CN111749686A - Drill bit rapid optimization method based on stratum drilling resistance parameters - Google Patents
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Abstract
The invention relates to the technical field of petroleum drilling, in particular to a drill bit fast optimization method based on stratum anti-drilling parameters, which comprises a stratum sensitive anti-drilling parameter extraction step, a drill bit type selection statistical mode establishment step and a drill bit optimization gray clustering step.
Description
Technical Field
The invention relates to the technical field of petroleum drilling, in particular to a drill bit rapid optimization method based on stratum drilling resistance parameters.
Background
The exploration and development of deep oil and gas resources are the main way to realize strategic succession of oil and gas resources in oil fields. The strong development of deep well and ultra-deep well drilling technology is not only the requirement of exploration and development, but also the requirement of actively adapting to the competition of drilling engineering technology service market.
The related data show that the quantity of petroleum recoverable resources in China is estimated to be 150 hundred million tons, and about 85 hundred million tons of petroleum recoverable resources are discovered and proved to account for 57 percent of the proved degree of petroleum recoverable resources. The part of petroleum resources to be explored are mainly distributed in four basins of Tarim wood, Quercoas, Chauda wood and Tuoha, and are main succession areas of the petroleum yield in China, and meanwhile, 73% of the resource amount is buried below 5000 meters.
The method realizes breakthrough of oil gas in new areas, new fields and new stratums, particularly deep layers, finds and enlarges the range and the field of oil gas in oil fields, becomes a main means for smoothly realizing the goal of replacing China petroleum resources, and is the most important realization mode for the quick and large-scale development of the deep well ultra-deep well drilling technology. In recent years, drilling technologies of deep wells, ultra-deep wells and horizontal wells in China have been greatly developed in Tarim basins, Sichuan basins and the like, but the problem of how to quickly and preferably select a drill bit adaptive to stratum properties to improve the mechanical drilling speed in actual drilling engineering is increasingly obvious.
Drilling deep wells and complex ultra-deep wells needs to drill through multiple sets of complex lithologic stratums in different geological ages, the pressure gradient change of the stratums in the same well section is large, and particularly, the underground complexity is aggravated due to the fact that geological conditions such as high temperature, high pressure and high ground stress exist in deep stratums. The problem of rapid drilling of deep hard formations is a technical problem in drilling and a bottleneck restricting the development of drilling technologies of deep wells, complex ultra-deep wells and horizontal wells, rock mechanical drilling resistance parameters of the deep formations are accurately calculated and extracted, the optimal type of a drill bit is selected, the number of times of tripping in and replacing the drill bit is reduced, the well body structure is optimized, and the method is one of key technologies in the aspects of improving the drilling speed of drilling machines of deep wells, ultra-deep wells and horizontal wells, reducing the drilling period, reducing the using amount of single-well drill bits and the like. The current common bit model selection method is to select a stratum core rock sample to perform indoor single-axis or confining pressure PDC micro bit and cone micro bit drillability and abrasiveness experiments to determine the drillability and abrasiveness of the rock sample stratum, and then select the bit type according to the latest standards and some mathematical analysis methods in the petroleum industry, such as discriminant analysis, neural network analysis, support vector machine method, etc., but the method comprises the following steps: firstly, the characteristics of rock in a heterogeneous stratum are difficult to reflect due to the limitation of coring data, the obtained drillability level value and abrasiveness data are scattered and discontinuous, and the rock drillability level value, abrasiveness and other test results cannot completely reflect the actual rock mechanics condition under the condition of underground temperature and pressure due to stress release of a rock core from the well bottom to the ground; and secondly, the mathematical analysis methods such as a discriminant analysis method, a neural network analysis method, a support vector machine method and the like are limited by the number of types and the resolution of the pattern recognition of the mathematical analysis methods, are generally used for recognizing or discriminating 3 to 5 types, have good effect, and cannot be used for optimizing various drill types and personalized drill bits researched and produced by various manufacturers and companies at present.
The problems that the existing model selection method technical scheme is discrete, random and limited according to experimental data, single in drilling resistance parameter, strong in limitation of a common model selection mathematical method and the like exist.
Disclosure of Invention
In order to overcome the problems and the defects in the prior art, the invention aims to provide a method for quickly and accurately extracting stratum sensitive drilling-resistant parameters according to various well logging data of a well zone where a well is to be drilled, comparing and selecting drill bit types with different stratum wells and fast depths, establishing a large data mode library of the stratum drilling-resistant parameters and the corresponding efficient drill bit types, and giving out the drill bit quick optimization method of the optimal drill bit type of the stratum of different well sections where the well is drilled, is being drilled or is to be drilled by adopting a grey dynamic clustering analysis method.
The invention discloses a drill bit rapid optimization method based on stratum drilling resistance parameters, which is characterized by comprising the following steps of: the method comprises the steps of extracting stratum sensitive drilling resistance parameters, establishing a drill bit type selection statistical mode and clustering the optimal gray of the drill bit;
the stratum sensitive anti-drilling parameter extraction step is to obtain continuous rock anti-drilling parameters of the drilling stratum section of all types to be selected by the drill bit through the GR, AC, DEN and other logging curve data of the stratum, and to obtain the stratum sensitive anti-drilling parameters of the drilling well of all types to be selected by the drill bit through the continuous rock anti-drilling parameters to serve as a stratum sensitive anti-drilling parameter library to be selected;
the drill bit model selection statistical mode establishing step comprises sub-mode establishing and center mode establishing, specifically, the sub-mode establishing is to select a drill bit type which corresponds to the sensitive anti-drilling parameters of each well section stratum, has high mechanical drilling speed (not less than 15m/h), good use effect and long service life (continuous service life is not less than 24h) as the sub-mode data of the alternative model drill bit type according to the sensitive anti-drilling parameters of each well section stratum obtained in the stratum sensitive anti-drilling parameter extracting step; the central mode is established by counting the times of the alternative type drill bit types contained in the sub-mode data selected in the set key well, and carrying out weighted average on the stratum sensitive anti-drilling parameters corresponding to the alternative type drill bit types selected in each time of the selected key well to obtain typical stratum sensitive anti-drilling parameters corresponding to each alternative type drill bit type; weighted averaging stratum sensitive drilling resistance parameters with the same drill bit type to obtain a strong representative stratum sensitive drilling resistance parameter height reflection drill bit type center statistical mode; the other statistical mode is formed by adding layers (subsamples) into the central mode, wherein the central mode not only has the main formation drilling resistance characteristics of the central mode, but also contains the relative sensitive drilling resistance parameters such as the drillability of the stratum section of the key well, the rock strength and the like, and the mode is superior to the independent central clustering mode and the submodes.
The drill bit optimization gray clustering step comprises the steps of extracting the stratum sensitive anti-drilling parameter library to be selected obtained in the stratum sensitive anti-drilling parameter extracting step, establishing a typical stratum sensitive anti-drilling parameter corresponding to each alternative drill bit type obtained in the drill bit selection statistical mode establishing step, taking the reference data containing the drill bit usage selection record in the existing well section as a gray system containing known factors and unknown factors, extracting the stratum sensitive anti-drilling parameters corresponding to the well section and the selected drill bit type from the reference data containing the drill bit usage selection record in the existing well section, converging the typical stratum sensitive anti-drilling parameters as a standard library of gray system evaluation parameters, calculating the gray correlation coefficient of each group of parameters in the stratum sensitive anti-drilling parameter library to be selected and each group of parameters in the standard library through the gray system, and selecting the drill bit corresponding to the parameter with the highest correlation coefficient of each group of parameters in the stratum sensitive anti-drilling parameter library to be selected in the standard library The type is the preferred drill bit. The automatic drill bit model selection mathematical method based on gray dynamic clustering pattern recognition overcomes the defects that pattern recognition methods such as a neural network method, a support vector machine method, a linear and nonlinear discriminant analysis method and the like are seriously limited by the number of types and difficult to model, comprises a plurality of pattern recognition types, can dynamically add new drill bit patterns at any time, actively expands and enriches a drill bit type pattern library, and has the advantages of quick and simple modeling, accurate and reasonable prediction result, intuition, feasibility and the like.
Further, in the stratum sensitive drilling-resistant parameter extraction step, the stratum sensitive drilling-resistant parameters comprise argillaceous substancesContent VshCompressive strength Sc, shear strength Ss, drillable grade Kd, abrasiveness Gd, hardness Hd, effective stress Pe, and brittleness index BI. The type of the drill bit is a main factor for determining the economic effect of the use engineering of the drill bit, for example, the roller cone drill bit mainly breaks rock by impact and mainly correspondingly reflects the compressive strength (Sc) and hardness (Hd) of the rock; the diamond drill bit mainly performs press-in scraping rock breaking and mainly correspondingly reflects rock hardness (Hd), shear strength (Ss) and the like. Rock drillability is the ability of rock to resist drill bit impact and shear failure, and is one of the direct bases for drill bit model selection, generally, the drillability level value of carbonate rock stratum is the highest, the drillability level value of pure sandstone rock stratum is higher, the drillability level value of porous sandstone rock stratum is smaller, and the drillability level value of mudstone hard interlayer rock fluctuates greatly, and the drillability average value is lower than that of the porous sandstone stratum.
The abrasiveness is a key parameter for judging the service life of the drill bit, the abrasiveness of the rock in a pure sandstone stratum is the highest, the abrasiveness of the rock in a carbonate rock stratum is higher, the abrasiveness of the rock in a porous sandstone stratum is smaller, the abrasiveness of a mudstone stratum is the smallest, the abrasiveness fluctuation of a hard interlayer of the mudstone is larger, and the average value of the abrasiveness of the hard interlayer of the mudstone is lower than that of the porous sandstone stratum and higher than that of the mudstone stratum.
The drilling resistance parameters of rocks such as drillability, abrasiveness, hardness and the like are the basis of the specific design and model selection of the drill bit, and a large amount of formation rock drillability information is stored in the logging information. For example, the mechanical properties of rock can be well reflected by the formation acoustic wave time difference and the density log, wherein the shear wave time difference reflects the shear deformation properties of the formation, and the longitudinal wave time difference reflects the tensile and compressive deformation properties and strength properties of the formation; the lower the acoustic wave time difference and the higher the density, the higher the drillable grade value. In addition, the shale content and the drillability of the rock are closely related, and the higher the shale content is, the lower the drillability value is, and the better the drillability of the rock is; the higher the shear strength and the compressive strength of stratum rock, the larger the hardness, the larger the effective stress, the smaller the brittleness index, the poorer the drillability of rock and the higher the drillable grade value.
Specifically, the argillaceous content VshIs calculated from the gamma log values in the log data,
in the formula, GCURTaking the geological age coefficient, such as taking 3.7 for a new stratum (a third series) and 2 for an old stratum;
while
Wherein GR is the gamma log value in API; and GRmaxAnd GRminThe maximum and minimum gamma log values (typically 140API and 40API) in the measurement are for the corresponding well section of the log data, respectively.
The compressive strength Sc is uniaxial compressive strength of rock, has a unit of MPa, and comprises compressive strength of a sand shale stratum and compressive strength of a carbonate rock stratum,
compressive strength Sc of sand shale stratum is 0.0045E (1-V)sh)+0.008E×Vsh，
Carbonate formation compressive strength Sc 0.0026E (1-V)sh)+0.008E×Vsh，
Wherein E is the Young's modulus of elasticity of the rock in MPa and VshIs the mud content;
the shear strength
The unit is MP; wherein Sc is compressive strength and k isbIs the bulk modulus of the rock,
wherein β is 109△ t being dimension transfer factors、△tcRespectively the time difference of transverse wave and longitudinal wave of the rock, and the unit is us/m; rhobIs the bulk density log of the rock volume in g/cm3。
Transverse wave of the rock
In the formula, △ tcThe time difference of the longitudinal wave obtained by direct measurement is expressed in us/m, and GR is a gamma log value.
The drillable grade value Kd is 18.9e-0.0051ACWhere AC is the rock longitudinal wave time difference, i.e. △ tc。
The abrasiveness
Wherein Kd is the drillable-grade value.
The hardness Hd is 84.109Sc +132.59, the unit is MP, and Sc is compressive strength.
The effective stress Pe ═ Sv-alpha Pp, unit is MPa, in the formula,
α is the Biot coefficient (effective stress coefficient or pore elastic coefficient), dimensionless, typically taken as 0.5 or 1.0,
and CbAnd CmaVolume compressibility (MPa) of rock mass and skeleton in the measured formation-1) Is a constant;
Sv＝(∑ρb×RLEV+2.31×H0)×0.00981，ρblogging the bulk density of the rock volume; RLEV is a logging sampling interval, and is generally 0.125m, H0Starting depth (m) of valid data for density logging;
pp is formation pore pressure (MPa), Pp ═ Go×TVD+(Gn-Go)×HeIn the formula, G0And GnRespectively overburden pressure gradient and hydrostatic pressure gradient (MPa/m), TVD is logging vertical depth (m), HeIs the equivalent depth (m).
The brittleness index BI (%) - (Delta E + Delta PR)/2, wherein
ΔE＝(E-Emin)/(Emax-Emin) × 100, E is Young's modulus of elasticity (MPa),
Emaxand EminRespectively the number of well-logging curvesAccording to the maximum and minimum Young's modulus (generally 100 and 10) of the corresponding well section in the measurement, the unit is GPa;
ΔPR＝(PRmax-PR)/(PRmax-PRmin) × 100, PR is the rock Poisson's ratio,
dimensionless, PRmaxAnd PRminRespectively taking the maximum Poisson ratio and the minimum Poisson ratio (generally 0.4 and 0.1) of the well section corresponding to the logging curve data in measurement;
in the formula of △ ts、△tcThe time difference (us/m) of transverse wave and longitudinal wave respectively, β is 109Is a transfer factor.
Further, in the drill bit preferred gray clustering step, known factors comprise evaluation criteria, evaluation parameters and weight values; unknown factors include the drill bit type.
The bit optimizing gray clustering step includes that firstly, an input stratum sensitive drilling resistance parameter base to be selected and a standard base are preprocessed by a gray system, each group of sensitive drilling resistance parameters are processed into normalized data columns in a dimensionless and normalized mode, then extreme values of layer point standard index absolute values of each normalized data column are subjected to weighted combination amplification, and gray multiple weighting coefficients are worked out; then, a comprehensive normalization method is adopted to normalize each gray multivariate weighting coefficient to a value to form a row matrix (generally 1 row and M columns) of gray weighted normalization coefficients; and finally, selecting the maximum value in the system multivariate weighted normalization vector by adopting a maximum membership principle Pmax ═ max { Cgi }, wherein the standard mode sample class attribute (namely the actually used high-efficiency drill bit class) corresponding to the maximum value is the classification result of the well section to be judged, namely the selected drill bit class.
The original data are normalized, in view of different physical meanings and different dimensions of various stratum characteristic parameters, the original data need to be preprocessed to generate dimensionless and normalized data columns, and normalized data columns obtained by preprocessing the sensitive drilling-resistant parameter library of the stratum to be selected are compared number columns Xi' (j) and a normalized data column obtained by preprocessing the standard libraryEvaluation series X0' (j) evaluation series X by the range standardization method0' (j) and the compared series Xi' (j) performing a data transformation,
namely, it is
(j＝1，2，3，...，M)，
(i＝1，2，3，...，n)；
In the formula, X0(j)，Xi(j) For the normalized evaluation series and compared series, n is equal to the number of known candidate bit types and M is the number of variables.
The gray multivariate weighting coefficient is obtained by weighting and combining and amplifying extreme values of absolute values of layer point standard indexes, and specifically, the gray multivariate weighting coefficient
As data X0And XiGray multivariate weighting factor at jth parameter, where Δ i (j) is data X0And XiThe absolute difference Δ i (j) of the criteria for the jth parameter, X0(j)-Xi(j)，
And
the minimum difference and the maximum difference of the two poles are used as standard indexes, A is a gray resolution coefficient which is usually 0.5, and Y (j) is the weight of the jth parameter.
The grey multivariate weighting coefficient is classified into a value by adopting a comprehensive normalization method, and specifically, a compared array X is calculatediAnd evaluation series X0The gray-associated similarity coefficient Cgi of (c),
compared with the prior art, the technical scheme of the invention has the following beneficial effects:
according to the relation between the structural characteristic type of the drill bit and the stratum sensitive anti-drilling parameter, the method utilizes the cable of a well region where a well to be drilled or a well being drilled is located or logging data while drilling to extract the stratum sensitive anti-drilling parameter, namely obtains the rock anti-drilling parameter of the whole stratum section by using logging curve data such as AC, GR, DEN and the like, contrasts and selects the drill bit type with faster depth of the well in different stratums, and establishes a drill bit type selection database for quickly drilling the stratum anti-drilling parameter and the high-efficiency drill bit type. Meanwhile, an automatic drill bit model selection method based on gray dynamic clustering pattern recognition is established, the method overcomes the defects that the sample types of pattern recognition methods such as a neural network method, a support vector machine method, a linear and nonlinear discriminant analysis method and the like cannot be too many (not more than 5), the pattern samples need to be learned persistently, and modeling is difficult and complicated, is an accurate, efficient and strong-practicability drill bit model selection method, reduces the times of frequently tripping and replacing a drill bit in a drilling process, and particularly ensures higher mechanical drilling speed in the drilling process by automatically selecting a cone and a PDC drill bit which are matched and adapted with stratum drilling resistance attributes (parameters) from the existing drill bit types when drilling a deep well, an ultra-deep well and a well with a complex structure.
The scheme in the scheme belongs to an open big data cloud data system, new modes can be added or old modes can be deleted at any time, the method is not limited by the storage capacity, namely the included modes are numerous, a complex high nonlinear mathematical model is not required to be established through repeated training and learning for a large number of times, the optimal drill bit type of a drilled well or a drill bit to be drilled can be given quickly, the calculation result is visual, accurate, continuous and economical, the operability is high, and the method is more favorable for field application. At present, in the complex ultra-deep well body structure and track optimization practical project in Chongqing areas, obvious application effects of speed increase, drilling cost saving and the like are obtained.
In addition, the automatic drill bit model selection mathematical method based on gray dynamic clustering pattern recognition overcomes the defects that pattern recognition methods such as a neural network method, a support vector machine method, a linear and nonlinear discriminant analysis method and the like are seriously limited by the number of types and difficult to model, and the like.
Detailed Description
The technical solutions for achieving the objects of the present invention are further illustrated by the following specific examples, and it should be noted that the technical solutions claimed in the present invention include, but are not limited to, the following examples.
As a specific implementation scheme of the system, the embodiment discloses a rapid drill bit optimization method based on stratum drilling resistance parameters, which comprises a stratum sensitive drilling resistance parameter extraction step, a drill bit type selection statistical mode establishment step and a drill bit optimization gray clustering step;
the stratum sensitive anti-drilling parameter extraction step is to obtain continuous rock anti-drilling parameters of the drilling stratum section of all types to be selected by the drill bit through the GR, AC, DEN and other logging curve data of the stratum, and to obtain the stratum sensitive anti-drilling parameters of the drilling well of all types to be selected by the drill bit through the continuous rock anti-drilling parameters to serve as a stratum sensitive anti-drilling parameter library to be selected;
and the formation sensitive drilling-resistant parameter comprises a shale content VshCompressive strength Sc, shear strength Ss, drillable grade Kd, abrasiveness Gd, hardness Hd, effective stress Pe, and brittleness index BI. The type of the drill bit is a main factor for determining the economic effect of the use engineering of the drill bit, for example, the roller cone drill bit mainly breaks rock by impact and mainly correspondingly reflects the compressive strength (Sc) and hardness (Hd) of the rock; the diamond drill bit mainly performs press-in scraping rock breaking and mainly correspondingly reflects rock hardness (Hd), shear strength (Ss) and the like. The drillability of rock is the ability of rock to resist the impact and shear damage of a drill bit, and is one of the direct bases of drill bit type selection, generally, the drillability level value of carbonate rock stratum is highest, the drillability level value of pure sandstone rock stratum is higher, the drillability level value of porous sandstone rock stratum is smaller, the drillability level value of mudstone hard interlayer rock is more fluctuant, and the drillability average value is larger than that of the porous sandstone rockThe formation is low.
The abrasiveness is a key parameter for judging the service life of the drill bit, the abrasiveness of the rock in a pure sandstone stratum is the highest, the abrasiveness of the rock in a carbonate rock stratum is higher, the abrasiveness of the rock in a porous sandstone stratum is smaller, the abrasiveness of a mudstone stratum is the smallest, the abrasiveness fluctuation of a hard interlayer of the mudstone is larger, and the average value of the abrasiveness of the hard interlayer of the mudstone is lower than that of the porous sandstone stratum and higher than that of the mudstone stratum.
The drilling resistance parameters of rocks such as drillability, abrasiveness, hardness and the like are the basis of the specific design and model selection of the drill bit, and a large amount of formation rock drillability information is stored in the logging information. For example, the mechanical properties of rock can be well reflected by the formation acoustic wave time difference and the density log, wherein the shear wave time difference reflects the shear deformation properties of the formation, and the longitudinal wave time difference reflects the tensile and compressive deformation properties and strength properties of the formation; the lower the acoustic wave time difference and the higher the density, the higher the drillable grade value. In addition, the shale content and the drillability of the rock are closely related, and the higher the shale content is, the lower the drillability value is, and the better the drillability of the rock is; the higher the shear strength and the compressive strength of stratum rock, the larger the hardness, the larger the effective stress, the smaller the brittleness index, the poorer the drillability of rock and the higher the drillable grade value.
Specifically, the argillaceous content VshIs calculated from the gamma log values in the log data,
in the formula, GCURTaking the geological age coefficient, such as taking 3.7 for a new stratum (a third series) and 2 for an old stratum;
while
Wherein GR is the gamma log value in API; and GRmaxAnd GRminThe maximum and minimum gamma log values (typically 140API and 40API) in the measurement are for the corresponding well section of the log data, respectively.
The compressive strength Sc is uniaxial compressive strength of rock, has a unit of MPa, and comprises compressive strength of a sand shale stratum and compressive strength of a carbonate rock stratum,
compressive strength Sc of sand shale stratum is 0.0045E (1-V)sh)+0.008E×Vsh，
Carbonate formation compressive strength Sc 0.0026E (1-V)sh)+0.008E×Vsh，
Wherein E is the Young's modulus of elasticity of the rock in MPa and VshIs the mud content;
the shear strength
The unit is MP; wherein Sc is compressive strength and k isbIs the bulk modulus of the rock,
wherein β is 109△ t being dimension transfer factors、△tcRespectively the time difference of transverse wave and longitudinal wave of the rock, and the unit is us/m; rhobIs the bulk density log of the rock volume in g/cm3。
Transverse wave of the rock
In the formula, △ tcThe time difference of the longitudinal wave obtained by direct measurement is expressed in us/m, and GR is a gamma log value.
The drillable grade value Kd is 18.9e-0.0051ACWhere AC is the rock longitudinal wave time difference, i.e. △ tc。
The abrasiveness
Wherein Kd is the drillable-grade value.
The hardness Hd is 84.109Sc +132.59, the unit is MP, and Sc is compressive strength.
The effective stress Pe ═ Sv-alpha Pp, unit is MPa, in the formula,
α is Biot coefficient (effective stress system)Number or pore elastic coefficient), dimensionless, typically taken as 0.5 or 1.0,
and CbAnd CmaVolume compressibility (MPa) of rock mass and skeleton in the measured formation-1) Is a constant;
Sv＝(∑ρb×RLEV+2.31×H0)×0.00981，ρblogging the bulk density of the rock volume; RLEV is a logging sampling interval, and is generally 0.125m, H0Starting depth (m) of valid data for density logging;
pp is formation pore pressure (MPa), Pp ═ Go×TVD+(Gn-Go)×HeIn the formula, G0And GnRespectively overburden pressure gradient and hydrostatic pressure gradient (MPa/m), TVD is logging vertical depth (m), HeIs the equivalent depth (m).
The brittleness index BI (%) - (Delta E + Delta PR)/2, wherein
ΔE＝(E-Emin)/(Emax-Emin) × 100, E is Young's modulus of elasticity (MPa),
Emaxand EminRespectively taking the maximum and minimum Young modulus (generally 100 and 10) of a well section corresponding to the logging curve data in the measurement, and the unit is GPa;
ΔPR＝(PRmax-PR)/(PRmax-PRmin) × 100, PR is the rock Poisson's ratio,
dimensionless, PRmaxAnd PRminRespectively taking the maximum Poisson ratio and the minimum Poisson ratio (generally 0.4 and 0.1) of the well section corresponding to the logging curve data in measurement;
in the formula of △ ts、△tcThe time difference (us/m) of transverse wave and longitudinal wave respectively, β is 109Is a transfer factor.
That is, according to the above-mentioned aspect, the content including the argillaceous component can be obtainedVshThe stratum sensitive drilling-resistant parameter library comprises stratum sensitive drilling-resistant parameters such as compressive strength Sc, shear strength Ss, drillable level Kd, abrasiveness Gd, hardness Hd, effective stress Pe, brittleness index BI and the like.
Further, the drill bit model selection statistical mode establishing step comprises sub-mode establishing and center mode establishing, specifically, the sub-mode establishing is to select a drill bit type which corresponds to the sensitive anti-drilling parameters of the stratum of each well section and has high mechanical drilling speed (not less than 15m/h), good use effect and long service life (the continuous service life is not less than 24h) as the sub-mode data of the alternative model drill bit type according to the sensitive anti-drilling parameters of the stratum of each well section obtained in the stratum sensitive anti-drilling parameter extracting step;
the method comprises the following steps of (1) performing integrated analysis on the known optimal corresponding relation between each type of drill bit and the stratum drilling resistance sensitive parameters as prior data to obtain sub-mode data of the alternative drill bit type as shown in the following table:
TABLE 1
The central mode is established by counting the times of the alternative type drill bit types contained in the sub-mode data selected in the set key well, and carrying out weighted average on the stratum sensitive anti-drilling parameters corresponding to the alternative type drill bit types selected in each time of the selected key well to obtain typical stratum sensitive anti-drilling parameters corresponding to each alternative type drill bit type;
that is, as shown in table 2 below, the drill bit type-selective center cluster model based on formation sensitive drilling resistance parameters such as drillability and abrasiveness is a weighted average of the drill bit type repeat terms for the data in table 1:
TABLE 2
| NO | BI(％) | Vsh(％) | Kd | Gd(mg) | Hd(MPa) | Sc(MPa) | Ss(MPa) | Pe(MPa) | Drill bit type |
| 1 | 47.85 | 5.36 | 8.08 | 12.54 | 2404.80 | 181.00 | 33.42 | 29.33 | DFS1605BU |
| 2 | 44.87 | 15.73 | 7.32 | 9.50 | 1765.80 | 181.40 | 25.74 | 39.62 | HJT617GL |
| 3 | 48.54 | 15.48 | 6.78 | 7.32 | 1389.00 | 208.30 | 30.98 | 43.43 | SJT617GG |
| 4 | 38.47 | 25.65 | 6.04 | 5.31 | 1264.40 | 155.40 | 23.12 | 37.90 | KPM1634DST |
| 5 | 41.29 | 56.97 | 6.39 | 5.92 | 1512.10 | 227.70 | 33.04 | 44.87 | SH533 |
| 6 | 39.55 | 36.76 | 6.39 | 7.10 | 1633.40 | 184.80 | 24.26 | 37.07 | DF1606BU |
| 7 | 33.63 | 17.78 | 4.98 | 2.84 | 778.80 | 105.40 | 14.42 | 17.52 | GS1905T |
| 8 | 34.89 | 16.27 | 5.36 | 3.73 | 933.00 | 118.30 | 17.12 | 29.29 | GS1635R |
| 9 | 40.02 | 32.51 | 6.20 | 6.17 | 1530.60 | 171.60 | 26.93 | 54.00 | GS1605S |
| 12 | 35.02 | 23.04 | 5.45 | 3.80 | 951.70 | 156.50 | 19.64 | 6.26 | HJT547GK |
| 13 | 37.41 | 9.45 | 5.89 | 4.79 | 1113.90 | 157.50 | 21.25 | 10.56 | GS1606S |
| 14 | 38.62 | 9.15 | 6.15 | 5.44 | 1228.90 | 165.80 | 23.21 | 15.90 | SV516TAUL |
| 15 | 36.20 | 28.12 | 5.68 | 4.39 | 1078.00 | 156.10 | 12.47 | 28.60 | HJT547G |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
Weighted averaging stratum sensitive drilling resistance parameters with the same drill bit type to obtain a strong representative stratum sensitive drilling resistance parameter height reflection drill bit type center statistical mode; the other statistical mode is formed by adding layers (subsamples) into the central mode, wherein the central mode not only has the main formation drilling resistance characteristics of the central mode, but also contains the relative sensitive drilling resistance parameters such as the drillability of the stratum section of the key well, the rock strength and the like, and the mode is superior to the independent central clustering mode and the submodes.
Further, the drill bit optimization gray clustering step includes the steps of extracting the stratum sensitive anti-drilling parameter library to be selected obtained in the stratum sensitive anti-drilling parameter extracting step, obtaining typical stratum sensitive anti-drilling parameters corresponding to each alternative drill bit type obtained in the drill bit selection statistical mode establishing step, using reference data containing drill bit usage selection records in the existing well section as a gray system containing known factors and unknown factors, extracting stratum sensitive anti-drilling parameters corresponding to the well section and the selected drill bit type from the reference data containing the drill bit usage selection records in the existing well section, converging the typical stratum sensitive anti-drilling parameters as a standard library of gray system evaluation parameters, and then calculating gray correlation coefficients of each group of parameters in the stratum sensitive anti-drilling parameter library to be selected and each group of parameters in the standard library through the gray system, and selecting the drill bit type corresponding to the parameter with the highest correlation coefficient with each group of parameters in the sensitive drilling-resistant parameter library of the stratum to be selected in the standard library as the preferred drill bit. The automatic drill bit model selection mathematical method based on gray dynamic clustering pattern recognition overcomes the defects that pattern recognition methods such as a neural network method, a support vector machine method, a linear and nonlinear discriminant analysis method and the like are seriously limited by the number of types and difficult to model, comprises a plurality of pattern recognition types, can dynamically add new drill bit patterns at any time, actively expands and enriches a drill bit type pattern library, and has the advantages of quick and simple modeling, accurate and reasonable prediction result, intuition, feasibility and the like.
Namely, the preferred and recommended results for the drill bit for each interval as shown in table 3 below:
TABLE 3
Preferably, in the drill bit preferred gray clustering step, known factors comprise evaluation criteria, evaluation parameters and weight values; unknown factors include the drill bit type.
The bit optimizing gray clustering step includes that firstly, an input stratum sensitive drilling resistance parameter base to be selected and a standard base are preprocessed by a gray system, each group of sensitive drilling resistance parameters are processed into normalized data columns in a dimensionless and normalized mode, then extreme values of layer point standard index absolute values of each normalized data column are subjected to weighted combination amplification, and gray multiple weighting coefficients are worked out; then, a comprehensive normalization method is adopted to normalize each gray multivariate weighting coefficient to a value to form a row matrix (generally 1 row and M columns) of gray weighted normalization coefficients; and finally, selecting the maximum value in the system multivariate weighted normalization vector by adopting a maximum membership principle Pmax ═ max { Cgi }, wherein the standard mode sample class attribute (namely the actually used high-efficiency drill bit class) corresponding to the maximum value is the classification result of the well section to be judged, namely the selected drill bit class.
The original data are normalized, in view of different physical meanings and different dimensions of various stratum characteristic parameters, the original data need to be preprocessed to generate dimensionless and normalized data columns, and normalized data columns obtained by preprocessing the sensitive drilling-resistant parameter library of the stratum to be selected are compared number columns Xi' (j) and the evaluation number sequence X of the normalized data sequence obtained by preprocessing the standard library0' (j) evaluation series X by the range standardization method0' (j) and the compared series Xi' (j) performing a data transformation,
namely, it is
(j＝1，2，3，...，M)，
(i＝1，2，3，...，n)；
In the formula, X0(j)，Xi(j) For the normalized evaluation series and compared series, n is equal to the number of known candidate bit types and M is the number of variables.
The gray multivariate weighting coefficient is obtained by weighting and combining and amplifying extreme values of absolute values of layer point standard indexes, and specifically, the gray multivariate weighting coefficient
As data X0And XiGray multivariate weighting factor at jth parameter, where Δ i (j) is data X0And XiThe absolute difference Δ i (j) of the criteria for the jth parameter, X0(j)-Xi(j)，
And
the minimum difference and the maximum difference of the two poles are used as standard indexes, A is a gray resolution coefficient which is usually 0.5, and Y (j) is the weight of the jth parameter.
The grey multivariate weighting coefficient is classified into a value by adopting a comprehensive normalization method, and specifically, a compared array X is calculatediAnd evaluation series X0The gray-associated similarity coefficient Cgi of (c),
Claims (10)
1. a drill bit rapid optimization method based on stratum drilling resistance parameters is characterized by comprising the following steps: the method comprises the steps of extracting stratum sensitive drilling resistance parameters, establishing a drill bit type selection statistical mode and clustering the optimal gray of the drill bit; the stratum sensitive anti-drilling parameter extraction step is that continuous rock anti-drilling parameters of the drilling stratum section of all types to be selected of the drill bit are obtained through the logging curve data of the stratum, and the stratum sensitive anti-drilling parameters of the drilling of all types to be selected of the drill bit are obtained through the continuous rock anti-drilling parameters to serve as a stratum sensitive anti-drilling parameter library to be selected;
the drill bit model selection statistical mode establishing step comprises sub-mode establishing and center mode establishing, specifically, the sub-mode establishing is to select a drill bit type with a mechanical drilling speed of not less than 15m/h and a continuous service life of not less than 24h corresponding to the sensitive drilling resistance parameter of each well section stratum as sub-mode data of the alternative model drill bit type according to the sensitive drilling resistance parameter of each well section stratum obtained in the stratum sensitive drilling resistance parameter extracting step; the central mode is established by counting the times of the alternative type drill bit types contained in the sub-mode data selected in the set key well, and carrying out weighted average on the stratum sensitive anti-drilling parameters corresponding to the alternative type drill bit types selected in each time of the selected key well to obtain typical stratum sensitive anti-drilling parameters corresponding to each alternative type drill bit type;
the drill bit optimization gray clustering step comprises the steps of extracting the stratum sensitive anti-drilling parameter library to be selected obtained in the stratum sensitive anti-drilling parameter extracting step, establishing a typical stratum sensitive anti-drilling parameter corresponding to each alternative drill bit type obtained in the drill bit selection statistical mode establishing step, taking the reference data containing the drill bit usage selection record in the existing well section as a gray system containing known factors and unknown factors, extracting the stratum sensitive anti-drilling parameters corresponding to the well section and the selected drill bit type from the reference data containing the drill bit usage selection record in the existing well section, converging the typical stratum sensitive anti-drilling parameters as a standard library of gray system evaluation parameters, calculating the gray correlation coefficient of each group of parameters in the stratum sensitive anti-drilling parameter library to be selected and each group of parameters in the standard library through the gray system, and selecting the drill bit corresponding to the parameter with the highest correlation coefficient of each group of parameters in the stratum sensitive anti-drilling parameter library to be selected in the standard library The type is the preferred drill bit.
2. The method for rapidly optimizing the drill bit based on the formation drilling resistance parameter as claimed in claim 1, wherein: the stratum sensitive drilling-resistant parameters comprise argillaceous content VshCompressive strength Sc, shear strength Ss, drillable grade Kd, abrasiveness Gd, hardness Hd, effective stress Pe, and brittleness index BI.
3. The drill bit tool based on the formation drilling resistance parameter as claimed in claim 2A method of rapid optimization characterized by: the mud content VshIs calculated from the gamma log values in the log data,
in the formula, GCURIs a geological age coefficient;
while
Wherein GR is the gamma log value in API; and GRmaxAnd GRminMaximum and minimum gamma log values (generally 140API and 40API) in the measurement for the corresponding well section of the log data, respectively;
the compressive strength Sc is uniaxial compressive strength of rock, has a unit of MPa, and comprises compressive strength of a sand shale stratum and compressive strength of a carbonate rock stratum,
compressive strength Sc of sand shale stratum is 0.0045E (1-V)sh)+0.008E×Vsh，
Carbonate formation compressive strength Sc 0.0026E (1-V)sh)+0.008E×Vsh，
Wherein E is the Young's modulus of elasticity of the rock in MPa and VshIs the mud content;
the shear strength
The unit is MP; wherein Sc is compressive strength and k isbIs the bulk modulus of the rock,
wherein β is 109△ t being dimension transfer factors、△tcRespectively the time difference of transverse wave and longitudinal wave of the rock, and the unit is us/m; rhobIs the bulk density log of the rock volume in g/cm3。
4. A method for rapid bit optimization based on formation drilling resistance parameters according to claim 2 or 3, wherein: transverse wave of the rock
In the formula, △ tcThe unit is us/m for the measured longitudinal wave time difference, and GR is a gamma logging value;
the drillable grade value Kd is 18.9e-0.0051ACWhere AC is the rock longitudinal wave time difference, i.e. △ tc；
The abrasiveness
Wherein Kd is the drillable-level value;
the hardness Hd is 84.109Sc +132.59, the unit is MP, and Sc is compressive strength.
5. A method for rapid bit optimization based on formation drilling resistance parameters according to claim 2 or 3, wherein: the effective stress Pe ═ Sv-alpha Pp, unit is MPa, in the formula,
α is the Biot coefficient (effective stress coefficient or pore elastic coefficient),
and CbAnd CmaVolume compressibility (MPa) of rock mass and skeleton in the measured formation-1) Is a constant;
Sv＝(∑ρb×RLEV+2.31×H0)×0.00981，ρblogging the bulk density of the rock volume; RLEV is the logging sampling interval, H0Starting depth (m) of valid data for density logging;
pp is formation pore pressure (MPa), Pp ═ Go×TVD+(Gn-Go)×HeIn the formula, G0And GnRespectively overburden pressure gradient and hydrostatic pressure gradient (MPa/m), TVD is logging vertical depth (m), HeIs the equivalent depth (m).
The brittleness index BI (%) - (Delta E + Delta PR)/2, wherein
ΔE＝(E-Emin)/(Emax-Emin) × 100, E is Young's modulus of elasticity (MPa),
Emaxand EminRespectively obtaining the maximum and minimum Young modulus of a well section corresponding to the logging curve data in the measurement, wherein the unit is GPa;
ΔPR＝(PRmax-PR)/(PRmax-PRmin) × 100, PR is the rock Poisson's ratio,
dimensionless, PRmaxAnd PRminRespectively the maximum Poisson's ratio and the minimum Poisson's ratio of the well section corresponding to the logging curve data in the measurement;
in the formula of △ ts、△tcThe time difference (us/m) of transverse wave and longitudinal wave respectively, β is 109Is a transfer factor.
6. The method for rapidly optimizing the drill bit based on the formation drilling resistance parameter as claimed in claim 1, wherein: in the step of the drill bit optimization gray clustering, known factors comprise evaluation criteria, evaluation parameters and weight; unknown factors include the drill bit type.
7. The method for rapidly optimizing the drill bit based on the formation drilling resistance parameter as claimed in claim 6, wherein: the bit optimization gray clustering step is that the gray system firstly preprocesses an input stratum sensitive drilling resistance parameter base to be selected and a standard base, the preprocessing is to take each group of sensitive drilling resistance parameters as dimensionless and normalized data columns, then extreme value weighting combination amplification is carried out on layer point standard index absolute values of each normalized data column, and gray multivariate weighting coefficients are worked out; then, adopting a comprehensive normalization method to normalize each gray multivariate weighting coefficient into a value to form a row matrix of gray weighted normalization coefficients; and finally, selecting the maximum value in the system multivariate weighted normalization vector by adopting a maximum membership principle Pmax ═ max { Cgi }, wherein the standard mode sample class attribute (namely the actually used high-efficiency drill bit class) corresponding to the maximum value is the classification result of the well section to be judged, namely the selected drill bit class.
8. The method for rapidly optimizing the drill bit based on the formation drilling resistance parameter as claimed in claim 7, wherein: normalizing the original data, and listing normalized data obtained by preprocessing the sensitive drilling-resistant parameter library of the stratum to be selected as a compared series Xi' (j) and the evaluation number sequence X of the normalized data sequence obtained by preprocessing the standard library0' (j) evaluation series X by the range standardization method0' (j) and the compared series Xi' (j) performing a data transformation,
namely, it is
In the formula, X0(j)，Xi(j) For the normalized evaluation series and compared series, n is equal to the number of known candidate bit types and M is the number of variables.
9. The method for rapidly optimizing the drill bit based on the formation drilling resistance parameter as claimed in claim 7, wherein: the gray multivariate weighting coefficient is obtained by weighting and combining and amplifying extreme values of absolute values of layer point standard indexes, and specifically, the gray multivariate weighting coefficient
As data X0And XiGray multivariate weighting factor at jth parameter, where Δ i (j) is data X0And XiThe absolute difference Δ i (j) of the criteria for the jth parameter, X0(j)-Xi(j)，
And
the minimum difference and the maximum difference of the two poles are used as standard indexes, A is a gray resolution coefficient, and Y (j) is the weight value of the jth parameter.
10. The method for rapidly optimizing the drill bit based on the formation drilling resistance parameter as claimed in claim 7, wherein: the grey multivariate weighting coefficient is classified into a value by adopting a comprehensive normalization method, and specifically, a compared array X is calculatediAnd evaluation series X0The gray-associated similarity coefficient Cgi of (c),
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Application publication date: 20201009 |
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