CN115510736A - Multi-model drilling speed prediction method and device for small drilling machine based on stratum lithology classification - Google Patents

Multi-model drilling speed prediction method and device for small drilling machine based on stratum lithology classification Download PDF

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CN115510736A
CN115510736A CN202211016860.5A CN202211016860A CN115510736A CN 115510736 A CN115510736 A CN 115510736A CN 202211016860 A CN202211016860 A CN 202211016860A CN 115510736 A CN115510736 A CN 115510736A
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甘超
牛帅康
曹卫华
汪发文
徐俊
张思敏
张少锋
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Hubei Urban Geological Engineering Institute
China University of Geosciences
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Abstract

The invention discloses a multi-model drilling speed prediction method and equipment for a small-sized drilling machine based on stratum lithology classification, which consider the effect of stratum lithology, which is an important factor reflecting the drilling environment of the small-sized drilling machine, on the prediction of drilling speed, establish drilling speed prediction models of various lithologies by introducing stratum lithology information, and perform the prediction in three stages, wherein in the first stage, data are screened and resampled according to the practical engineering application experience, and then the data are processed through wavelet filtering and normalization; in the second stage, three typical lithologies are selected from the drilling data, and lithology classification is carried out by utilizing real-time drilling data; in the third stage, classified drilling data are imported into drilling speed models of corresponding lithology for prediction by using the lithology classification result of the second stage, a good foundation is laid for intelligent control research of the drilling process of the small-sized drilling machine, the problem that a single drilling speed model is not high in adaptability to different lithologies of different stratums is solved, and the drilling speed prediction precision is improved.

Description

Multi-model drilling speed prediction method and device for small drilling machine based on stratum lithology classification
Technical Field
The invention relates to the technical field of geological drilling, in particular to a multi-model drilling speed prediction method and multi-model drilling speed prediction equipment for a small-sized drilling machine based on stratum lithology classification.
Background
In the current stage, the drilling construction of the small-sized drilling machine mainly takes manual experience as a main part, the quality and the efficiency of geological investigation and scientific research work are further improved, the geological work is promoted to be developed towards an accurate, rapid and comprehensive direction, and the development of a high-end intelligent drilling technology plays an important role in geological exploration and development of the small-sized drilling machine. The drilling rate is a key index for measuring the drilling efficiency, and accurate drilling rate prediction has great significance for the drilling process, but the drilling process of a small-sized drilling machine has the defects of complex geomechanical environment, prominent characteristics of nonlinearity, strong coupling, strong interference and the like, and low accuracy of a drilling rate model. Therefore, establishing a high-precision drilling rate model is an important basis for optimizing the efficiency of the drilling process of the small-sized drilling machine.
Disclosure of Invention
In order to solve the problems, the invention provides a multi-model drilling speed prediction method and equipment of a small-sized drilling machine based on stratum lithology classification, and the multi-model drilling speed prediction method of the small-sized drilling machine based on stratum lithology classification mainly comprises the following steps:
s1: according to the practical engineering application experience, well depth, bit pressure, rotating speed, torque, vertical pressure and drilling speed data are screened and resampled, and then the data are processed through wavelet filtering and normalization;
s2: dividing the data of the drilling pressure, the rotating speed, the torque, the vertical pressure and the drilling speed corresponding to the lithology of the stratum into a training set, a verification set and a test set after the data of the drilling pressure, the rotating speed, the torque, the vertical pressure and the drilling speed are processed in the step S1; establishing a lithology classification model based on an extreme learning machine and a four-fold cross validation method, training the lithology classification model by utilizing a training set and a validation set, wherein the input is the bit pressure, the rotating speed, the torque, the vertical pressure and the drilling speed, and the output is the lithology;
s3: dividing well depth, drilling pressure and rotating speed data corresponding to formation lithology into a training set, a verification set and a test set, establishing a drilling speed prediction model of each lithology based on an extreme learning machine and a ten-fold cross verification method, and training the drilling speed prediction model by using the training set and the verification set data, wherein the input is the well depth, the drilling pressure and the rotating speed, and the output is the predicted drilling speed;
s4: integrating the trained lithology classification model and the corresponding drilling speed prediction model to form a drilling speed prediction model of the small-sized drilling machine based on stratum lithology classification;
s5: and carrying out simulation verification on the drilling speed prediction method by using the test set data, and applying a drilling speed prediction model of the small-sized drilling machine reaching preset verification precision to actual engineering to obtain the actual predicted drilling speed.
Further, step S1 specifically includes the following processes:
1) According to the practical engineering experience, under the condition that the well depth is equal to the drilling position, the drilling pressure, the rotating speed and the drilling speed are subjected to data screening, and the data screening needs to meet the following conditions: the drilling pressure is more than 0 (t), (2) the rotating speed is more than 10 (r/min), (3) the drilling speed is more than 0 (m/h) and less than 6 (m/h); resampling the data according to a set interval;
2) Filtering the drilling data by adopting a wavelet threshold denoising method for the bit pressure, the rotating speed, the torque, the vertical pressure and the drilling speed according to the following formula:
Figure BDA0003812869010000021
wherein a is a scale factor corresponding to the time t, b is a displacement variation corresponding to the time t,
Figure BDA0003812869010000022
is a wavelet basis function.
3) The bit pressure, the rotating speed, the torque, the vertical pressure and the drilling speed are normalized according to the following formula, so that the magnitude of each index is the same:
Figure BDA0003812869010000023
wherein x * Is the normalized data set, x is the true data set, min (x) is the smallest data in the data set, and max (x) is the largest data in the data set.
Further, the set interval in step 1) is 0.01m.
Further, step S2 specifically includes the following processes:
1) Obtaining a predicted value of the lithology of the stratum by using an extreme learning machine method according to the following formula;
Figure BDA0003812869010000024
T＝H·β
where g (x) is the activation function, ω i As input weights, b i For bias, T is the output matrix, β is the output weight, H is the hidden layer output matrix, i =1,2, 3.
2) And determining the input weight omega and the deviation b of the model to be established by using a four-fold cross verification method, and further establishing a lithology classification model.
Further, step S3 specifically includes the following processes:
1) Similarly, the drilling speed is predicted by using an extreme learning machine method;
2) And determining the input weight omega and the deviation b of each model to be established by using a ten-fold cross-validation method, and further establishing a drilling speed prediction model of each lithology.
3) And the prediction result of the lithology classification model is used as a switching condition of the drilling speed prediction model to complete the integration of the lithology classification model and the corresponding drilling speed prediction model, so that the drilling data at the same stage is appointed to be input to the drilling speed model corresponding to the lithology according to the lithology classification result as a judgment condition to predict the drilling speed, and the data meeting the corresponding lithology can be input to the corresponding drilling speed model.
Further, in the simulation verification in step S5, a calculation formula of the verification index of the test set is as follows:
Figure BDA0003812869010000031
Figure BDA0003812869010000032
where RMSE is the root mean square error, NRMSE is the normalized root mean square error, y i Is the measurement data that is to be measured,
Figure BDA0003812869010000033
is the prediction data, and n is the number of samples.
A multi-model drilling speed prediction device of a small-sized drilling machine based on stratum lithology classification comprises the following components: a processor and a storage device; and the processor loads and executes the instructions and the data stored in the storage device to realize the multi-model drilling speed prediction method of the small-sized drilling machine based on the stratum lithology classification.
The technical scheme provided by the invention has the beneficial effects that:
(1) According to the multi-model drilling speed prediction method of the small-sized drilling machine based on the stratum lithology classification, preprocessing operations such as screening, resampling, wavelet filtering and normalization are firstly carried out on drilling data, so that the data quality can be effectively improved, and a good foundation is laid for subsequent lithology classification and drilling speed prediction modeling work;
(2) The invention relates to a multi-model drilling speed prediction method of a small-sized drilling machine based on stratum lithology classification, which adopts an extreme learning machine algorithm, uses training set and verification set data, and uses a four-fold cross verification method to determine hyper-parameters of a model and establish a classification model of stratum lithology, wherein the model can use real-time drilling data to achieve the purpose of predicting the stratum lithology;
(3) The invention discloses a multi-model drilling speed prediction method of a small-sized drilling machine based on stratum lithology classification. And then, the test set data is used for simulating and verifying the model prediction effect, thereby being beneficial to the application of the method in actual production.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a multi-model drilling rate prediction method for a small-sized drilling machine based on formation lithology classification in an embodiment of the invention.
FIG. 2 is a schematic illustration of a pre-processing of noise reduction of drilling data in an embodiment of the present invention.
FIG. 3 is a schematic diagram of N-fold cross validation in an embodiment of the present invention.
FIG. 4 is a diagram of results of lithology classification of a test suite small drill rig in an embodiment of the present invention.
FIG. 5 is a graph comparing predicted drilling rates of small-sized drilling machines according to embodiments of the invention.
Fig. 6 is a schematic diagram of the operation of the hardware device according to the embodiment of the present invention.
Detailed Description
For a more clear understanding of the technical features, objects, and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
The embodiment of the invention provides a multi-model drilling speed prediction method and equipment for a small-sized drilling machine based on stratum lithology classification, wherein the small-sized drilling machine is used for drilling within 500 m.
Referring to fig. 1, fig. 1 is a flowchart of a multi-model drilling rate prediction method for a small-sized drilling machine based on stratum lithology classification according to an embodiment of the present invention, in which a modeling process is divided into three stages, and in the first stage, data is screened and resampled according to practical engineering application experience. Then, processing the data through wavelet filtering and normalization; the second stage is based on three typical lithologies, including lithology A (limestone), lithology B (carbonaceous dolomite) and lithology C (carbonaceous limestone), selecting corresponding drilling data, dividing well depth, drilling pressure, rotating speed, torque, vertical pressure and drilling speed data into a training set and a testing set, adopting four-fold cross validation, dividing the training set into 4 parts, wherein 3 parts are new training sets, 1 part is a validation set, using an extreme learning machine algorithm, inputting the well depth, the drilling pressure, the rotating speed, the torque, the vertical pressure and the drilling speed as models, and outputting prediction results of the lithology in 3 parts as models to establish a lithology classification model; in the third stage, training set data is adopted, 10 equal parts of the training set data of each lithology is divided through a ten-fold cross validation method, wherein 9 parts of the training set data are new training sets, 1 part of the training set data are validation sets, well depth, drilling pressure and rotating speed are used as model input through an extreme learning machine algorithm, a drilling rate predicted value is used as model output, a drilling rate prediction model corresponding to each lithology is established, and the lithology classification model and the corresponding drilling rate prediction model are integrated to form a drilling rate prediction model for stratum lithology classification based on real-time drilling process parameters; finally, simulation verification is performed by using the test set data. The method comprises the following specific steps:
(1) The drilling data is preprocessed, the comparison results before and after data denoising are shown in each graph in fig. 2, and the training set and validation set data based on lithology classification are shown in table 1:
TABLE 1 training and validation set lithology samples
Name(s) Number of
Lithology A 14
Lithology B 22
Lithology C 5
(2) The training and verification processes of lithology classification and drilling rate prediction are shown in fig. 3, and the four-fold cross validation method and the ten-fold cross validation method are respectively used for determining the model hyperparameters in the lithology classification and drilling rate prediction training processes.
(3) And (3) testing the lithology classification model of the small drilling machine by using the test set data, wherein the finally obtained lithology classification precision can reach 74.21% as shown in figure 4.
(4) The test results of the drilling speed prediction of the small-sized drilling machine by using the test set are shown in the table 2 and the figure 5, and the comparison methods mainly comprise three methods, namely the method provided by the invention, the drilling speed prediction of the extreme learning machine without lithology classification and the support vector regression drilling speed prediction of the lithology classification by using the extreme learning machine. Test results show that the model precision and the generalization ability of the method provided by the invention have optimal effects in three comparison methods, and the method is favorable for application in actual production.
TABLE 2 drilling speed prediction accuracy comparison for small-sized drilling machine
Method RMSE NRMSE
The method mentioned 67.59％ 21.21％
Extreme learning machine drilling speed prediction without lithology classification 74.63％ 23.42％
Extreme learning machine lithology classification + support vector regression drilling rate prediction 72.33％ 22.70％
The invention respectively establishes the drilling rate prediction model of each lithology by fully considering different lithologies of different stratums to have different influences on the same drilling rate prediction model, classifies the lithology by utilizing real-time drilling data, and integrates the lithology classification and the drilling rate prediction model, thereby ensuring that the lithology information is obtained in real time and the drilling rate model obtains higher modeling precision and stronger generalization capability.
Referring to fig. 6, fig. 6 is a schematic diagram of a hardware device according to an embodiment of the present invention, where the hardware device specifically includes: a multi-model drilling rate prediction device 401, a processor 402 and a storage device 403 of a small-sized drilling machine based on formation lithology classification.
A multi-model drilling rate prediction device 401 for a small-sized drilling machine based on formation lithology classification: the multi-model drilling speed prediction device 401 for the small-sized drilling machine based on the stratum lithology classification realizes the multi-model drilling speed prediction method for the small-sized drilling machine based on the stratum lithology classification.
The processor 402: the processor 402 loads and executes instructions and data in the storage device 403 for realizing the multi-model drilling speed prediction method of the small-sized drilling machine based on the formation lithology classification.
The storage device 403: the storage device 403 stores instructions and data; the storage device 403 is used for implementing the multi-model drilling speed prediction method for the small-sized drilling machine based on the formation lithology classification.
The beneficial effects of the invention are:
(1) According to the multi-model drilling speed prediction method of the small-sized drilling machine based on the stratum lithology classification, preprocessing operations such as screening, resampling, wavelet filtering and normalization are firstly carried out on drilling data, the data quality can be effectively improved, and a good foundation is laid for subsequent lithology classification and drilling speed prediction modeling work;
(2) The invention relates to a multi-model drilling speed prediction method of a small-sized drilling machine based on stratum lithology classification, which adopts an extreme learning machine algorithm, uses training set and verification set data, and uses a four-fold cross verification method to determine the hyper-parameters of a model and establish a classification model of stratum lithology, wherein the model can use real-time drilling data to achieve the purpose of predicting the stratum lithology;
(3) The invention discloses a multi-model drilling speed prediction method of a small-sized drilling machine based on stratum lithology classification. And then, the test set data is used for simulating and verifying the model prediction effect, thereby being beneficial to the application of the method in actual production.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (7)

1. A multi-model drilling speed prediction method of a small-sized drilling machine based on stratum lithology classification is characterized by comprising the following steps: the method comprises the following steps:
s1: according to practical engineering application experience, screening and resampling well depth, bit pressure, rotating speed, torque, vertical pressure and drilling speed data, and then processing the data through wavelet filtering and normalization;
s2: dividing the data of the drilling pressure, the rotating speed, the torque, the vertical pressure and the drilling speed corresponding to the lithology of the stratum into a training set, a verification set and a test set after the data of the drilling pressure, the rotating speed, the torque, the vertical pressure and the drilling speed are processed in the step S1; establishing a lithology classification model based on an extreme learning machine and a four-fold cross validation method, training the lithology classification model by utilizing a training set and a validation set, wherein the input is the bit pressure, the rotating speed, the torque, the vertical pressure and the drilling speed, and the output is the lithology;
s3: dividing well depth, drilling pressure and rotating speed data corresponding to formation lithology into a training set, a verification set and a test set, establishing a drilling speed prediction model of each lithology based on an extreme learning machine and a ten-fold cross verification method, and training the drilling speed prediction model by using the training set and the verification set data, wherein the input is the well depth, the drilling pressure and the rotating speed, and the output is the predicted drilling speed;
s4: integrating the trained lithology classification model and the corresponding drilling speed prediction model to form a drilling speed prediction model of the small-sized drilling machine based on stratum lithology classification;
s5: and carrying out simulation verification on the drilling speed prediction method by using the test set data, and applying a drilling speed prediction model of the small-sized drilling machine reaching preset verification precision to actual engineering to obtain the actual predicted drilling speed.
2. The multi-model drilling speed prediction method for the small-sized drilling machine based on the stratum lithology classification as claimed in claim 1, characterized in that: the step S1 specifically includes the following processes:
1) According to actual engineering experience, under the condition that the well depth is equal to the drilling position, data screening is carried out on the drilling pressure, the rotating speed and the drilling speed, and the following conditions are met: the drilling pressure is more than 0 (t), (2) the rotating speed is more than 10 (r/min), (3) the drilling speed is more than 0 (m/h) and less than 6 (m/h); resampling the data according to a set interval;
2) Filtering the drilling data by adopting a wavelet threshold denoising method for the bit pressure, the rotating speed, the torque, the vertical pressure and the drilling speed according to the following formula:
Figure FDA0003812862000000011
wherein a is a scale factor corresponding to the time t, b is a displacement variation corresponding to the time t,
Figure FDA0003812862000000012
is a wavelet basis function.
3) The bit pressure, the rotating speed, the torque, the vertical pressure and the drilling speed are normalized according to the following formula, so that the magnitude of each index is the same:
Figure FDA0003812862000000021
wherein x * Is the normalized data set, x is the true data set, min (x) is the smallest data in the data set, and max (x) is the largest data in the data set.
3. The method for predicting the multi-model drilling speed of the small-sized drilling machine based on the formation lithology classification as claimed in claim 2, wherein the method comprises the following steps: the set interval in step 1) is 0.01m.
4. The multi-model drilling speed prediction method for the small-sized drilling machine based on the stratum lithology classification as claimed in claim 1, characterized in that: step S2 specifically includes the following processes:
1) Obtaining a predicted value of the lithology of the stratum by using an extreme learning machine method according to the following formula;
Figure FDA0003812862000000022
T＝H·β
where g (x) is the activation function, ω i As input weights, b i For bias, T is the output matrix, β is the output weight, H is the hidden layer output matrix, i =1,2, 3.
2) And determining the input weight omega and the deviation b of the model to be established by using a four-fold cross verification method, and further establishing a lithology classification model.
5. The method for predicting the multi-model drilling speed of the small-sized drilling machine based on the formation lithology classification as claimed in claim 1, wherein the method comprises the following steps: step S3 specifically includes the following processes:
1) Similarly, the drilling speed is predicted by using an extreme learning machine method;
2) And determining the input weight omega and the deviation b of each model to be established by using a ten-fold cross-validation method, and further establishing a drilling rate prediction model of each lithology.
3) And the prediction result of the lithology classification model is used as a switching condition of the drilling speed prediction model to complete the integration of the lithology classification model and the corresponding drilling speed prediction model, so that the drilling data at the same stage is appointed to be input to the drilling speed model corresponding to the lithology according to the lithology classification result as a judgment condition to predict the drilling speed, and the data meeting the corresponding lithology can be input to the corresponding drilling speed model.
6. The method for predicting the multi-model drilling speed of the small-sized drilling machine based on the formation lithology classification as claimed in claim 1, wherein the method comprises the following steps: in the simulation verification in step S5, the calculation formula of the verification index of the test set is as follows:
Figure FDA0003812862000000031
Figure FDA0003812862000000032
where RMSE is the root mean square error, NRMSE is the normalized root mean square error, y i Is the data that is to be measured and,
Figure FDA0003812862000000033
is the prediction data, and n is the number of samples.
7. The method for predicting the multi-model drilling speed of the small-sized drilling machine based on the formation lithology classification as claimed in claim 1, wherein the method comprises the following steps: the method comprises the following steps: a processor and a storage device; the processor loads and executes the instructions and data stored in the storage device to realize the multi-model drilling speed prediction method of the small-sized drilling machine based on the stratum lithology classification, which is disclosed by any one of claims 1 to 6.
CN202211016860.5A 2022-08-24 2022-08-24 Multi-model drilling speed prediction method and device for small drilling machine based on stratum lithology classification Pending CN115510736A (en)

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