host, information real-time synchronization.According to different hilllocks
Position setting corresponding authority.
(2), software systems implement explanation
System software is made of boundary layer, kernel service layer, Data support, driving application layer etc..
Wherein, boundary layer mainly completes man-machine information and control instruction interactive function.By driller's main interface, drilling engineer
The compositions such as interface, platform manager interface, head pusher interface, Facilities Engineer interface.Wherein, driller's main interface and system host
Integrated design, by HDMA interface drivers, other interfaces are deployed in PC machine, and long-range friendship in real time is realized by network and host
Mutually.
By boundary layer, system is made to realize Complete Information, including real-time drilling machinery ginseng first to drilling well overall process
Number, hydraulic parameters, actual efficiency data, geography information, formation information, safe condition etc..Meanwhile drilling well overall process is realized
It is the large closed-loop control being oriented to target.
Kernel service layer is mainly support with various software models.Using intelligent algorithm, to drilling machinery parameter, water
Force parameter, accident and the complicated, borehole wall and frictional resistance etc. modeling program.Mainly it is made of following subitem:Bottom schedule driven mould
Block, well data handle analysis module, drilling risk diagnosis prediction module, rate of penetration inspection optimization module, waterpower ginseng in real time
Count inspection optimization module, drag and torque computing module, with brill formation pressure calculation module etc..
Data support by device attribute data, drilling tool data, drilling engineering data, historical empirical data, in real time
Monitoring data derive from the compositions such as calculating data.Using database technology, high efficient and flexible is called, stores and is returned and looks into.
System data layer uses Mysql5.1 databases, and stable after transplanting optimizes, speed is fast, professional platform independence energy
It is good.
It is divided into following several classes by system function and service logic:
(1) real-time data collection table (SSCJinfo) is stored in whole gathered datas;
(2) derivation amount information table (PSinfo) is calculated, the derivation amount directly calculated is stored in;
(3) algorithm derives from scale (SFPSLinfo), is stored in the derivation amount calculated from algorithm;
(4) Optimal Parameters information table (YHCSinfo), the Optimal Parameters that deposit algorithm calculates in brill;
(5) and the relevant data of accident, accident critical parameter table (STPDinfo), this table store the reality of all accidents judgements
When gathered data, derivation amount, threshold values;Accident record table (SGJLinfo) etc.;
(6) other units need the data stored, including the parameter of optimization before drilling, equipment running status, driller's operation letter
Breath, warning message, down-hole pressure prompt, system prompt execute record, accident and complex situations parameter and processing;
(7) corresponding every real data is generated in each item data of storage design and operation.
In recent years, due to the universal operation of ultradeep well, extended reach well and horizontal well so that drilling prospection development difficulty is continuous
It increases, oil drilling exploration all over the world and exploitation Frequent Accidents, severe and great casualty such as Mexico of Britain BP companies offshore oil
Drilling platforms accident, China Petroleum Chongqing Kaixian gas blowout accident etc. cause the great warp such as equipment scrapping, casualties, environmental pollution
Ji loss and serious social influence.In addition, small accident such as well kick, leakage, bit freezing etc. almost occurs everyday.According to non-public data
Incomplete statistics, the direct economic loss that domestic each oil drilling company handles small-sized production accident every year is few then millions of, more
Then several ten million.
The application of intelligent drilling expert system provided by the invention can make drilling well overall process prevent maloperation completely, miss
The man's activities such as judge, give arbitrary and impracticable direction so that drilling engineering safety and be efficiently completed.Meanwhile spudder's number of passes of situ of drilling well
It is negative for engineers and technicians, head pusher personnel, drilling team administrative staff and higher level according to the multistage industrial network of composition field level
Duty personnel understand drilling well overall process information, participate in and cooperate at any time, and pass through Internet or wireless network real time remote
Corporate HQ is transmitted to being drilled with meter borehole track, adjusting drilling method, determine completion strategy etc., proposes that expert consultation decision refers to
Opinion is enabled, drilling crew is fed back to, real-time optimized drilling construction is realized, can also make drilling well and reservoir geology personnel " perspective " stratum
The positive drilling well of image real-time oversight and the well track for waiting for drilling well, and directly control drilling equipment and precisely hit target a layer target spot, it is
Oil and gas discovery provides the technical guarantee of update.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
Depending on protection scope of the present invention.
Claims (1)
1. a kind of intelligent drilling expert system, which is characterized in that including:Spot sensor detecting system, intelligent expert system and
Executing agency;The output end of the spot sensor detecting system is connect with the input terminal of the intelligent expert system;The intelligence
The output end of energy expert system is connect with the input terminal of the executing agency, and automated closed-loop drilling well regulator control system is consequently formed;
Wherein, the spot sensor detecting system is used for:In drilling well overall process, drilling well detection data is acquired in real time, and will
The collected drilling well detection data is uploaded to the intelligent expert system in real time;
The intelligent expert system is used for:The drilling well detection data that the spot sensor detecting system uploads is received, and
Intellectual analysis is carried out to the drilling well detection data in real time, to optimize situ of drilling well technique as target, generation sets situ of drilling well
Standby regulation and control instruction, and the regulation and control instruction is issued to corresponding executing agency;
The executing agency is used for:The regulation and control instruction that the intelligent expert system issues is received, and is set to corresponding scene
Preparation send the regulation and control instruction, and then controls the working condition of the field device, to optimize situ of drilling well technical process;
The intelligent expert system includes:Input unit, expert model, output unit and overall process informationization man-machine interface;
The input unit is used to input basic data source to the expert model;The basic data source includes and situ of drilling well
Relevant well bore project data, geological environment data, device attribute data, offset well data and spot sensor inspection
The drilling well detection data that examining system is acquired and uploaded in real time;
The expert model includes wellbore hydraulics computation model, well drilling pipe column mechanics model, hole stability analysis mould
Type, drilling risk prediction and diagnostic model, borehole track computation model, rate of penetration and cost forecast model and with bore be laminated
Power prediction model;
Wherein, the wellbore hydraulics computation model is used for:The basic data source that the input unit is inputted is read, is obtained
Drilling well pump characteristics, drill column structure, drill bit classification, property of drilling fluid, drilling fluid in pipe and the flow regime of annular space;And it calculates
It obtains creeping into recycle stream dynamic pressure consumption in real time, in conjunction with Hydrodynamics Theory, optimizes and determine drilling well waterpower target component, and then generation pair
The control instruction of drilling process;
The well drilling pipe column mechanics model is used for:Read the currently practical drill assembly number that the input unit is inputted
According to obtaining the drilling tool attribute in the well drilling pipe column of actual use, including drilling tool classification, drilling tool length, drilling tool internal diameter outer diameter value, bore
Have the antitorque value of tension, drilling tool service life and drilling tool class information;And when real-time drilling is calculated the drilling tool Mechanical Data,
Optimization determines drilling machinery limit restraint Protection parameters, and then generates the control instruction to drilling process;
The hole stability analysis model is used for:The derived data source that the input unit is inputted is read, drilling fluid is obtained
Real-time pressure equilibrium response on annular space stratum, the automatic drillability for calculating identification formation lithology and Different Strata and lithology
Stability feature, optimizing drilling mud and hydraulic parameters;
The drilling risk prediction and diagnostic model are used for:Pre-established drilling risk model;The input unit is inputted
The basic data source is input to the drilling risk model, runs the drilling risk model, is carried out to live drilling condition
Risk profile;Wherein, the drilling risk model includes:Well kick risk model, blowout risk model, leakage risk model and card
Bore risk model;
The operational process of the well kick risk model, the blowout risk model and the leakage risk model is:By described
Basic data source, the situation of change of data and drilling liquid parameter when obtaining gas detection, boring, by the gas detection, brill
When data and the situation of change of drilling liquid parameter analyzed, predict relevant risk;
The bit freezing risk model includes:The sub- risk model of the viscous suction bit freezing of pressure difference, the sub- risk model of sand setting bit freezing, cave in bit freezing
Risk model, the sub- risk model of well wall dropping block bit freezing, turn on pump suppress the sub- risk model of leakage bit freezing, the sub- risk mould of junk bit freezing in well
Type, the sub- risk model of undergauge bit freezing, the sub- risk model of key-seating sticking and the sub- risk model of balling-up sticking;
The borehole track computation model is used for:Obtain true stratigraphic section complete data, including formation lithology and density, storage
Layer characteristic and reference lamina, pneumatic jack, oil reservoir, interlayer, oily bed rock and its depth, formation fluid depth and Fluid pressure, fluidity
Matter, the three-dimensional wellbore trajectory of real brill, drill string and its real-time working condition of accessory and drill bit, downhole drill dynamic operation condition;To the stratum
Section complete data carries out comprehensive analysis and integration, and interpretation process obtains the technical parameter and decision of well section optimization to be drilled,
And with planned well path structure geology and compared with the engineering model moment, generate the control instruction to drilling process;
The rate of penetration and cost forecast model are used for:It predicts real-time bit pressure, and bit pressure rotating speed is optimized;Specially:
It is associated with the mathematic(al) mode of drilling process rule and set optimization aim, establishes drilling object function;On this basis, with people
Work Intelligent Control Theory and linear, nonlinear programming approach, in the case where determining constraints, the drilling of optimization object function
Parameter, and then generate the control instruction to drilling process;
It is described to be used for brill prediction of formation pressure model:To bottom pressure control and the control of mud line manifold system automation;Specifically
For:Analysis in real time calculate termination of pumping, stop boring, pull out of hole, it is lower bore, the bottom pressure under drilling condition, balance and manage with bottom pressure
By, bottom pressure Controlling model is established, thus optimizing drilling and the speed that makes a trip, and then generate and the control of drilling process is referred to
Enable, control well invade, overflow, well kick, blowout, the generation of leakage, well slough drilling failure;
The output unit is for exporting the control instruction to drilling process that the expert model is generated;It is additionally operable to output report
Alert information, casing programme, drill assembly, ground environment, borehole track, mechanical parameter, hydraulic parameters, mud solid phase information;
The overall process informationization man-machine interface is used for:Show real-time analog picture and the drilling risk prompt of drilling process;
Wherein, the intelligent expert system further includes:It bores front simulation module and is summarized back after boring and look into module;
The operational process for boring front simulation module includes the following steps:
S1, reading and the relevant basic data of situ of drilling well, including:Well bore project data, geological environment data, device attribute number
According to this and offset well data;
S2, is based on the basic data, and simulation creates virtual emulation well;
S3, the virtual emulation well loads pending drilling technology parameter, and chooses a kind of pre-stored expert model, by institute
It states drilling technology parameter to be input to the expert model and run, obtains the 1st drilling well appraisal report;Wherein, the 1st drilling well is commented
Valence report to the engineering design of the virtual emulation well, complexity and risk, cost and efficiency for carrying out scientific evaluation;
S4 is based on the 1st drilling well appraisal report, adjusts the drilling technology parameter, and recycles and carry out S3, thus constantly to institute
It states drilling technology parameter and optimizes and change well, obtain best drilling technology parameter;The drilling technology parameter instruct existing
Field drilling process;
It is summarized back after the brill and looks into the operational process of module and include the following steps:
S5, after drilling engineering, the virtual emulation well corresponding with situ of drilling well is led to selected expert model
Enter offset well complete report, and the history real-time detector data in drilling well all-work, the expert are inputted to the expert model
After model running, the 2nd drilling well appraisal report is obtained, and store the 2nd drilling well appraisal report as offset well supplemental characteristic;
The rate of penetration and cost forecast model, are specifically used for:
Step 10:Construct the parameter optimization multiple objective function of PDC drill bit drilling;
This step is specially:It is theoretical using binary objective optimization according to drilling speed object function and every meter of drilling cost object function,
Construct following parameter optimization multiple objective function:
MaxF (x)=α1f1(x)-α2f2(x)
Wherein:
F(x):Object function;
f1(x) it is rate of penetration target, unit m/h;
f2(x) it is every meter of drilling cost target, unit is member/m;
α1,α2For reflect policymaker's wish each object function weight,
Step 20:The constraints for determining the parameter optimization multiple objective function is established PDC drill bit according to the constraints and is bored
Into optimization model;
Wherein, the constraints includes:Bit pressure constraints, rotating speed constraints, bit wear constraints, drilling depth constraint
Condition, rate of penetration constraints and drilling cost constraints;
Step 30:According to the real-time detection parameters of drilling, real-time regression Duffing equation parameter;
Step 40:According to the PDC drill bit drilling parameter Optimized model, the drilling parameter of PDC drill bit is determined, realize drilling parameter
Multiple-objection optimization;
Wherein, step 10 is specially:
The parameter optimization multiple objective function established is the object function of a broad sense;Work as weight α1=0, α2When=1, target letter
Number becomes every meter of drilling cost least model;Work as weight α1=1, α2When=0, object function becomes rate of penetration maximum model;
PDC drill bit equation for drilling rate and wear equation are the bases of PDC drill bit drilling parameter objective optimization;I.e.:Establish PDC below
The equation that drill bit ROP mode and quaternary ROP mode are combined:
In formula:
vpeFor drilling speed, m/h;
K be and the relevant coefficient of colligation of drillability of rock factor;
WsFor than bit pressure, kN/cm;
N is drill speed, r/min;
D is drill bit than water power, W/cm2;
E is the truth of a matter of natural logrithm, is an irrational number, is approximately equal to 2.71828;
△ p are bottom hole pressure difference, Mpa;
a1,b1,c1,d1The respectively influence coefficient of bit pressure, rotating speed, water power and bottom hole pressure difference to drilling speed;
Assuming that bit breakage is that occur in the form of bit wear, then according to bit life equation and linear cumulative damage law,
The bit wear equation that a certain interval or a certain microbedding section can be obtained is:
In formula:
φ is internal friction angle of rock, degree;
T is rotating hours, h;
hfFor bit wear amount, dimensionless;
Parameter a, b, c, d is bit life equation coefficient;
N is rotating speed to be optimized, rpm;
W is bit pressure to be optimized, kN;
By integral, drill bit total drilling time t can be obtainedfFor:
tf=a φbWcedN(hf2-hf1)
hf2, hf1The respectively bit wear amount at current time and previous moment, dimensionless;
Total footage HfFor:
Drilling speed is:
Every meter of drilling cost is:
In formula:
CpmFor every meter of drilling cost, member/m;
CbFor drill bit cost, member/only;
CrFor drilling machine operating cost;
ttTo make a trip, making up a joint the time;
Multiple objective function expression formula is:
It determines constraints, drilling parameter is analysed in depth, the parameter calculated in above-mentioned equation is returned, according to application enhancements
Simulated annealing genetic algorithm based on real coding solve PDC drill bit drilling parameter Model for Multi-Objective Optimization, in totality
Upper determining PDC drill bit drilling parameter;
Wherein, described that drilling parameter is analysed in depth, the parameter calculated in above-mentioned equation is returned, according to the base of application enhancements
PDC drill bit drilling parameter Model for Multi-Objective Optimization is solved in the simulated annealing genetic algorithm of real coding, specially:
Step 1:Evolutionary generation counter initializes:t←0;
Step 2:Randomly generate initial population P (t);
Step 3:Evaluate the adaptive value of population P (t);
Step 4:Individual intersection operates:P’(t)←Crossover[P(t)];
Step 5:Individual variation operates:P”(t)←Mutation[P’(t)];
Step 6:Individual simulated annealing operation:P”’(t)←SimulatedAnnealing[P”(t)];
Step 7:Evaluate the adaptive value of population P " ' (t);
Step 8;Individual choice replicates operation:P(t+1)←Reproduction[P(t)U P”’(t)];
Step 9:End condition judges;
If being unsatisfactory for end condition,:T ← t+1 goes to Step4, continues evolutionary process:If meeting end condition,:Output
Current optimum individual, algorithm terminate;
Intelligent drilling expert system is made of system hardware and software, and system software is supported by boundary layer, kernel service layer, data
Layer, driving application layer composition;
Wherein, boundary layer completes man-machine information and control instruction interactive function;By driller's main interface, drilling engineer interface, put down
Platform handles interface, head pusher interface, Facilities Engineer interface composition;Wherein, driller's main interface is integrated with system host sets
Meter, by HDMA interface drivers, other interfaces are deployed in PC machine, and long-range real-time, interactive is realized by network and host;
By boundary layer, system is made to realize Complete Information, including real-time drilling mechanical parameter, water first to drilling well overall process
Force parameter, actual efficiency data, geography information, formation information, safe condition;Meanwhile drilling well overall process is realized with target
Target spot is the large closed-loop control being oriented to;
Kernel service layer is support with software model, using intelligent algorithm, to drilling machinery parameter, hydraulic parameters, accident
It programs with the complicated, borehole wall and frictional resistance modeling, is made of following subitem:Bottom schedule driven module, well data are handled in real time
Analysis module, drilling risk diagnosis prediction module, rate of penetration inspection optimization module, hydraulic parameters inspection optimization module, frictional resistance
Torque arithmetic module, with bore formation pressure calculation module;
Data support is by device attribute data, drilling tool data, drilling engineering data, historical empirical data, in real time monitoring
Data derive from calculating data composition;System data layer uses Mysql5.1 databases, stable, the speed after transplanting optimizes
Soon, professional platform independence can be good;
It is divided into following several classes by system function and service logic:
(1) real-time data collection table is stored in whole gathered datas;
(2) derivation amount information table is calculated, the derivation amount directly calculated is stored in;
(3) algorithm derives from scale, is stored in the derivation amount calculated from algorithm;
(4) Optimal Parameters information table, the Optimal Parameters that deposit algorithm calculates in brill;
(5) and the relevant data of accident, accident critical parameter table, this table store the real-time data collection of all accidents judgements, group
Raw amount, threshold values;Accident record table;
(6) unit needs the data stored, including the parameter of optimization before drilling, equipment running status, driller's operation information, alarm signal
Breath, down-hole pressure prompt, system prompt execute record, accident and complex situations parameter and processing;
(7) corresponding every real data is generated in each item data of storage design and operation;
Wherein, the spot sensor detecting system includes ground transaucer detecting system and downhole sensor detecting system;
The ground transaucer detecting system includes winch state sensor detection subsystem, the detection of automatic bit feed state sensor
Subsystem, turntable state sensor detection subsystem, operating status sensor detection subsystem is driven on top, slush pump operating status passes
Sensor detects the operation monitor and detection subsystem that subsystem, mud solid phase and volume trend state sensor detect subsystem, MCC
System, well control device detection subsystem, standpipe pressure detection subsystem and annular pressure detect subsystem;
The downhole sensor detecting system includes drill bit bit pressure detection subsystem, torque-on-bit detection subsystem, drill speed
It detects subsystem, bottom pressure detection subsystem, bottom hole temperature (BHT) detection subsystem and bit orientation angle and detects subsystem;
Wherein, the winch state sensor detection subsystem is for detecting following information:Winch operating status, alarm condition,
Tourist bus position, hook weigh and winch motor electric current;
The automatic bit feed state sensor detection subsystem is for detecting following information:It send and bores operating status, alarm condition, trip
Truck position, hook weigh and send brill current of electric;
The turntable state sensor detection subsystem is for detecting following information:Rotary speed, the limitation of turntable torque, turntable bore
Well torque and turntable electric current;
Operating status sensor detection subsystem is driven for detecting following information in the top:Drive operating status, alarm condition, brill in top
Well torque, torque wrench moment, Top Drive RPM, arrange parameter is driven on top and electric current is driven on top;
The slush pump operating status sensor detection subsystem is for detecting following information:Pump impulse, pump pressure, discharge capacity and rotating speed;
The mud solid phase and volume trend state sensor detection subsystem are for detecting following information:Mud entrance density,
Slurry outlet density, mud entrance viscosity, slurry outlet viscosity, mud entrance flow, mud-outlet-flow, mud entrance temperature
Degree, the detection of slurry outlet temperature, oil-containing gassiness, mud pit liquid level, mud pit total volume, mud entrance conductivity and mud
Export conductivity;
The operation monitor and detection subsystem of the MCC is for detecting following information:Hydraulic station pump, winch hydraulic power source, blender, filling
It notes pump, replenishment pump, aggravate pump, mixing pump, deaerator, vibrating screen, centrifugal pump and shear pump;
Wherein, the field device that the executing agency is driven includes:Lifting system, rotary system, the circulatory system, power are set
Standby, drive apparatus, control system and ancillary equipment;
Wherein, the lifting system includes winch, automatic bit feed winch, traveling system, rig tool, bank head machinery equipment;
The rotary system includes turntable, tap and top drive;
The circulatory system includes drilling pump, high pressure pipe joint, circulating tank, solid control equipment, Deal With Drilling Fluid and storage facilities;
The power-equipment includes diesel generating set, Gas Generator Set and motor;
The drive apparatus includes deceleration and vehicle, transfer, steering, reversing, speed change, bending moment machine driving, hydraulic power, hydraulic pressure
Transmission and electric-driving installation;
The control system includes SCR/VFD electric driving control systems, hydraulic control system and pneumatic control system;
The ancillary equipment includes gas supply supply equipment, auxiliary generating plant, hoisting aids equipment and blowout control equipment;
The control of drilling well overall process function monitoring is divided into:Real time monitoring in front simulation, brill is bored, is summarized back after brill and looks into function;
It bores front simulation and a well is beaten according to the input data simulation in addition to real-time detection, engineering design, complexity to this mouthful of well
Scientific evaluation is carried out with risk, cost and efficiency;It summarizes back and looks into after brill, for further total to drilling well overall process historical data
Knot, analysis, generate report, are stored in database, and providing more optimized data to drilling well in future as offset well data instructs;In brill in real time
Monitoring is core, is calculated by wellbore hydraulics, well drilling pipe column calculates, hole stability analysis, drilling risk is predicted and diagnosis, well
Eye orbit computation, rate of penetration and forecasting of cost are completed with brill strata pressure software model support;Control and functional mode are stringent
It requires to set out with site technique, integrates the pure science and professional technique of different industries and profession, including the behaviour that scene optimizes
Make regulation and experience, carry out scientific and reasonable analysis and calculating, takes precautions against and prevented artificial erroneous judgement completely, maloperation, becomes blind
Commander, high risk, low efficiency problem;
Therefore, by intelligent drilling expert system, by casing programme, drill assembly, ground environment, borehole track, rock feelings are taken
Condition is information-based, and the real-time processing and analysis of drilling parameter, realization automatically control report to the visualizing monitor of drilling process, risk
Alert, optimized drilling instruction output.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510219785.6A CN104806226B (en) | 2015-04-30 | 2015-04-30 | intelligent drilling expert system |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510219785.6A CN104806226B (en) | 2015-04-30 | 2015-04-30 | intelligent drilling expert system |
Publications (2)
Publication Number | Publication Date |
---|---|
CN104806226A CN104806226A (en) | 2015-07-29 |
CN104806226B true CN104806226B (en) | 2018-08-17 |
Family
ID=53691378
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201510219785.6A Expired - Fee Related CN104806226B (en) | 2015-04-30 | 2015-04-30 | intelligent drilling expert system |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN104806226B (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
SE2150278A1 (en) * | 2021-03-11 | 2022-09-12 | Husqvarna Ab | An automatic feed unit for feeding a core drill into a work object |
RU2779905C1 (en) * | 2019-01-31 | 2022-09-15 | Бейкер Хьюз Оилфилд Оперейшнс Ллк | Optimising an industrial machine |
Families Citing this family (65)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105422088B (en) * | 2015-11-11 | 2020-02-07 | 中国煤炭科工集团太原研究院有限公司 | Coal mine tunnel geological parameter on-line monitoring system |
WO2017206182A1 (en) * | 2016-06-03 | 2017-12-07 | Schlumberger Technology Corporation | Detecting events in well reports |
CN106121621A (en) * | 2016-07-15 | 2016-11-16 | 西南石油大学 | A kind of intelligent drilling specialist system |
CN106321059B (en) * | 2016-08-30 | 2019-03-19 | 中国海洋石油集团有限公司 | A kind of well control system and other well system safety interlock methods |
CN106194028A (en) * | 2016-08-31 | 2016-12-07 | 四川鹦鹉螺工业设备运行管理有限公司 | A kind of gas drilling pressure releasing method |
JP7122302B2 (en) | 2016-10-26 | 2022-08-19 | ニュー・ピッグ・コーポレイション | Liquid storage facility spill risk assessment |
CN106504328A (en) * | 2016-10-27 | 2017-03-15 | 电子科技大学 | A Modeling Method for Complex Geological Structures Based on Sparse Point Cloud Surface Reconstruction |
CN106545327B (en) * | 2016-12-09 | 2017-11-28 | 北京四利通控制技术股份有限公司 | Intelligent driller's control system of rig |
CN106640035A (en) * | 2016-12-19 | 2017-05-10 | 四川宏华电气有限责任公司 | VFD control system and method for automatic optimization of drilling parameters |
US10502009B2 (en) * | 2017-02-16 | 2019-12-10 | Saudi Arabian Oil Company | Smart selective drilling fluid system |
CN108694258B (en) * | 2017-04-10 | 2021-09-07 | 中国石油化工股份有限公司 | Drilling underground virtual simulation method and system for construction scheme rehearsal optimization |
CN108729911A (en) * | 2017-04-24 | 2018-11-02 | 通用电气公司 | Optimization devices, systems, and methods for resource production system |
CN108960553A (en) * | 2017-05-27 | 2018-12-07 | 中国石油化工股份有限公司 | A kind of visualizing monitor method of drillng operation information and risk |
CN107193055B (en) * | 2017-05-27 | 2019-10-18 | 中国地质大学(武汉) | A Double-layer Intelligent ROP Modeling System for Complex Geological Drilling Process |
CN110121053B (en) * | 2018-02-07 | 2021-07-20 | 中国石油化工股份有限公司 | Video monitoring method for drilling site risk grading early warning |
CN108756848B (en) * | 2018-05-21 | 2019-05-21 | 北京四利通控制技术股份有限公司 | A kind of intelligent drilling control cloud platform and intelligent drilling control system |
US10982526B2 (en) * | 2018-05-22 | 2021-04-20 | Baker Hughes, A Ge Company, Llc | Estimation of maximum load amplitudes in drilling systems independent of sensor position |
CN111126735B (en) * | 2018-11-01 | 2022-07-19 | 中国石油化工股份有限公司 | Drilling digital twin system |
CN109377101B (en) * | 2018-11-30 | 2021-09-28 | 西南石油大学 | Well wall stability quantitative evaluation method based on risk control model |
CN109779602B (en) * | 2018-12-12 | 2022-07-15 | 武汉盛华伟业科技股份有限公司 | Intelligent safety risk early warning method and system for drilling engineering |
CN111434886B (en) * | 2019-01-15 | 2021-12-28 | 中国石油化工股份有限公司 | Mechanical drilling speed calculation method and device for drilling process |
CN109577892B (en) * | 2019-01-21 | 2020-12-18 | 西南石油大学 | An intelligent overflow detection system and early warning method based on downhole parameters |
CN111561307A (en) * | 2019-02-14 | 2020-08-21 | 贵州远东兄弟钻探有限公司 | Intelligent control device of full hydraulic drilling machine |
CN110222352B (en) * | 2019-02-18 | 2023-10-24 | 中国石油天然气集团有限公司 | Annulus pressure monitoring method for preventing sand setting stuck drill |
CN111598366A (en) * | 2019-02-20 | 2020-08-28 | 中国石油化工股份有限公司 | Real-time drilling auxiliary decision-making method and system |
CN111677493B (en) * | 2019-03-11 | 2023-06-30 | 中国石油化工股份有限公司 | Drilling data processing method |
CN109869131B (en) | 2019-03-26 | 2022-09-23 | 北京中晟高科能源科技有限公司 | Micro-composite deflecting method and device for directional well |
CN111832197A (en) * | 2019-03-26 | 2020-10-27 | 中石化石油工程技术服务有限公司 | A drilling analysis method |
CN110145501B (en) * | 2019-04-10 | 2020-05-12 | 中国矿业大学 | Method for controlling position and posture of lifting container of double-rope winding type ultra-deep vertical shaft lifting system |
CN109989740B (en) * | 2019-04-10 | 2022-09-23 | 中煤科工集团西安研究院有限公司 | Coal measure stratum drilling intelligent identification system and method based on multi-source information fusion |
CN110147588A (en) * | 2019-04-29 | 2019-08-20 | 中国石油大学(华东) | One kind is based on the shortest three-dimensional Study on Optimum Design of Horizontal Well Path of drilling time |
US11143775B2 (en) | 2019-05-09 | 2021-10-12 | Schlumberger Technology Corporation | Automated offset well analysis |
CN110259433B (en) * | 2019-06-28 | 2023-03-24 | 宝鸡石油机械有限责任公司 | Digital monitoring method for solid drilling machine |
CN110593847B (en) * | 2019-08-22 | 2023-07-28 | 中国地质科学院探矿工艺研究所 | LabVIEW-based intelligent drilling system and application thereof |
CN110567623B (en) * | 2019-08-31 | 2021-03-09 | 中国石油集团川庆钻探工程有限公司 | Method for determining installation position of hydraulic oscillator on drilling tool |
CN110500081B (en) * | 2019-08-31 | 2022-09-16 | 中国石油集团川庆钻探工程有限公司 | Automatic drilling method based on deep learning |
CN110852018B (en) * | 2019-10-21 | 2022-04-05 | 中国石油天然气集团有限公司 | PSO drilling parameter optimization method based on neural network |
CN113062731B (en) * | 2019-12-30 | 2024-05-21 | 中石化石油工程技术服务有限公司 | Intelligent identification method for complex underground drilling conditions |
CN113393334B (en) * | 2020-03-11 | 2024-05-14 | 中国石油化工股份有限公司 | Drilling parameter optimization recommendation method and system |
CA3098212C (en) * | 2020-03-13 | 2024-05-28 | Landmark Graphics Corporation | Early warning and automated detection for lost circulation in wellbore drilling |
CN113530520B (en) * | 2020-04-17 | 2023-09-12 | 中石化石油工程技术服务有限公司 | Drill string bidirectional torsion control system and method |
US11521324B2 (en) | 2020-06-18 | 2022-12-06 | International Business Machines Corporation | Terrain-based automated detection of well pads and their surroundings |
CN111734396B (en) * | 2020-06-22 | 2023-06-20 | 中国石油天然气集团有限公司 | Friction determination method, device and equipment |
CN112302617A (en) * | 2020-08-10 | 2021-02-02 | 陕西鼎晟石油电气控制技术有限公司 | Intelligent drilling machine operation control system |
CN114169652A (en) * | 2020-09-10 | 2022-03-11 | 中国石油化工股份有限公司 | Cloud edge coordination system and method for intelligent drilling |
CN114427430B (en) * | 2020-09-21 | 2024-05-07 | 中国石油化工股份有限公司 | Multi-well real-time collaborative drilling parameter optimization method and system |
CN114320265B (en) * | 2020-09-25 | 2024-12-27 | 中石化石油工程技术服务有限公司 | Early detection method of lost circulation based on downhole engineering parameter measurement |
CN114482982A (en) * | 2020-11-11 | 2022-05-13 | 中国石油化工股份有限公司 | Real drilling mechanical drilling speed monitoring method and system based on big data |
CN112963138A (en) * | 2021-03-17 | 2021-06-15 | 中国建筑第五工程局有限公司 | Automatic drilling monitoring device of drilling machine, drill bit mechanism and drill bit replacement forecasting system |
CN115726759B (en) * | 2021-08-26 | 2024-08-23 | 中国石油化工股份有限公司 | Intelligent analysis decision-making system for controlling well drilling |
CN113669023A (en) * | 2021-08-27 | 2021-11-19 | 四机赛瓦石油钻采设备有限公司 | Automatic control system for coiled tubing |
CN116122789A (en) * | 2021-11-12 | 2023-05-16 | 中油国家油气钻井装备工程技术研究中心有限公司 | Intelligent control system and method for petroleum drilling machine |
CN114215499B (en) * | 2021-11-15 | 2024-01-26 | 西安石油大学 | Well drilling parameter optimization method based on intelligent algorithm |
US11965407B2 (en) | 2021-12-06 | 2024-04-23 | Saudi Arabian Oil Company | Methods and systems for wellbore path planning |
CN114293936B (en) * | 2021-12-07 | 2023-08-25 | 中煤科工集团西安研究院有限公司 | Drilling state monitoring device and monitoring method for drilling machine |
CN114464039B (en) * | 2021-12-24 | 2024-06-25 | 中国海洋石油集团有限公司 | Deep water well shut-in well control multi-post collaborative drilling system and method based on VR technology |
CN114482989B (en) * | 2021-12-30 | 2025-01-28 | 中国石油天然气集团有限公司 | Method and device for identifying drilling overflow conditions |
CN114482885B (en) * | 2022-01-25 | 2024-03-29 | 西南石油大学 | Intelligent control system for pressure-controlled drilling |
CN114718549A (en) * | 2022-04-29 | 2022-07-08 | 中石化江钻石油机械有限公司 | Ground and underground integrated drilling real-time optimization system |
CN115841247B (en) * | 2022-09-30 | 2025-01-28 | 中国石油天然气集团有限公司 | Digital drilling risk monitoring method and device |
CN115680605A (en) * | 2022-10-31 | 2023-02-03 | 中国石油天然气集团有限公司 | Driller navigator and system |
CN116065950B (en) * | 2022-12-08 | 2023-11-03 | 大庆石油管理局有限公司 | Intelligent drilling construction process based on oil gas development engineering driller surface layer construction |
CN116006151B (en) * | 2022-12-27 | 2024-10-29 | 三一能源装备有限公司 | Drilling equipment operation state monitoring and early warning method and system |
CN117386358B (en) * | 2023-12-13 | 2024-02-20 | 甘肃地质灾害防治工程勘查设计院有限公司 | Geological investigation system |
CN118167275B (en) * | 2024-05-16 | 2024-07-19 | 莱州亚通重型装备有限公司 | Method and system for stably rotating mechanical arm of underground coal mine drilling machine |
Family Cites Families (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6612382B2 (en) * | 1996-03-25 | 2003-09-02 | Halliburton Energy Services, Inc. | Iterative drilling simulation process for enhanced economic decision making |
US6282452B1 (en) * | 1998-11-19 | 2001-08-28 | Intelligent Inspection Corporation | Apparatus and method for well management |
US6424919B1 (en) * | 2000-06-26 | 2002-07-23 | Smith International, Inc. | Method for determining preferred drill bit design parameters and drilling parameters using a trained artificial neural network, and methods for training the artificial neural network |
US7054750B2 (en) * | 2004-03-04 | 2006-05-30 | Halliburton Energy Services, Inc. | Method and system to model, measure, recalibrate, and optimize control of the drilling of a borehole |
US7957946B2 (en) * | 2007-06-29 | 2011-06-07 | Schlumberger Technology Corporation | Method of automatically controlling the trajectory of a drilled well |
CN101566059B (en) * | 2009-05-27 | 2014-06-04 | 中曼石油天然气集团股份有限公司 | Wide-adaptive novel automatic driller |
AU2012370482B2 (en) * | 2012-02-24 | 2016-06-30 | Landmark Graphics Corporation | Determining optimal parameters for a downhole operation |
CN102852511B (en) * | 2012-09-28 | 2015-12-09 | 中国科学院自动化研究所 | A kind of intelligent drilling control system and method for oil-well rig |
SG11201502406PA (en) * | 2012-09-28 | 2015-04-29 | Landmark Graphics Corp | Self-guided geosteering assembly and method for optimizing well placement and quality |
EP2971489B1 (en) * | 2013-03-13 | 2019-05-15 | Halliburton Energy Services, Inc. | Monitor and control of directional drilling operations and simulations |
MX369745B (en) * | 2013-03-20 | 2019-11-20 | Schlumberger Technology Bv | Drilling system control. |
-
2015
- 2015-04-30 CN CN201510219785.6A patent/CN104806226B/en not_active Expired - Fee Related
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
RU2779905C1 (en) * | 2019-01-31 | 2022-09-15 | Бейкер Хьюз Оилфилд Оперейшнс Ллк | Optimising an industrial machine |
SE2150278A1 (en) * | 2021-03-11 | 2022-09-12 | Husqvarna Ab | An automatic feed unit for feeding a core drill into a work object |
WO2022191762A1 (en) * | 2021-03-11 | 2022-09-15 | Husqvarna Ab | A feed unit for feeding a core drill bit into a work object |
SE544766C2 (en) * | 2021-03-11 | 2022-11-08 | Husqvarna Ab | An automatic feed unit for feeding a core drill into a work object |
Also Published As
Publication number | Publication date |
---|---|
CN104806226A (en) | 2015-07-29 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN104806226B (en) | intelligent drilling expert system | |
CN111479982B (en) | In-situ operating system with filter | |
EP3494276B1 (en) | Downhole equipment transport control | |
CN106121621A (en) | A kind of intelligent drilling specialist system | |
US7861800B2 (en) | Combining belief networks to generate expected outcomes | |
US20220120176A1 (en) | Adaptive drillstring condition determination | |
US10185306B2 (en) | Utilizing look-up tables representing all models in an automation control architecture to independently handle uncertainties in sensed data in oil and gas well construction | |
CN109779602A (en) | A kind of drilling engineering intelligent safety risk early warning method and system | |
EA028514B1 (en) | System and method for online automation | |
EA023817B1 (en) | System and method for optimizing drilling speed | |
US20230039147A1 (en) | Drilling operations friction framework | |
Saputelli et al. | Real-time decision-making for value creation while drilling | |
CN104834248A (en) | Multi-use data processing circuitry for well monitoring | |
CN117460878A (en) | Drilling control | |
CN116802381A (en) | Drilling machine operation controller | |
US20240026769A1 (en) | Drilling framework | |
US20250075609A1 (en) | Wellbore drill deviation handling | |
CN115298623A (en) | Multi-domain controller | |
CN117627615A (en) | Petroleum workover rig operation monitoring control system | |
US11898441B2 (en) | Method and system for optimizing rig energy efficiency using machine learning | |
Chernyi | The problems of automation technological process of drilling oil and gas wells | |
CN114761665A (en) | Drilling rate drilling operation controller | |
RU2808359C1 (en) | Well drilling process automated control system | |
Al-Saeedi et al. | Fastest Deep Marrat Well in North Kuwait: Case History of Raudhatain 206 | |
CN115059447A (en) | Safety early warning system for drilling operation |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
EXSB | Decision made by sipo to initiate substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
CB03 | Change of inventor or designer information | ||
CB03 | Change of inventor or designer information |
Inventor after: Gou Baihong Inventor after: To build a strong country Inventor before: Gou Baihong Inventor before: Li Lindong Inventor before: Li Guodong Inventor before: To build a strong country |
|
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CF01 | Termination of patent right due to non-payment of annual fee | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20180817 |