CN114201824B - Drill bit optimization method for fusion analysis of multi-source data - Google Patents
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Abstract
The invention relates to a drill bit optimization method for fusion analysis of multi-source data, which is characterized in that based on multi-source data such as drilled geological data, drilling data, logging data, experimental test data and the like, part of parameters capable of reflecting stratum properties are extracted to form drill bit type selection index data; and performing principal component analysis on the drill bit type selection index data by using a principal component analysis method, performing dimension reduction processing, and extracting mutually independent comprehensive indexes which account for more than 85% of the cumulative contribution rate and can represent the multi-source drill bit type selection index data to serve as final drill bit type selection index data. And taking the extracted comprehensive index as an input parameter of a neural network, taking the drill bit type with the drilling speed and the drilling footage which are both larger than the average value in the actual drilling process as label data, and establishing a neural network mathematical model for selecting the drill bit type by training and analyzing the internal relation between the comprehensive index and the drill bit type through the neural network, thereby realizing the optimization of the drill bit type of the whole well section of the adjacent well.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration, in particular to a drill bit optimization method for fusion analysis of multi-source data.
Background
During the drilling process of the oil and gas well, the drilling speed of the drill bit is determined by whether a reasonable drill bit type is selected or not when the drill bit drills into the stratum. Through the optimization of the drill bit, the type of the drill bit matched with the stratum property is selected, the mechanical drilling speed can be improved, the cost of drilling construction is reduced, meanwhile, the occurrence of underground accidents can be reduced, the safe drilling is guaranteed, and the requirement of drilling construction of an oil and gas field is met.
However, the current drilling process is complicated in formation conditions, the formation lithology, rock mechanics and drilling resistance characteristics change frequently, and the contrast of the formation between wells is poor, which is not favorable for optimizing the most reasonable drill bit type. When the single type of data is used and the drill bit type optimization is carried out by adopting a conventional method, the comprehensive analysis of the data is insufficient, the geophysical properties of the stratum cannot be comprehensively analyzed, the optimized drill bit type cannot adapt to the characteristics of the stratum, the drilling speed and the drilling footage are low in the drilling process, the drilling effect is poor, and even a series of underground complex accidents are caused.
Disclosure of Invention
The present application provides a drill bit optimization method for fusion analysis of multi-source data to solve the above technical problems.
The application is realized by the following technical scheme:
a drill bit optimization method for fusion analysis of multi-source data is characterized in that based on multi-source data such as drilled geological data, drilling data, logging data and experimental test data, part of parameters capable of reflecting stratum properties are extracted to form drill bit type selection index data.
In order to merge similar indexes, reduce useless indexes, reduce the complexity of a neural network and improve the operation speed and precision of the neural network, a principal component analysis method is used for carrying out principal component analysis on the drill bit type selection index data, dimension reduction processing is carried out, and mutually independent comprehensive indexes which account for more than 85% of the accumulated contribution rate and can represent the multi-source drill bit type selection index data are extracted and serve as final drill bit type selection index data.
And taking the extracted comprehensive index as an input parameter of a neural network, taking the drill bit type with the drilling speed and the drilling footage which are both larger than the average value in the actual drilling process as label data, and establishing a neural network mathematical model for selecting the drill bit type by training and analyzing the internal relation between the comprehensive index and the drill bit type through the neural network, thereby realizing the optimization of the drill bit type of the whole well section of the adjacent well.
Compared with the prior art, the method has the following beneficial effects:
1, compare in using single type data to develop the drill bit lectotype, this application uses multisource data as drill bit lectotype index, from the multidimension degree analysis stratum characteristic, has compensatied the defect that single type data exists, can effectively improve the compatibility of preferred drill bit and stratum, acquires the preferred drill bit type of reasonable full well interval.
According to the method, methods such as Principal Component Analysis (PCA) and neural network are used for developing data fusion analysis and establishing a drill bit model selection mathematical model, and accurate and rapid drill bit optimization can be achieved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention.
FIG. 1 is a block diagram of a neural network of the present invention;
FIG. 2 is a diagram comparing the output result of the neural network training with the actual result;
FIG. 3 is a graph of absolute error between the neural network training output and the actual result;
FIG. 4 is a graph of error variations during neural network training;
FIG. 5 is a graph comparing the output result of the neural network test with the actual result;
FIG. 6 is a graph of absolute error of neural network test output versus actual result;
FIG. 7 is a graph of error variation for a neural network test procedure;
FIG. 8 is a cross-sectional view of a full interval predicted bit type.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments. It is to be understood that the described embodiments are only a few embodiments of the present invention, and not all embodiments.
In addition, the embodiments of the present invention and the features of the embodiments may be combined with each other without conflict. The embodiments in the present specification are all described in a progressive manner, and each embodiment focuses on differences from other embodiments, and portions that are the same and similar between the embodiments may be referred to each other.
The invention discloses a drill bit optimization method for fusion analysis of multi-source data, which comprises the following steps:
s1, collecting regional geological data, well drilling data, well logging data, rock mechanics and drilling resistance characteristic parameters.
And S2, extracting all drill bit types used in drilling from the collected drilling data, and screening out the drill bit types with the drilling speed and the drilling footage larger than the average value as drill bit type selection label data. The drill bit categories are divided according to the IADC drill bit international coding rules, and different drill bit categories are numbered by using different training codes, as shown in Table 1.
Table 1: large-class numbering table for different drill bits
And S3, extracting stratum characteristic parameters of the layer positions drilled by various types of drill bits, wherein the various types refer to the drill bit types with the drilling speed and the drilling depth larger than the average value, which are screened in the step S2.
The stratigraphic characteristic parameters include geological stratification D in the geological data c (ii) a Drilling rate Z in well drilling data s Drilling fluid density rho m Drilling fluid type Z l (ii) a Lithology (after homing) Y in logging data x (ii) a Hole enlargement rate k, natural gamma GR, natural potential SP, acoustic moveout AC, shallow/deep resistivity R in logging data s /R t (ii) a Compressive strength of uniaxial UCS, cohesion C, internal friction angle
Hardness H d Value of drillability K d Abrasive G d . The extracted 17 stratum characteristic parameters are used as drill bit model selection index data together, and the text information in the data is coded by using numbers, as shown in table 2.
Table 2: literal information coding table
And S4, performing principal component analysis on the drill bit model selection index data consisting of the 17 formation characteristic parameters, and extracting principal components which have accumulated contribution rates reaching a preset proportion and can represent the first n mutually independent terms of the drill bit model selection index data to serve as a drill bit model selection comprehensive index.
In this embodiment, the top 7 mutually independent principal components with the cumulative contribution rate of 85.85% are extracted as the drill selection comprehensive index. Wherein, the first main component can represent hardness, uniaxial compressive strength, grindability, drillability level value, sound wave time difference and cohesion; the second main component can represent the density, geological stratification, lithology and drilling fluid type of the drilling fluid; the third principal component can characterize shallow resistivity; the fourth main component can represent the internal friction angle; the fifth principal component can characterize deep resistivity and natural gamma; the sixth main component can represent the expanding rate and the natural potential; the seventh principal component may characterize the rate of penetration, as shown in table 3.
Table 3: principal component analysis results table
In another embodiment, the cumulative contribution rate may be other values.
S5, drill bit type label data and 7 mutually independent drill bit type selection comprehensive index data are subjected to normalization processing, the 7 mutually independent drill bit type selection comprehensive index data subjected to normalization processing serve as neural network input data, the drill bit type label data subjected to normalization processing serve as neural network output data, and the data are divided into a training data set and a verification data set respectively.
S6, constructing a drill bit model selection neural network, wherein the neural network selects a deep belief neural network (DBN), and the number of network layers and the number of nodes are respectively set as follows according to the actual conditions of input parameters: the number of the limited Boltzmann machines is 2, the number of nodes of a network input layer is set to be 7, the number of nodes of a hidden layer of a layer 1 is set to be 28, the number of nodes of a hidden layer of a layer 2 is set to be 14, and the number of nodes of an output layer is set to be 1. Setting the training frequency of each restricted Boltzmann machine to 200000 times, setting the random number of samples at each time as the total number of training samples, setting the learning rate to 0.05, setting the reverse fine-tuning layer training function to sigmoid, setting the training frequency to 200000 times, setting the reverse fine-tuning layer activation function to logsig, and setting the reverse fine-tuning layer error calculation function to traingdx. The specific network structure is shown in fig. 1.
And S7, inputting the training data set into a neural network to complete network training, and obtaining a trained drill bit model selection neural network model after the network continuously iterates to reach specified precision. The network training end state, the training result and the prediction result are shown in fig. 2-7.
S8, inputting a verification data set into the trained drill bit model selection neural network model, counting 52 drill bit type sample points of 5 wells, obtaining the drill bit type predicted by the neural network, and comparing with the drill bit type actually used, as shown in Table 4. As can be seen from Table 4, the predicted drill bit type has good compatibility, and the prediction precision reaches 100%, which illustrates the feasibility of carrying out drill bit type selection by fusing and analyzing multi-source data.
Table 4: neural network prediction drill bit type and actual practical drill bit type comparison table
| NO. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
| Well name | Well A | Well A | Well A | Well A | Well A | Well A | Well A | Well A | Well A | Well A | Well A | Well A | Well A |
| Code of |
1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
| |
1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 3 | 3 | 2 | 2 |
| Results | Correction of | Correction of | Correction of | Correction of | Correction of | Is accurate to | Correction of | Correction of | Correction of | Correction of | Correction of | Correction of | Correction of |
| NO. | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 |
| Well name | Well B | B well | Well B | Well B | Well B | Well B | B well | Well B | Well B | B well | C well | C well | C well |
| Code of |
3 | 3 | 2 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| |
3 | 3 | 2 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| Results | Is accurate to | Correction of | Is accurate to | Correction of | Is accurate to | Correction of | Correction of | Correction of | Is accurate to | Correction of | Correction of | Correction of | Correction of |
| NO. | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 |
| Well name | C well | C well | C well | C well | C well | C well | C well | C well | D well | D well | D well | D well | D well |
| Code of |
3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 2 | 2 | 1 | 2 |
| |
3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 2 | 2 | 1 | 2 |
| Results | Correction of | Correction of | Correction of | Correction of | Correction of | Correction of | Correction of | Correction of | Is accurate to | Is accurate to | Correction of | Correction of | Correction of |
| NO. | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 |
| Well name | D well | D well | E well | E well | E well | E well | E well | E well | E well | E well | E well | E well | E well |
| Code of |
3 | 3 | 5 | 5 | 1 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| |
3 | 3 | 5 | 5 | 1 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| Results | Correction of | Correction of | Correction of | Correction of | Is accurate to | Correction of | Correction of | Correction of | Correction of | Correction of | Is accurate to | Correction of | Correction of |
S9, inputting relevant stratum characteristic parameters of the drill type well to be predicted into the trained drill type selection neural network model, and obtaining the predicted drill type of the whole well section of the well by combining fusion analysis multi-source data and a neural network method, as shown in figure 3.
This application uses multisource data as drill bit lectotype index, from multidimension degree analysis stratum characteristic, can effectively improve the compatibility of preferred drill bit and stratum, acquires the preferred drill bit type of reasonable full well section.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A drill bit optimization method for fusion analysis of multi-source data is characterized by comprising the following steps: the method comprises the following steps:
collecting data, wherein the collected data comprises geological data, drilling data, logging data, rock mechanics and drilling resistance characteristic parameters;
extracting all drill bit types used in drilling from the collected data, and screening out the drill bit types with the drilling speed and the footage larger than the average value as drill bit type selection label data;
extracting stratum characteristic parameters of the screened layer position drilled by the drill bit from the collected data, and forming drill bit type selection index data by the stratum characteristic parameters;
performing principal component analysis on the drill bit model selection index data, and extracting principal components which have accumulated contribution rates reaching a preset proportion and can represent the first n mutually independent items of the drill bit model selection index data as drill bit model selection comprehensive indexes;
establishing a drill bit model selection neural network model by adopting a neural network algorithm based on the drill bit model selection label data and the drill bit model selection comprehensive index data;
and inputting the related stratum characteristic parameters of the drill type well to be predicted into the drill type selection neural network model to obtain the predicted drill type of the whole well section of the well.
2. The drill bit-optimized method for fusion analysis of multi-source data of claim 1, wherein: the drill bit selection indicator data comprises: geological stratification
Drilling speed
Density of drilling fluid
Drilling fluid type
Lithology of
Expansion ratio k, natural gamma GR, natural potential SP, sonic time difference AC, shallow resistivity
Deep resistivity
Uniaxial compressive strength UCS, cohesion C, internal friction angle phi, hardness
Value of drillability
Abrasive property of
。
3. The bit-optimized method for fusion analysis of multi-source data according to claim 1 or 2, wherein: and extracting the main components of the first 7 items which are independent from each other and have the cumulative contribution rate of 85.85 percent to serve as the comprehensive index of drill bit model selection.
4. The bit-optimized method for fusion analysis of multi-source data of claim 3, wherein: the first main component can represent hardness, uniaxial compressive strength, grindability, drillability level value, sound wave time difference and cohesion;
the second main component can represent the density, geological stratification, lithology and drilling fluid type of the drilling fluid;
the third main component can represent shallow resistivity;
the fourth main component can represent the internal friction angle;
the fifth principal component can characterize deep resistivity and natural gamma;
the sixth main component can represent the expanding rate and the natural potential;
the seventh principal component may characterize rate of penetration.
5. The bit-optimized method for fusion analysis of multi-source data according to claim 1, 2 or 4, wherein: carrying out normalization processing on the drill bit type label data and the drill bit type selection comprehensive index data;
and training the constructed drill bit model selection neural network by taking the drill bit model selection comprehensive index data after normalization processing as neural network input data and the drill bit model label data after normalization processing as neural network output data to obtain the drill bit model selection neural network model.
6. The bit-optimizing method for fusion analysis of multi-source data of claim 5, wherein: the neural network is a deep belief neural network.
7. The bit-optimizing method for fusion analysis of multi-source data of claim 6, wherein: the number of network layers and the number of nodes of the neural network are respectively set according to the actual condition of the input parameters, and the method specifically comprises the following steps:
the number of the restricted Boltzmann machines is 2, the number of nodes of the input layer of the network is set to be 7, the number of nodes of the hidden layer of the 1 st layer is set to be 28, the number of nodes of the hidden layer of the 2 nd layer is set to be 14, and the number of nodes of the output layer is set to be 1.
8. The bit-optimizing method for fusion analysis of multi-source data of claim 7, wherein: the training times of each limited Boltzmann machine are set to 200000 times, the number of random samples at each time is the total number of training samples, the learning rate is 0.05, the reverse fine-tuning layer training function is sigmoid, the training times are 200000 times, the reverse fine-tuning layer activation function is logsig, and the reverse fine-tuning layer error calculation function is tractindxx.
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