CN104806226A - Intelligent drilling expert system - Google Patents

Intelligent drilling expert system Download PDF

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Publication number
CN104806226A
CN104806226A CN201510219785.6A CN201510219785A CN104806226A CN 104806226 A CN104806226 A CN 104806226A CN 201510219785 A CN201510219785 A CN 201510219785A CN 104806226 A CN104806226 A CN 104806226A
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China
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drilling
well
expert
risk
bit
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CN201510219785.6A
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CN104806226B (en
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缑柏弘
李林栋
李国栋
来建强
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北京四利通控制技术股份有限公司
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    • EFIXED CONSTRUCTIONS
    • E21EARTH DRILLING; MINING
    • E21BEARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B44/00Automatic 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

Abstract

The invention provides an intelligent drilling expert system. The intelligent drilling expert system comprises a field sensor detection system, an intelligent expert system and an execution mechanism. An automatic closed-loop drilling regulation and control system is formed. Data in a whole drilling process are acquired through a field sensor; then, the acquired data are transmitted into a computer for processing, monitoring, prediction, analysis, explanation, control and the like. Most importantly, through the researched cross-specialty and cross-industry intelligent expert control models for drilling hydraulic control, well wall stability control, friction resistance and torque control, drilling speed and cost control, drilling complexity and accident control and the like, instantaneity, early discovery, early prediction, interpretation while drilling and an automatic control function can be realized, and accurate information is provided for drilling engineering; moreover, the intelligent drilling expert system has the advantages of reducing the drilling cost, increasing the drilling speed, avoiding sudden accidents and discovering oil and gas accurately.

Description

Intelligent drilling expert system
Technical field
The invention belongs to oil drilling exploration and development technical field, be specifically related to a kind of intelligent drilling expert system.
Background technology
Oil-well rig is made up of Hoisting System, rotary system, the circulating system, power-equipment, auxiliary equipment and rig substructure etc., is a set of huge and complicated drilling well factory, completes petroleum exploration & development task according to drilling engineering and technological requirement.
Control system and supervision instrumentation are the core systems embodying oil-well rig technical level, because rig is machinery, hydraulic pressure, gas control, automatically controlled and geology, chemistry, mathematics, geometry, the mechanics of materials, the multi-specialized multi-disciplinary assembly of hydrodynamics etc., produce in supporting process at rig, same set of rig reaches the supporting of tens producers, each specialized factory is because of professional barrier, famine is multi-disciplinary, inter-trade unified coordination, cause a whole set of drill control technical level not high and repeat supporting and waste, and the Equipment Inspection of identical data different majors goes out different pieces of information, driller cannot know which is correct, more effectively can not instruct and complete drilling well task safely and efficiently with accurate control appliance.
In addition, from actual well drilled process requirements angle, each professional local techniques has reached equal world level all.As the application of AC frequency conversion electric driving control system, winch rotating speed, tourist bus displacement, TORQ, borehole pump discharge capacity and pump pressure etc. accurately can be controlled completely; Drilling instrument can realize measuring multiple parameters and display; The international Advanced Drilling Technologies such as DHM-MWD, rotary steering, geosteering have also been obtained successful Application.
But, no matter be peupendicular hole, inclined shaft, horizontal well or extended reach well, the equipment such as its actuating equipment is all driven by electric control system controls boring winch, rotating disk, top, slush pump and solid controlling throttling well control realize, in practice of construction, same casing programme, due to the difference of driller personal experience, again owing to being disconnect in actual applications substantially between each specialty and technology, the phenomenon causing different drillers can operate out different-effect thus occurs.
Therefore, how from drilling technology requirement, strengthening overall plan design and researchp, the particularly design and researchp of integration, automation, intelligent control system, is key subjects of Petroleum Machinery Manufacturing Industry.
Summary of the invention
For the defect that prior art exists, the invention provides a kind of intelligent drilling expert system, can effectively solve the problem.
The technical solution used in the present invention is as follows:
The invention provides a kind of intelligent drilling expert system, comprising: spot sensor detection system, intelligent expert system and executing agency; The output of described spot sensor detection system is connected with the input of described intelligent expert system; The output of described intelligent expert system is connected with the input of described executing agency, forms automated closed-loop drilling well regulator control system thus;
Wherein, described spot sensor detection system is used for: in drilling well overall process, and Real-time Collection drilling well detects data, and the described drilling well detection data collected are uploaded to described intelligent expert system in real time;
Described intelligent expert system is used for: receive the described drilling well detection data that described spot sensor detection system is uploaded, and in real time intellectual analysis is carried out to described drilling well detection data, with Drilling optimization site technique for target, generate the regulation and control instruction to situ of drilling well equipment, and described regulation and control instruction is issued to corresponding executing agency;
Described executing agency is used for: receive the described regulation and control instruction that described intelligent expert system issues, and sends described regulation and control instruction to the site plant of correspondence, and then controls the duty of this site plant, thus Drilling optimization site technique process.
Preferably, described spot sensor detection system comprises ground transaucer detection system and downhole sensor detection system;
Described ground transaucer detection system comprises winch state sensor detection subsystem, running status sensor detection subsystem, slush pump running status sensor detection subsystem, mud solid phase and volume trend state sensor detection subsystem, the operation monitoring detection subsystem of MCC, well control device detection subsystem, standpipe pressure detection subsystem and annular pressure detection subsystem are driven in automatic bit feed state sensor detection subsystem, rotating disk state sensor detection subsystem, top;
Described downhole sensor detection system comprises drill bit the pressure of the drill detection subsystem, torque-on-bit detection subsystem, drill speed detection subsystem, bottom pressure detection subsystem, bottom hole temperature (BHT) detection subsystem and bit orientation angle detection subsystem.
Preferably, described winch state sensor detection subsystem is for detecting following information: winch running status, alarm condition, tourist bus position, hook weigh and winch motor electric current;
Described automatic bit feed state sensor detection subsystem is for detecting following information: send bore running status, alarm condition, tourist bus position, hook weigh and send brill current of electric;
Described rotating disk state sensor detection subsystem is for detecting following information: the restriction of rotary speed, rotating disk moment, rotary drilling moment of torsion and rotating disk electric current;
Running status sensor detection subsystem is driven for detecting following information in described top: running status, alarm condition, drilling torque, torque wrench moment, Top Drive RPM are driven in top, parameters is driven on top and electric current is driven on top;
Described slush pump running status sensor detection subsystem is for detecting following information: pump punching, pump pressure, discharge capacity and rotating speed;
Described mud solid phase and volume trend state sensor detection subsystem are for detecting following information: the detection of mud entrance density, slurry outlet density, mud entrance viscosity, slurry outlet viscosity, mud entrance flow, mud-outlet-flow, mud entrance temperature, slurry outlet temperature, oil-containing gassiness, mud pit liquid level, mud pit cumulative volume, mud entrance electrical conductivity and slurry outlet electrical conductivity;
The operation monitoring detection subsystem of described MCC is for detecting following information: Hydraulic Station pump, winch hydraulic power source, mixer, charge pump, replenishment pump, increase the weight of pump, mixing pump, deaerator, vibrosieve, centrifugal pump and shear pump.
Preferably, described intelligent expert system comprises: the information-based man-machine interface of input block, expert model, output unit and overall process;
Described input block is used for described expert model input basic data source; Described basic data source comprises the well bore project data relevant to situ of drilling well, geological environment data, device attribute data, offset well data and described spot sensor detection system Real-time Collection and the described drilling well of uploading detects data;
Described expert model comprises wellbore hydraulics computation model, well drilling pipe column mechanics model, hole stability analytical model, drilling well risk profile and diagnostic model, borehole track computation model, rate of penetration and cost forecast model and with brill prediction of formation pressure model;
Wherein, described wellbore hydraulics computation model is used for: read the described basic data source that described input block inputs, and obtains borehole pump characteristic, drill column structure, drill bit classification, property of drilling fluid, drilling fluid in pipe and the flow regime of annular space; And calculate and creep into circular flow dynamic pressure consumption in real time, in conjunction with hydrodynamics basic theories, optimize and determine drilling well waterpower target component, and then generate control instruction to drilling process;
Described well drilling pipe column mechanics model is used for: read the current actual drilling assembly data that described input block inputs, obtain the drilling tool attribute in the actual well drilling pipe column used, comprise drilling tool classification, drilling tool length, drilling tool internal diameter external diameter value, the antitorque value of drilling tool tension, drilling tool service life and drilling tool class information; And calculate the Mechanical Data of this drilling tool when creeping in real time, optimize and determine drilling machinery limit restraint Protection parameters, and then generate the control instruction to drilling process;
Described hole stability analytical model is used for: read the derived data source that described input block inputs, obtain the real-time pressure equilibrium response of drilling fluid on annular space stratum, automatic calculating identification formation lithology, and the drillability stability feature of Different Strata and lithology, optimizing drilling mud and hydraulic parameters;
Described drilling well risk profile and diagnostic model are used for: pre-established various drilling well risk model; The described basic data source inputted by described input block is input to described drilling well risk model, runs described drilling well risk model, carries out risk profile to on-the-spot drilling condition; Wherein, described drilling well risk model comprises: well kick risk model, blowout risk model, leakage risk model and bit freezing risk model;
The running of described well kick risk model, described blowout risk model and described leakage risk model is: by described basic data source, obtain gas detect, data and the situation of change of drilling liquid parameter when boring, by analyzing the situation of change of data when described gas detect, brill and drilling liquid parameter, prediction relevant risk;
Described bit freezing risk model comprises: the sub-risk model of pressure reduction sticky suction bit freezing, the sub-risk model of sand setting bit freezing, the sub-risk model of the bit freezing that caves in, the sub-risk model of well wall dropping block bit freezing, turn on pump suppress the sub-risk model of junk bit freezing in the sub-risk model of Lou bit freezing, well, 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;
Described borehole track computation model is used for: obtain real stratigraphic section complete data, comprises formation lithology and density, reservoir characteristics and reference lamina, pneumatic jack, oil reservoir, interlayer, oily bed rock and the degree of depth, the formation fluid degree of depth and fluid pressure, fluid properties, real real-time working condition, the downhole drill dynamic operation condition boring three-dimensional wellbore trajectory, drill string and each lens and drill bit; Comprehensively analyze and integration described stratigraphic section complete data, interpretation process draws the technical data and decision-making for the treatment of that drilling well section is optimized, and compares with planned well path structure geology and engineering model moment, generates the control instruction to drilling process;
Described rate of penetration and cost forecast model are used for: predict real-time the pressure of the drill, and are optimized the pressure of the drill rotating speed; Be specially: the mathematic(al) mode of association drilling process basic law and set optimization aim, set up and creep into object function; On this basis, use Artificial Intelligence Controlling Theory and various linear, nonlinear programming approach, when determining various constraints, every drilling parameter of optimization object function, and then generate the control instruction to drilling process;
Describedly to be used for brill prediction of formation pressure model: bottom pressure to be controlled and mud line manifold system automation controls; Be specially: real-time analysis calculates at termination of pumping, the bottom pressure stopped under brill, trip-out, lower brill, drilling condition, use bottom pressure balance theory, set up bottom pressure Controlling model, thus optimizing drilling and the speed that makes a trip, and then generate control instruction to drilling process, control well is invaded, overflow, well kick, blowout, leakage, well slough drilling failure generation;
The control instruction to drilling process that described output unit generates for exporting described expert model; Also for output alarm information, casing programme, drilling assembly, ground environment, borehole track, mechanical parameter, hydraulic parameters, mud solid phase information;
The information-based man-machine interface of described overall process is used for: the real-time Simulation picture of display drilling process and various drilling well indicating risk.
Preferably, described intelligent expert system also comprises: bore front simulation module and sum up back after boring and look into module;
The running of described brill front simulation module comprises the following steps:
S1, reads the basic data relevant to situ of drilling well, comprising: well bore project data, geological environment data, device attribute data and offset well data;
S2, based on described basic data, simulation creates virtual emulation well;
S3, described virtual emulation well loads pending drilling technology parameter, and choose a kind of expert model of pre-stored, is run by described drilling technology parameters input, obtain the 1st drilling well appraisal report to described expert model; Wherein, described 1st drilling well appraisal report carries out scientific evaluation for the engineering design to this virtual emulation well, complexity and risk, cost and efficiency;
S4, based on described 1st drilling well appraisal report, adjusts described drilling technology parameter, and S3 is carried out in circulation, and continuous being optimized described drilling technology parameter changes well thus, obtains best drilling technology parameter; Described drilling technology parameter carries out guide field drilling process;
Described brill is summed up back the running looking into module afterwards and is comprised the following steps:
S5, after drilling engineering terminates, the described virtual emulation well corresponding with situ of drilling well imports offset well complete report to selected expert model, and to the history real-time detector data in described expert model input drilling well all-work, after described expert model runs, obtain the 2nd drilling well appraisal report, and described 2nd drilling well appraisal report is stored as offset well supplemental characteristic.
Preferably, the site plant that described executing agency drives comprises: Hoisting System, rotary system, the circulating system, power-equipment, drive apparatus, control system and auxiliary equipment.
Preferably, described Hoisting System comprises winch, automatic bit feed winch, traveling system, rig tool, bank head machinery equipment;
Described rotary system comprises rotating disk, water tap and top drive;
The described circulating system comprises borehole pump, high pressure pipe joint, circulating tank, solid control equipment, Deal With Drilling Fluid and storage facilities;
Described power-equipment comprises diesel generating set, Gas Generator Set and motor;
Described drive apparatus comprises deceleration and car, transfer, turn to, reverse, speed change, bending moment machine driving, hydraulic power, hydraulic drive and electric-driving installation;
Described control system comprises SCR/VFD electric driving control system, hydraulic control system and pneumatic control system;
Described auxiliary equipment comprises air feed supply equipment, auxiliary generating plant, hoisting aids equipment and blowout control equipment.
Preferably, described rate of penetration and cost forecast model, specifically for:
Step 10: the parameter optimization multiple objective function that structure PDC drill bit creeps into;
This step is specially: according to drilling speed object function and every meter of drilling cost object function, utilizes binary objective optimization theoretical, the parameter optimization multiple objective function be constructed as follows:
maxF(x)=α 1f 1(x)-α 2f 2(x)
Wherein:
F (x): object function;
F 1x () is rate of penetration target, unit is m/h;
F 2(x) for every meter of drilling cost target, unit be unit/m;
α 1, α 2for the weight of each object function of reflection policymaker wish,
Step 20: the constraints determining described parameter optimization multiple objective function, sets up PDC drill bit according to described constraints and creeps into optimization model;
Wherein, described constraints comprises: the pressure of the drill constraints, rotating speed constraints, bit wear constraints, drilling depth constraints, rate of penetration constraints and drilling cost constraints;
Step 30: according to creeping into real-time detected parameters, real-time regression Duffing equation parameter;
Step 40: creep into optimization model according to described PDC drill bit, determine the drilling parameter of PDC drill bit, realize the multiple-objection optimization of drilling parameter.
Preferably, step 10 is specially:
The parameter optimization multiple objective function set up is the object function of a broad sense; Work as weight α 1=0, α 2when=1, object function becomes every meter of drilling cost least model; Work as weight α 1=1, α 2when=0, object function becomes the maximum model of rate of penetration;
PDC drill bit equation for drilling rate and wear equation are the bases that PDC drill bit creeps into parameter objectives optimization; That is: the equation that following PDC bit drills fast mode and quaternary ROP mode combine is set up:
v pe = k W s a 1 N b 1 D c 1 e d 1 Δp
In formula:
V pefor drilling speed, m/h;
K is the coefficient of colligation relevant to drillability of rock factor;
W sfor than the pressure of the drill, kN/cm;
N is drill speed, r/min;
D be drill bit than water-horse power, W/cm2;
E is the truth of a matter of natural logrithm, is an irrational number, approximates 2.71828;
Δ p is bottom hole pressure difference, Mpa;
A 1, b 1, c 1, d 1be respectively the pressure of the drill, rotating speed, water-horse power and the bottom hole pressure difference influence coefficient to drilling speed;
Suppose what bit breakage mainly occurred with bit wear form, then according to bit life equation and linear cumulative damage law, the bit wear equation that can obtain a certain interval or a certain microbedding section is:
dh f dt = 1 a φ b W c e dN
In formula:
φ is internal friction angle of rock, degree;
T is the rotating hours, h;
H ffor bit wear amount, dimensionless;
Parameter a, b, c, d are bit life equation coefficient;
N is rotating speed to be optimized, rpm;
W is the pressure of the drill to be optimized, kN;
By integration, drill bit total drilling time t can be obtained ffor:
t f=aφ bW ce dN(h f2-h f1)
H f2, h f1be respectively the bit wear amount of current time and previous moment, dimensionless;
Total footage H ffor:
H f = k W s a 1 N b 1 D c 1 e d 1 Δp a φ b W c e dH ( h f 2 - h f 1 )
Drilling speed is:
v pe = k W s a 1 N b 1 D c 1 e d 1 Δp
Every meter of drilling cost is:
C pm = C b + C r [ a φ b W c e dN ( h f 2 - h f 1 ) + t t ] k W s a 1 N b 1 D c 1 e d 1 Δp a φ b W c e dN ( h f 2 - h f 1 )
In formula:
C pmfor every meter of drilling cost, unit/m;
C bfor drill bit cost, unit/only;
C rfor rig operating cost;
T tfor making a trip, making up a joint the time;
Multiple objective function expression formula is:
max F ( W , N , h f 2 ) = α 1 V pe - α 2 C pm = α 1 k W s a 1 N b 1 D c 1 e d 1 Δp - α 2 C b + C r [ a φ b W c e dN ( h f 2 - h f 1 ) + t t ] k W s a 1 N b 1 D c 1 e d 1 Δp a φ b W c e dN ( h f 2 - h f 1 )
Determine constraints, drilling parameter is analysed in depth, return the parameter calculated in above-mentioned equation, solve PDC drill bit drilling parameter Model for Multi-Objective Optimization according to the simulated annealing genetic algorithm based on real coding of application enhancements, determine PDC drill bit drilling parameter on the whole.
Preferably, described drilling parameter to be analysed in depth, returns the parameter calculated in above-mentioned equation, solve PDC drill bit drilling parameter Model for Multi-Objective Optimization according to the simulated annealing genetic algorithm based on real coding of application enhancements, be specially:
Step1: evolutionary generation counter initialization: t ← 0.
Step2: random generation initial population P (t);
Step3: the adaptive value evaluating population P (t);
Step4: individual interlace operation: P ' (t) ← Crossover [P (t)];
Step5: individual variation operates: P " and (t) ← Mutation [P ' (t)];
Step6: individual simulated annealing operation: P " ' and (t) ← SimulatedAnnealing [P " (t)];
' adaptive value of (t) Step7: evaluation population P ";
Step8; Individual choice, copy operation: P (t+1) ← Reproduction [P (t) U P " ' (t)];
Step9: end condition judges;
If do not meet end condition, then: t ← t+1, forward Step4 to, continue evolutionary process: if meet end condition, then: export current optimum individual, algorithm terminates.
Beneficial effect of the present invention is as follows:
Intelligent drilling expert system provided by the invention has the following advantages:
Intelligent drilling expert system provided by the invention, pass through spot sensor, gather the data of whole drilling process, comprise all equipment relevant with drilling well and the drilling engineering data of ground and down-hole, as: the state of hoisting system of rig, rotary system, the circulating system, power-equipment, auxiliary equipment etc. and service data; Then, the data collected are sent into computer and carry out process monitoring, forecast, analysis, explanation, control etc.Wherein, the most important thing is, multi-disciplinary, inter-trade intelligence and the Multimode Control models such as the wellbore hydraulics control 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, real-time can be realized, early find, early forecast, with explanation and automatic control function, not only for drilling engineering provides information accurately, and, system can make quick decision automatically, have reduce drilling cost, improve bit speed, stop contingency, the advantage of accurate oil and gas discovery.
Accompanying drawing explanation
Fig. 1 is the structural representation of intelligent drilling expert system provided by the invention;
Fig. 2 is flow chart PDC drill bit being crept into parameter optimization provided by the invention.
Detailed description of the invention
Below in conjunction with accompanying drawing, the present invention is described in detail:
As shown in Figure 1, the invention provides a kind of intelligent drilling expert system, make drilling technology to information-based, intellectuality, remote automation future development and stride forward.Under the pressure of low oil price market, controlling drilling well risk, Drilling optimization parameter, reduce drilling cost, improve drilling efficiency, is oil drill equipment update technology, has filled up industry blank.For oil drilling, company improves the market competitiveness greatly, simultaneously also for petroleum exploration in China exploitation and energy security have made contribution.
Concrete, comprising: spot sensor detection system, intelligent expert system and executing agency; The output of described spot sensor detection system is connected with the input of described intelligent expert system; The output of described intelligent expert system is connected with the input of described executing agency, forms automated closed-loop drilling well regulator control system thus.Below above-mentioned part is introduced in detail:
(1) spot sensor detection system
Wherein, described spot sensor detection system is used for: in drilling well overall process, and Real-time Collection drilling well detects data, and the described drilling well detection data collected are uploaded to described intelligent expert system in real time;
In the present invention, spot sensor detection system comprises ground transaucer detection system and downhole sensor detection system;
(1) ground transaucer detection system
Described ground transaucer detection system comprises winch state sensor detection subsystem, running status sensor detection subsystem, slush pump running status sensor detection subsystem, mud solid phase and volume trend state sensor detection subsystem, the operation monitoring detection subsystem of MCC, well control device detection subsystem, standpipe pressure detection subsystem and annular pressure detection subsystem are driven in automatic bit feed state sensor detection subsystem, rotating disk state sensor detection subsystem, top;
Described winch state sensor detection subsystem is for detecting following information: winch running status, alarm condition, tourist bus position, hook weigh and winch motor electric current;
Described automatic bit feed state sensor detection subsystem is for detecting following information: send bore running status, alarm condition, tourist bus position, hook weigh and send brill current of electric;
Described rotating disk state sensor detection subsystem is for detecting following information: the restriction of rotary speed, rotating disk moment, rotary drilling moment of torsion and rotating disk electric current;
Running status sensor detection subsystem is driven for detecting following information in described top: running status, alarm condition, drilling torque, torque wrench moment, Top Drive RPM are driven in top, parameters is driven on top and electric current is driven on top;
Described slush pump running status sensor detection subsystem is for detecting following information: pump punching, pump pressure, discharge capacity and rotating speed;
Described mud solid phase and volume trend state sensor detection subsystem are for detecting following information: the detection of mud entrance density, slurry outlet density, mud entrance viscosity, slurry outlet viscosity, mud entrance flow, mud-outlet-flow, mud entrance temperature, slurry outlet temperature, oil-containing gassiness, mud pit liquid level, mud pit cumulative volume, mud entrance electrical conductivity and slurry outlet electrical conductivity;
The operation monitoring detection subsystem of described MCC is for detecting following information: Hydraulic Station pump, winch hydraulic power source, mixer, charge pump, replenishment pump, increase the weight of pump, mixing pump, deaerator, vibrosieve, centrifugal pump and shear pump.
(2) downhole sensor detection system
Described downhole sensor detection system comprises drill bit the pressure of the drill detection subsystem, torque-on-bit detection subsystem, drill speed detection subsystem, bottom pressure detection subsystem, bottom hole temperature (BHT) detection subsystem and bit orientation angle detection subsystem.
(2) intelligent expert system
Described intelligent expert system is used for: receive the described drilling well detection data that described spot sensor detection system is uploaded, and in real time intellectual analysis is carried out to described drilling well detection data, with Drilling optimization site technique for target, generate the regulation and control instruction to situ of drilling well equipment, and described regulation and control instruction is issued to corresponding executing agency;
Described intelligent expert system comprises: the information-based man-machine interface of input block, expert model, output unit and overall process;
Described input block is used for described expert model input basic data source; Described basic data source comprises the well bore project data relevant to situ of drilling well, geological environment data, device attribute data, offset well data and described spot sensor detection system Real-time Collection and the described drilling well of uploading detects data;
Described expert model comprises wellbore hydraulics computation model, well drilling pipe column mechanics model, hole stability analytical model, drilling well risk profile and diagnostic model, borehole track computation model, rate of penetration and cost forecast model and with brill prediction of formation pressure model;
Wherein, described wellbore hydraulics computation model is used for: read the described basic data source that described input block inputs, and obtains borehole pump characteristic, drill column structure, drill bit classification, property of drilling fluid, drilling fluid in pipe and the flow regime of annular space; And calculate and creep into circular flow dynamic pressure consumption in real time, in conjunction with hydrodynamics basic theories, optimize and determine drilling well waterpower target component, and then generate control instruction to drilling process;
Described well drilling pipe column mechanics model is used for: read the current actual drilling assembly data that described input block inputs, obtain the drilling tool attribute in the actual well drilling pipe column used, comprise drilling tool classification, drilling tool length, drilling tool internal diameter external diameter value, the antitorque value of drilling tool tension, drilling tool service life and drilling tool class information; And calculate the Mechanical Data of this drilling tool when creeping in real time, optimize and determine drilling machinery limit restraint Protection parameters, and then generate the control instruction to drilling process;
Described hole stability analytical model is used for: read the derived data source that described input block inputs, obtain the real-time pressure equilibrium response of drilling fluid on annular space stratum, automatic calculating identification formation lithology, and the drillability stability feature of Different Strata and lithology, optimizing drilling mud and hydraulic parameters;
Described drilling well risk profile and diagnostic model are used for: pre-established various drilling well risk model; The described basic data source inputted by described input block is input to described drilling well risk model, runs described drilling well risk model, carries out risk profile to on-the-spot drilling condition; Wherein, described drilling well risk model comprises: well kick risk model, blowout risk model, leakage risk model and bit freezing risk model;
Concrete, the present invention analyzes various drilling well Risk Modeling, analyzes generation drilling failure and complicated reason, the scheme making reply engineering abnormal accident and complexity, accomplishes early discovery, prevention morning, early process.System model contains all drilling failures and complexity.Comprise the bit freezing etc. of well kick, blowout, leakage and various character.Well kick, blowout, leakage all belong to head of liquid and can not cause by equilibrium strata pressure, in conjunction with gas detect display, and data during brill, the source of the change judgement of drilling liquid parameter, and reliable kill-job density can be provided according to kill-job supervisor.Bit freezing comprises that pressure reduction sticky inhales bit freezing, sand setting bit freezing, the bit freezing that caves in, well wall dropping block bit freezing, turn on pump suppress Lou junk bit freezing, undergauge bit freezing, key-seating sticking, balling-up sticking in bit freezing, well.
The running of described well kick risk model, described blowout risk model and described leakage risk model is: by described basic data source, obtain gas detect, data and the situation of change of drilling liquid parameter when boring, by analyzing the situation of change of data when described gas detect, brill and drilling liquid parameter, prediction relevant risk;
Described bit freezing risk model comprises: the sub-risk model of pressure reduction sticky suction bit freezing, the sub-risk model of sand setting bit freezing, the sub-risk model of the bit freezing that caves in, the sub-risk model of well wall dropping block bit freezing, turn on pump suppress the sub-risk model of junk bit freezing in the sub-risk model of Lou bit freezing, well, 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;
Described borehole track computation model is used for: obtain real stratigraphic section complete data, comprises formation lithology and density, reservoir characteristics and reference lamina, pneumatic jack, oil reservoir, interlayer, oily bed rock and the degree of depth, the formation fluid degree of depth and fluid pressure, fluid properties, real real-time working condition, the downhole drill dynamic operation condition boring three-dimensional wellbore trajectory, drill string and each lens and drill bit; Comprehensively analyze and integration described stratigraphic section complete data, interpretation process draws the technical data and decision-making for the treatment of that drilling well section is optimized, and compares with planned well path structure geology and engineering model moment, generates the control instruction to drilling process;
Described rate of penetration and cost forecast model are used for: predict real-time the pressure of the drill, and are optimized the pressure of the drill rotating speed; Be specially: the mathematic(al) mode of association drilling process basic law and set optimization aim, set up and creep into object function; On this basis, use Artificial Intelligence Controlling Theory and various linear, nonlinear programming approach, when determining various constraints, every drilling parameter of optimization object function, and then generate the control instruction to drilling process;
Describedly to be used for brill prediction of formation pressure model: bottom pressure to be controlled and mud line manifold system automation controls; Be specially: real-time analysis calculates at termination of pumping, the bottom pressure stopped under brill, trip-out, lower brill, drilling condition, use bottom pressure balance theory, set up bottom pressure Controlling model, thus optimizing drilling and the speed that makes a trip, and then generate control instruction to drilling process, control well is invaded, overflow, well kick, blowout, leakage, well slough drilling failure generation;
The control instruction to drilling process that described output unit generates for exporting described expert model; Also for output alarm information, casing programme, drilling assembly, ground environment, borehole track, mechanical parameter, hydraulic parameters, mud solid phase information;
The information-based man-machine interface of described overall process is used for: the real-time Simulation picture of display drilling process and various drilling well indicating risk.
Described intelligent expert system also comprises: bore front simulation module and sum up back after boring and look into module;
The running of described brill front simulation module comprises the following steps:
S1, reads the basic data relevant to situ of drilling well, comprising: well bore project data, geological environment data, device attribute data and offset well data;
S2, based on described basic data, simulation creates virtual emulation well;
S3, described virtual emulation well loads pending drilling technology parameter, and choose a kind of expert model of pre-stored, is run by described drilling technology parameters input, obtain the 1st drilling well appraisal report to described expert model; Wherein, described 1st drilling well appraisal report carries out scientific evaluation for the engineering design to this virtual emulation well, complexity and risk, cost and efficiency;
S4, based on described 1st drilling well appraisal report, adjusts described drilling technology parameter, and S3 is carried out in circulation, and continuous being optimized described drilling technology parameter changes well thus, obtains best drilling technology parameter; Described drilling technology parameter carries out guide field drilling process;
Described brill is summed up back the running looking into module afterwards and is comprised the following steps:
S5, after drilling engineering terminates, the described virtual emulation well corresponding with situ of drilling well imports offset well complete report to selected expert model, and to the history real-time detector data in described expert model input drilling well all-work, after described expert model runs, obtain the 2nd drilling well appraisal report, and described 2nd drilling well appraisal report is stored as offset well supplemental characteristic.
That is, drilling well overall process function monitoring controls to be divided into: bore front simulation, bore in monitor in real time, bore rear summary and return and look into function.
Bore front simulation and can beat a bite well according to the input digital simulation except in real time detection, scientific evaluation is carried out to the engineering design of this mouthful of well, complexity and risk, cost and efficiency etc.; Sum up back after boring and look into, for summing up further, analyzing the various historical data of drilling well overall process, generating report forms, stored in database, can be used as offset well data provides more optimization data to instruct to drilling well in future; In brill, monitoring is in real time patent core of the present invention, primarily of wellbore hydraulics calculating, well drilling pipe column calculating, hole stability analysis, drilling well risk profile and diagnosis, borehole track calculating, rate of penetration and forecasting of cost, has supported with software models such as brill strata pressures.Each control and functional mode strictly require to set out with site technique, integrate pure science and the professional technique of different industries and specialty, comprise on-the-spot optimized operational procedure and experience, carry out scientific and reasonable analysis and calculation, take precautions against completely and stopped various artificial erroneous judgement, maloperation, given arbitrary and impracticable direction, the problem such as excessive risk, poor efficiency.
Therefore, by intelligent drilling expert system, by casing programme, drilling assembly, ground environment, borehole track (mark), take the informationization of rock situation etc., the real-time treatment and analyses of drilling parameter, realizes the visualizing monitor to drilling process, risk automatic control alarm, optimized drilling instruction output etc.
(3) executing agency
Described executing agency is used for: receive the described regulation and control instruction that described intelligent expert system issues, and sends described regulation and control instruction to the site plant of correspondence, and then controls the duty of this site plant, thus Drilling optimization site technique process.
The site plant that executing agency drives comprises: Hoisting System, rotary system, the circulating system, power-equipment, drive apparatus, control system and auxiliary equipment.
Wherein, described Hoisting System comprises winch, automatic bit feed winch, traveling system, rig tool, bank head machinery equipment;
Described rotary system comprises rotating disk, water tap and top drive;
The described circulating system comprises borehole pump, high pressure pipe joint, circulating tank, solid control equipment, Deal With Drilling Fluid and storage facilities;
Described power-equipment comprises diesel generating set, Gas Generator Set and motor;
Described drive apparatus comprises deceleration and car, transfer, turn to, reverse, speed change, bending moment machine driving, hydraulic power, hydraulic drive and electric-driving installation;
Described control system comprises SCR/VFD electric driving control system, hydraulic control system and pneumatic control system;
Described auxiliary equipment comprises air feed supply equipment, auxiliary generating plant, hoisting aids equipment and blowout control equipment.
In addition, for different bite type conventional in current actual well drilled process, in conjunction with the service condition of on-the-spot drilling equipment, propose different forecast models and the rate of penetration in drilling process and cost are predicted.Below for PDC drill bit, introduce a kind of concrete forecast model of rate of penetration and cost forecast model.
As shown in Figure 2, for one is by creeping into parameter optimization to PDC drill bit, to the flow chart that rate of penetration and cost are predicted.
Step 1: structure PDC drill bit creeps into the multiple objective function of parameter optimization
Step 2: the constraints determining parameter optimization multiple objective function, sets up PDC drill bit according to constraints and creeps into optimization model.
Step 3: according to creeping into real-time detected parameters, real-time regression Duffing equation parameter.
Step 4: the drilling parameter determining PDC drill bit according to above-mentioned Optimized model, realizes the multiple-objection optimization of drilling parameter.
Wherein step 1 comprises further:
According to drilling well actual conditions, based on two object functions: drilling speed and every meter of drilling cost, utilize the object function that binary objective optimization theory building drilling parameter is optimized.The object function of described drilling parameter optimization is:
maxF(x)=α 1f 1(x)-α 2f 2(x)
Wherein:
F 1x () is rate of penetration target, unit is m/h;
F 2(x) for every meter of drilling cost target, unit be unit/m;
α 1, α 2for the weight of each object function of reflection policymaker wish,
In step 2, described constraints comprises: the pressure of the drill constraints, rotating speed constraints, bit wear constraints, drilling depth constraints, rate of penetration constraints and drilling cost constraints.
What deserves to be explained is, the drilling parameter multi-goal optimizing function set up is the object function of a broad sense.Work as weight α 1=0, α 2when=1, object function just becomes every meter of drilling cost least model; Work as weight α 1=1, α 2when=0, object function just becomes the maximum model of rate of penetration.
PDC drill bit equation for drilling rate and wear equation are the bases that PDC drill bit creeps into parameter objectives optimization.The method that combines of PDC bit drills fast mode and quaternary ROP mode innovatively in this enforcement, learns from other's strong points to offset one's weaknesses, is beneficial to and calculates and optimize.
v pe = k W s a 1 N b 1 D c 1 e d 1 Δp
In formula:
V is drilling speed, m/h;
K is the coefficient of colligation relevant to factors such as the drillabilities of rock;
W sfor than the pressure of the drill, kN/cm;
N is drill speed, r/min;
D be drill bit than water-horse power, W/cm2;
Δ p is bottom hole pressure difference, Mpa;
A 1, b 1, c 1, d 1be respectively the pressure of the drill, rotating speed, water-horse power and the bottom hole pressure difference influence coefficient to drilling speed.
Suppose what bit breakage mainly occurred with the form of bit wear, then according to bit life equation and linear cumulative damage law, the bit wear equation that can obtain a certain interval or a certain microbedding section (full well section is divided into multiple interval or multiple microbedding section by base area layer parameter principle of invariance) is:
dh f dt = 1 a φ b W c e dN
In formula:
φ is internal friction angle of rock, degree;
T is the rotating hours, h;
H ffor bit wear amount, dimensionless;
Parameter a, b, c, d are bit life equation coefficient;
N is rotating speed to be optimized, rpm;
W is the pressure of the drill to be optimized, kN.
By integration, drill bit total drilling time t can be obtained ffor:
t f=aφ bW ce dN(h f2-h f1)
Total footage H ffor:
H f = k W s a 1 N b 1 D c 1 e d 1 Δp a φ b W c e dN ( h f 2 - h f 1 )
Drilling speed is:
v pe = k W s a 1 N b 1 D c 1 e d 1 Δp
Every meter of drilling cost is:
C pm = C b + C r [ a φ b W c e dN ( h f 2 - h f 1 ) + t t ] k W s a 1 N b 1 D c 1 e d 1 Δp a φ b W c e dN ( h f 2 - h f 1 )
In formula:
C pmfor every meter of drilling cost, unit/m;
C bfor drill bit cost, unit/only;
C rfor rig operating cost;
T tfor making a trip, making up a joint the time.
Multiple objective function expression formula is:
max F ( W , N , h f 2 ) = α 1 V pe - α 2 C pm = α 1 k W s a 1 N b 1 D c 1 e d 1 Δp - α 2 C b + C r [ a φ b W c e dN ( h f 2 - h f 1 ) + t t ] k W s a 1 N b 1 D c 1 e d 1 Δp a φ b W c e dN ( h f 2 - h f 1 )
Determine constraints, drilling parameter is analysed in depth, return the parameter calculated in above-mentioned equation, PDC drill bit drilling parameter Model for Multi-Objective Optimization is solved according to the simulated annealing genetic algorithm based on real coding of application enhancements, solve the problem that genetic algorithm local search ability is poor, thus determine PDC drill bit drilling parameter on the whole.
This method to realize thought as follows:
Step1: evolutionary generation counter initialization: t ← 0.
Step2: random generation initial population P (t).
Step3: the adaptive value evaluating population P (t).
Step4: individual interlace operation: P ' (t) ← Crossover [P (t)].
Step5: individual variation operates: P " and (t) ← Mutation [P ' (t)].
Step6: individual simulated annealing operation: P " ' and (t) ← SimulatedAnnealing [P " (t)].
' adaptive value of (t) Step7: evaluation population P ".
Step8; Individual choice, copy operation: P (t+1) ← Reproduction [P (t) U P " ' (t)].
Step9: end condition judges.
If do not meet end condition, then: t ← t+1, forward Step4 to, continue evolutionary process: if meet end condition, then: export current optimum individual, algorithm terminates.
In specific implementation, intelligent drilling expert system provided by the invention, is made up of system hardware and software.
(1) hardware implementation explanation
Comprise: 1GPS/3G antenna, 2 displays, 3 explosion-resistant enclosures, 4 power supplys, 5 system master making sheet and the composition such as spot sensor, CAN acquisition module.Overall integrated explosion-proof design, is installed in driller control house of drilling rig.If drilling engineer, head pusher, platform manager host computer display terminal, be connected with main frame by communication cable, information real-time synchronization.According to different post setting corresponding authority.
(2), software systems implementation
Systems soft ware is made up of boundary layer, kernel service layer, Data support, driving application layer etc.
Wherein, boundary layer mainly completes people's machine information and control instruction interactive function.Be made up of the main interface of driller, drilling engineer interface, platform manager interface, head pusher interface, Facilities Engineer interface etc.Wherein, the main interface of driller and system host integrated design, by HDMA interface driver, other interface portion is deployed on PC, realizes long-range real-time, interactive by network and main frame.
By boundary layer, make system first achieve Complete Information to drilling well overall process, comprise real-time drilling mechanical parameter, hydraulic parameters, actual efficiency data, geography information, formation information, safe condition etc.Meanwhile, achieving drilling well overall process with target is the large closed-loop control led.
Kernel service layer mainly with various software model for support.Adopt intelligent algorithm, drilling machinery parameter, hydraulic parameters, accident and complexity, the borehole wall and frictional resistance etc. modeling are programmed.Form primarily of following subitem: the real-time Treatment Analysis module of bottom schedule driven module, well data, drilling well diagnosis of risk prediction module, rate of penetration inspection optimization module, hydraulic parameters inspection optimization module, drag and torque computing module, with brill formation pressure calculation module etc.
Data support is calculated data etc. formed by device attribute data, drilling tool data, drilling engineering data, historical empirical data, Real-time Monitoring Data, derivation.Adopt database technology, high efficient and flexible is called, store and return and look into.
System data layer adopts Mysql5.1 database, stable after transplanting and optimizing, and speed is fast, and cross-platform performance is good.
Following a few class is divided into by systemic-function and service logic:
(1) real-time data collection table (SSCJinfo), stored in whole image data;
(2) derivation amount information table (PSinfo) is calculated, stored in the derivation amount directly calculated;
(3) algorithm derives from scale (SFPSLinfo), stored in the derivation amount calculated from algorithm;
(4) Optimal Parameters information table (YHCSinfo), stored in the Optimal Parameters that algorithm calculates in brill;
(5) relevant to accident data, accident critical parameter table (STPDinfo), real-time data collection, derivation amount, the threshold values that all accidents judge deposited by this table; Accident record table (SGJLinfo) etc.;
(6) other unit needs the data that store, comprises the parameter of optimization before drilling, equipment running status, driller's operation information, warning message, down-hole pressure prompting, system prompt performs record, accident and complex situations parameter and process;
(7) deposit every data of design and produce corresponding every real data in running.
In recent years, due to the general operation of ultradeep well, extended reach well and horizontal well, drilling prospection development difficulty is continued to increase, oil drilling exploration and development Frequent Accidents all over the world, severe and great casualty is as Mexico of BP company of Britain offshore oil drilling platform accident, and CNPC's Chongqing Kaixian gas blowout accident etc. causes the heavy economic losses such as equipment scrapping, casualties, environment pollution and serious social influence.In addition, small-sized accident such as well kick, leakage, bit freezing etc. almost occur everyday.According to non-public data incomplete statistics, the direct economic loss that domestic each oil drilling company processes small-sized production accident is every year millions of at least, and at most several ten million.
The application of intelligent drilling expert system provided by the invention, the man's activity such as drilling well overall process can be made to have stopped maloperation, erroneous judgement completely, give arbitrary and impracticable direction, makes completing of drilling engineering safety and efficiently.Simultaneously, the multistage industrial network of the drilling engineering data composition field level of situ of drilling well, for engineers and technicians, head pusher personnel, drilling team administrative staff and higher level responsible person understand drilling well overall process information, participate at any time and collaborative work, and be sent to corporate HQ with being drilled with meter borehole track by Internet or wireless network real time remote, adjustment drilling method, determine completion strategy etc., expert consultation decision instruction suggestion is proposed, feed back to drilling crew, realize the construction of real-time optimized drilling, drilling well and reservoir geology personnel " perspective " stratigraphic map also can be made as the positive drilling well of real-time oversight and the well track treating drilling well, and directly control drilling equipment accurate hit layer target spot, for oil and gas discovery provides the technical guarantee of update.
The above is only the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention; can also make some improvements and modifications, these improvements and modifications also should look protection scope of the present invention.

Claims (10)

1. an intelligent drilling expert system, is characterized in that, comprising: spot sensor detection system, intelligent expert system and executing agency; The output of described spot sensor detection system is connected with the input of described intelligent expert system; The output of described intelligent expert system is connected with the input of described executing agency, forms automated closed-loop drilling well regulator control system thus;
Wherein, described spot sensor detection system is used for: in drilling well overall process, and Real-time Collection drilling well detects data, and the described drilling well detection data collected are uploaded to described intelligent expert system in real time;
Described intelligent expert system is used for: receive the described drilling well detection data that described spot sensor detection system is uploaded, and in real time intellectual analysis is carried out to described drilling well detection data, with Drilling optimization site technique for target, generate the regulation and control instruction to situ of drilling well equipment, and described regulation and control instruction is issued to corresponding executing agency;
Described executing agency is used for: receive the described regulation and control instruction that described intelligent expert system issues, and sends described regulation and control instruction to the site plant of correspondence, and then controls the duty of this site plant, thus Drilling optimization site technique process.
2. intelligent drilling expert system according to claim 1, is characterized in that, described spot sensor detection system comprises ground transaucer detection system and downhole sensor detection system;
Described ground transaucer detection system comprises winch state sensor detection subsystem, running status sensor detection subsystem, slush pump running status sensor detection subsystem, mud solid phase and volume trend state sensor detection subsystem, the operation monitoring detection subsystem of MCC, well control device detection subsystem, standpipe pressure detection subsystem and annular pressure detection subsystem are driven in automatic bit feed state sensor detection subsystem, rotating disk state sensor detection subsystem, top;
Described downhole sensor detection system comprises drill bit the pressure of the drill detection subsystem, torque-on-bit detection subsystem, drill speed detection subsystem, bottom pressure detection subsystem, bottom hole temperature (BHT) detection subsystem and bit orientation angle detection subsystem.
3. intelligent drilling expert system according to claim 2, is characterized in that, described winch state sensor detection subsystem is for detecting following information: winch running status, alarm condition, tourist bus position, hook weigh and winch motor electric current;
Described automatic bit feed state sensor detection subsystem is for detecting following information: send bore running status, alarm condition, tourist bus position, hook weigh and send brill current of electric;
Described rotating disk state sensor detection subsystem is for detecting following information: the restriction of rotary speed, rotating disk moment, rotary drilling moment of torsion and rotating disk electric current;
Running status sensor detection subsystem is driven for detecting following information in described top: running status, alarm condition, drilling torque, torque wrench moment, Top Drive RPM are driven in top, parameters is driven on top and electric current is driven on top;
Described slush pump running status sensor detection subsystem is for detecting following information: pump punching, pump pressure, discharge capacity and rotating speed;
Described mud solid phase and volume trend state sensor detection subsystem are for detecting following information: the detection of mud entrance density, slurry outlet density, mud entrance viscosity, slurry outlet viscosity, mud entrance flow, mud-outlet-flow, mud entrance temperature, slurry outlet temperature, oil-containing gassiness, mud pit liquid level, mud pit cumulative volume, mud entrance electrical conductivity and slurry outlet electrical conductivity;
The operation monitoring detection subsystem of described MCC is for detecting following information: Hydraulic Station pump, winch hydraulic power source, mixer, charge pump, replenishment pump, increase the weight of pump, mixing pump, deaerator, vibrosieve, centrifugal pump and shear pump.
4. intelligent drilling expert system according to claim 1, is characterized in that, described intelligent expert system comprises: the information-based man-machine interface of input block, expert model, output unit and overall process;
Described input block is used for described expert model input basic data source; Described basic data source comprises the well bore project data relevant to situ of drilling well, geological environment data, device attribute data, offset well data and described spot sensor detection system Real-time Collection and the described drilling well of uploading detects data;
Described expert model comprises wellbore hydraulics computation model, well drilling pipe column mechanics model, hole stability analytical model, drilling well risk profile and diagnostic model, borehole track computation model, rate of penetration and cost forecast model and with brill prediction of formation pressure model;
Wherein, described wellbore hydraulics computation model is used for: read the described basic data source that described input block inputs, and obtains borehole pump characteristic, drill column structure, drill bit classification, property of drilling fluid, drilling fluid in pipe and the flow regime of annular space; And calculate and creep into circular flow dynamic pressure consumption in real time, in conjunction with hydrodynamics basic theories, optimize and determine drilling well waterpower target component, and then generate control instruction to drilling process;
Described well drilling pipe column mechanics model is used for: read the current actual drilling assembly data that described input block inputs, obtain the drilling tool attribute in the actual well drilling pipe column used, comprise drilling tool classification, drilling tool length, drilling tool internal diameter external diameter value, the antitorque value of drilling tool tension, drilling tool service life and drilling tool class information; And calculate the Mechanical Data of this drilling tool when creeping in real time, optimize and determine drilling machinery limit restraint Protection parameters, and then generate the control instruction to drilling process;
Described hole stability analytical model is used for: read the derived data source that described input block inputs, obtain the real-time pressure equilibrium response of drilling fluid on annular space stratum, automatic calculating identification formation lithology, and the drillability stability feature of Different Strata and lithology, optimizing drilling mud and hydraulic parameters;
Described drilling well risk profile and diagnostic model are used for: pre-established various drilling well risk model; The described basic data source inputted by described input block is input to described drilling well risk model, runs described drilling well risk model, carries out risk profile to on-the-spot drilling condition; Wherein, described drilling well risk model comprises: well kick risk model, blowout risk model, leakage risk model and bit freezing risk model;
The running of described well kick risk model, described blowout risk model and described leakage risk model is: by described basic data source, obtain gas detect, data and the situation of change of drilling liquid parameter when boring, by analyzing the situation of change of data when described gas detect, brill and drilling liquid parameter, prediction relevant risk;
Described bit freezing risk model comprises: the sub-risk model of pressure reduction sticky suction bit freezing, the sub-risk model of sand setting bit freezing, the sub-risk model of the bit freezing that caves in, the sub-risk model of well wall dropping block bit freezing, turn on pump suppress the sub-risk model of junk bit freezing in the sub-risk model of Lou bit freezing, well, 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;
Described borehole track computation model is used for: obtain real stratigraphic section complete data, comprises formation lithology and density, reservoir characteristics and reference lamina, pneumatic jack, oil reservoir, interlayer, oily bed rock and the degree of depth, the formation fluid degree of depth and fluid pressure, fluid properties, real real-time working condition, the downhole drill dynamic operation condition boring three-dimensional wellbore trajectory, drill string and each lens and drill bit; Comprehensively analyze and integration described stratigraphic section complete data, interpretation process draws the technical data and decision-making for the treatment of that drilling well section is optimized, and compares with planned well path structure geology and engineering model moment, generates the control instruction to drilling process;
Described rate of penetration and cost forecast model are used for: predict real-time the pressure of the drill, and are optimized the pressure of the drill rotating speed; Be specially: the mathematic(al) mode of association drilling process basic law and set optimization aim, set up and creep into object function; On this basis, use Artificial Intelligence Controlling Theory and various linear, nonlinear programming approach, when determining various constraints, every drilling parameter of optimization object function, and then generate the control instruction to drilling process;
Describedly to be used for brill prediction of formation pressure model: bottom pressure to be controlled and mud line manifold system automation controls; Be specially: real-time analysis calculates at termination of pumping, the bottom pressure stopped under brill, trip-out, lower brill, drilling condition, use bottom pressure balance theory, set up bottom pressure Controlling model, thus optimizing drilling and the speed that makes a trip, and then generate control instruction to drilling process, control well is invaded, overflow, well kick, blowout, leakage, well slough drilling failure generation;
The control instruction to drilling process that described output unit generates for exporting described expert model; Also for output alarm information, casing programme, drilling assembly, ground environment, borehole track, mechanical parameter, hydraulic parameters, mud solid phase information;
The information-based man-machine interface of described overall process is used for: the real-time Simulation picture of display drilling process and various drilling well indicating risk.
5. intelligent drilling expert system according to claim 1, is characterized in that, described intelligent expert system also comprises: bore front simulation module and sum up back after boring and look into module;
The running of described brill front simulation module comprises the following steps:
S1, reads the basic data relevant to situ of drilling well, comprising: well bore project data, geological environment data, device attribute data and offset well data;
S2, based on described basic data, simulation creates virtual emulation well;
S3, described virtual emulation well loads pending drilling technology parameter, and choose a kind of expert model of pre-stored, is run by described drilling technology parameters input, obtain the 1st drilling well appraisal report to described expert model; Wherein, described 1st drilling well appraisal report carries out scientific evaluation for the engineering design to this virtual emulation well, complexity and risk, cost and efficiency;
S4, based on described 1st drilling well appraisal report, adjusts described drilling technology parameter, and S3 is carried out in circulation, and continuous being optimized described drilling technology parameter changes well thus, obtains best drilling technology parameter; Described drilling technology parameter carries out guide field drilling process;
Described brill is summed up back the running looking into module afterwards and is comprised the following steps:
S5, after drilling engineering terminates, the described virtual emulation well corresponding with situ of drilling well imports offset well complete report to selected expert model, and to the history real-time detector data in described expert model input drilling well all-work, after described expert model runs, obtain the 2nd drilling well appraisal report, and described 2nd drilling well appraisal report is stored as offset well supplemental characteristic.
6. intelligent drilling expert system according to claim 1, is characterized in that, the site plant that described executing agency drives comprises: Hoisting System, rotary system, the circulating system, power-equipment, drive apparatus, control system and auxiliary equipment.
7. intelligent drilling expert system according to claim 6, is characterized in that, described Hoisting System comprises winch, automatic bit feed winch, traveling system, rig tool, bank head machinery equipment;
Described rotary system comprises rotating disk, water tap and top drive;
The described circulating system comprises borehole pump, high pressure pipe joint, circulating tank, solid control equipment, Deal With Drilling Fluid and storage facilities;
Described power-equipment comprises diesel generating set, Gas Generator Set and motor;
Described drive apparatus comprises deceleration and car, transfer, turn to, reverse, speed change, bending moment machine driving, hydraulic power, hydraulic drive and electric-driving installation;
Described control system comprises SCR/VFD electric driving control system, hydraulic control system and pneumatic control system;
Described auxiliary equipment comprises air feed supply equipment, auxiliary generating plant, hoisting aids equipment and blowout control equipment.
8. intelligent drilling expert system according to claim 4, is characterized in that, described rate of penetration and cost forecast model, specifically for:
Step 10: the parameter optimization multiple objective function that structure PDC drill bit creeps into;
This step is specially: according to drilling speed object function and every meter of drilling cost object function, utilizes binary objective optimization theoretical, the parameter optimization multiple objective function be constructed as follows:
maxF(x)=α 1f 1(x)-α 2f 2(x)
Wherein:
F (x): object function;
F 1x () is rate of penetration target, unit is m/h;
F 2(x) for every meter of drilling cost target, unit be unit/m;
α 1, α 2for the weight of each object function of reflection policymaker wish,
Step 20: the constraints determining described parameter optimization multiple objective function, sets up PDC drill bit according to described constraints and creeps into optimization model;
Wherein, described constraints comprises: the pressure of the drill constraints, rotating speed constraints, bit wear constraints, drilling depth constraints, rate of penetration constraints and drilling cost constraints;
Step 30: according to creeping into real-time detected parameters, real-time regression Duffing equation parameter;
Step 40: creep into optimization model according to described PDC drill bit, determine the drilling parameter of PDC drill bit, realize the multiple-objection optimization of drilling parameter.
9. intelligent drilling expert system according to claim 8, it is characterized in that, step 10 is specially:
The parameter optimization multiple objective function set up is the object function of a broad sense; Work as weight α 1=0, α 2when=1, object function becomes every meter of drilling cost least model; Work as weight α 1=1, α 2when=0, object function becomes the maximum model of rate of penetration;
PDC drill bit equation for drilling rate and wear equation are the bases that PDC drill bit creeps into parameter objectives optimization; That is: the equation that following PDC bit drills fast mode and quaternary ROP mode combine is set up:
v pe = k W s a 1 N b 1 D c 1 e d 1 Δp
In formula:
V pefor drilling speed, m/h;
K is the coefficient of colligation relevant to drillability of rock factor;
W sfor than the pressure of the drill, kN/cm;
N is drill speed, r/min;
D be drill bit than water-horse power, W/cm2;
E is the truth of a matter of natural logrithm, is an irrational number, approximates 2.71828;
Δ p is bottom hole pressure difference, Mpa;
A 1, b 1, c 1, d 1be respectively the pressure of the drill, rotating speed, water-horse power and the bottom hole pressure difference influence coefficient to drilling speed;
Suppose what bit breakage mainly occurred with bit wear form, then according to bit life equation and linear cumulative damage law, the bit wear equation that can obtain a certain interval or a certain microbedding section is:
dh f dt = 1 a φ b W c e dN
In formula:
φ is internal friction angle of rock, degree;
T is the rotating hours, h;
H ffor bit wear amount, dimensionless;
Parameter a, b, c, d are bit life equation coefficient;
N is rotating speed to be optimized, rpm;
W is the pressure of the drill to be optimized, kN;
By integration, drill bit total drilling time t can be obtained ffor:
t f=aφ bW ce dN(h f2-h f1)
H f2, h f1be respectively the bit wear amount of current time and previous moment, dimensionless;
Total footage H ffor:
H f = k W s a 1 N b 1 D c 1 e d 1 Δp a φ b W c e dN ( h f 2 - h f 1 )
Drilling speed is:
v pe = k W s a 1 N b 1 D c 1 e d 1 Δp
Every meter of drilling cost is:
C pm = C b + C r [ a φ b W c e dN ( h f 2 - h f 1 ) + t t ] k W s a 1 N b 1 D c 1 e d 1 Δp aφ b W c e dN ( h f 2 - h f 1 )
In formula:
C pmfor every meter of drilling cost, unit/m;
C bfor drill bit cost, unit/only;
C rfor rig operating cost;
T tfor making a trip, making up a joint the time;
Multiple objective function expression formula is:
max F ( W , N , h f 2 ) = α 1 V pe - α 2 C pm = α 1 k W s a 1 N b 1 D c 1 e d 1 Δp - α 2 C b + C r [ a φ b W c e dN ( h f 2 - h f 1 ) + t t ] k W s a 1 N b 1 D c 1 e d 1 Δp a φ b W c e dN ( h f 2 - h f 1 )
Determine constraints, drilling parameter is analysed in depth, return the parameter calculated in above-mentioned equation, solve PDC drill bit drilling parameter Model for Multi-Objective Optimization according to the simulated annealing genetic algorithm based on real coding of application enhancements, determine PDC drill bit drilling parameter on the whole.
10. intelligent drilling expert system according to claim 9, it is characterized in that, described drilling parameter to be analysed in depth, return the parameter calculated in above-mentioned equation, solve PDC drill bit drilling parameter Model for Multi-Objective Optimization according to the simulated annealing genetic algorithm based on real coding of application enhancements, be specially:
Step 1: evolutionary generation counter initialization: t ← 0.
Step 2: random generation initial population P (t);
Step 3: the adaptive value evaluating population P (t);
Step 4: individual interlace operation: P ' (t) ← Crossover [P (t)];
Step 5: individual variation operates: P " and (t) ← Mutation [P ' (t)];
Step 6: individual simulated annealing operation: P " ' and (t) ← SimulatedAnnealing [P " (t)];
' adaptive value of (t) Step 7: evaluation population P ";
Step 8; Individual choice, copy operation: P (t+1) ← Reproduction [P (t) U P " ' (t)];
Step 9: end condition judges;
If do not meet end condition, then: t ← t+1, forward Step4 to, continue evolutionary process: if meet end condition, then: export current optimum individual, algorithm terminates.
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CN105422088A (en) * 2015-11-11 2016-03-23 中国煤炭科工集团太原研究院有限公司 Coal mine roadway geological parameter on-line monitoring system
CN106121621A (en) * 2016-07-15 2016-11-16 西南石油大学 A kind of intelligent drilling specialist system
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
CN106321059A (en) * 2016-08-30 2017-01-11 中国海洋石油总公司 Well control system and method for safety interlocking of other well drilling systems
CN106504328A (en) * 2016-10-27 2017-03-15 电子科技大学 A kind of complex geological structure modeling method reconstructed based on sparse point cloud surface
CN106545327A (en) * 2016-12-09 2017-03-29 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
CN107193055A (en) * 2017-05-27 2017-09-22 中国地质大学(武汉) A kind of complicated geological drilling process Double-layer intelligent drilling speed modeling
WO2017206182A1 (en) * 2016-06-03 2017-12-07 Schlumberger Technology Corporation Detecting events in well reports
CN108756848A (en) * 2018-05-21 2018-11-06 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
CN109577892A (en) * 2019-01-21 2019-04-05 西南石油大学 A kind of intelligent overflow detection system and method for early warning based on downhole parameters
CN110121053A (en) * 2018-02-07 2019-08-13 中国石油化工股份有限公司 A kind of video monitoring method of situ of drilling well risk stratification early warning
CN110145501A (en) * 2019-04-10 2019-08-20 中国矿业大学 A kind of Double rope winding extra deep shaft lifting system hoisting container posture control method
CN110567623A (en) * 2019-08-31 2019-12-13 中国石油集团川庆钻探工程有限公司 Drilling tool torque force analysis method and method for determining hydraulic oscillator installation position
CN111046593A (en) * 2019-04-29 2020-04-21 中国石油大学(华东) Three-dimensional horizontal well track optimization design method based on shortest drilling time

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1332878A (en) * 1998-11-19 2002-01-23 智能检测公司 Apparatus and method of well management
GB2364081B (en) * 2000-06-26 2002-12-11 Smith International Method for determining preferred drill bit design parameters and drilling parameters using a trained artificial neural network and methods for training the ar
US20040000430A1 (en) * 1996-03-25 2004-01-01 Halliburton Energy Service, Inc. Iterative drilling simulation process for enhanced economic decision making
CN1910589A (en) * 2004-03-04 2007-02-07 哈利伯顿能源服务公司 Method and system to model, measure, recalibrate, and optimize control of the drilling of a borehole
CN101333923A (en) * 2007-06-29 2008-12-31 普拉德研究及开发有限公司 Method of automatically controlling the trajectory of a drilled well
CN101566059A (en) * 2009-05-27 2009-10-28 江苏中曼石油设备有限公司 Wide-adaptive novel automatic driller
CN102852511A (en) * 2012-09-28 2013-01-02 中国科学院自动化研究所 Intelligent drilling control system and method for petroleum drilling machine
WO2014051612A1 (en) * 2012-09-28 2014-04-03 Landmark Graphics Corporation Self-guided geosteering assembly and method for optimizing well placement and quality
WO2014147575A1 (en) * 2013-03-20 2014-09-25 Schlumberger Technology Corporation Drilling system control
WO2014158706A1 (en) * 2013-03-13 2014-10-02 Halliburton Energy Services, Inc. Monitor and control of directional drilling operations and simulations
CN104145079A (en) * 2012-02-24 2014-11-12 兰德马克绘图国际公司 Determining optimal parameters for a downhole operation

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040000430A1 (en) * 1996-03-25 2004-01-01 Halliburton Energy Service, Inc. Iterative drilling simulation process for enhanced economic decision making
CN1332878A (en) * 1998-11-19 2002-01-23 智能检测公司 Apparatus and method of well management
GB2364081B (en) * 2000-06-26 2002-12-11 Smith International Method for determining preferred drill bit design parameters and drilling parameters using a trained artificial neural network and methods for training the ar
CN1910589A (en) * 2004-03-04 2007-02-07 哈利伯顿能源服务公司 Method and system to model, measure, recalibrate, and optimize control of the drilling of a borehole
CN101333923A (en) * 2007-06-29 2008-12-31 普拉德研究及开发有限公司 Method of automatically controlling the trajectory of a drilled well
CN101566059A (en) * 2009-05-27 2009-10-28 江苏中曼石油设备有限公司 Wide-adaptive novel automatic driller
CN104145079A (en) * 2012-02-24 2014-11-12 兰德马克绘图国际公司 Determining optimal parameters for a downhole operation
CN102852511A (en) * 2012-09-28 2013-01-02 中国科学院自动化研究所 Intelligent drilling control system and method for petroleum drilling machine
WO2014051612A1 (en) * 2012-09-28 2014-04-03 Landmark Graphics Corporation Self-guided geosteering assembly and method for optimizing well placement and quality
WO2014158706A1 (en) * 2013-03-13 2014-10-02 Halliburton Energy Services, Inc. Monitor and control of directional drilling operations and simulations
WO2014147575A1 (en) * 2013-03-20 2014-09-25 Schlumberger Technology Corporation Drilling system control

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
PINK, T等: "Building an Automated Drilling System Where Surface Data to Optimize the Well Construction Process", 《SOCIETY OF PETROLEUM ENGINEERS》 *
ROMMETVEIT,R等: "e-Drilling : A System for Real-Time Drilling Simulation, 3D Visualization and Control", 《SOCIETY OF PETROLEUM ENGINEERS》 *
张奇志等: "钻进参数优化研究综述", 《石油化工应用》 *
徐刚: "《流域梯级水电站联合优化调度理论与实践》", 31 August 2013, 中国水利水电出版社 *

Cited By (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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CN105422088A (en) * 2015-11-11 2016-03-23 中国煤炭科工集团太原研究院有限公司 Coal mine roadway 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
CN106321059A (en) * 2016-08-30 2017-01-11 中国海洋石油总公司 Well control system and method for safety interlocking of other well drilling systems
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
CN106504328A (en) * 2016-10-27 2017-03-15 电子科技大学 A kind of complex geological structure modeling method reconstructed based on sparse point cloud surface
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
CN106545327A (en) * 2016-12-09 2017-03-29 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
CN107193055A (en) * 2017-05-27 2017-09-22 中国地质大学(武汉) A kind of complicated geological drilling process Double-layer intelligent drilling speed modeling
CN107193055B (en) * 2017-05-27 2019-10-18 中国地质大学(武汉) A kind of complicated geological drilling process Double-layer intelligent drilling speed modeling
CN110121053A (en) * 2018-02-07 2019-08-13 中国石油化工股份有限公司 A kind of video monitoring method of situ of drilling well risk stratification early warning
CN108756848A (en) * 2018-05-21 2018-11-06 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
CN109577892A (en) * 2019-01-21 2019-04-05 西南石油大学 A kind of intelligent overflow detection system and method for early warning based on downhole parameters
CN109577892B (en) * 2019-01-21 2020-12-18 西南石油大学 Intelligent overflow detection system and early warning method based on downhole parameters
CN110145501A (en) * 2019-04-10 2019-08-20 中国矿业大学 A kind of Double rope winding extra deep shaft lifting system hoisting container posture control 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
CN111046593A (en) * 2019-04-29 2020-04-21 中国石油大学(华东) Three-dimensional horizontal well track optimization design method based on shortest drilling time
CN110567623A (en) * 2019-08-31 2019-12-13 中国石油集团川庆钻探工程有限公司 Drilling tool torque force analysis method and method for determining hydraulic oscillator installation position
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

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