China National Science and Technology Major Project. 2016ZX05020-006
Abstract
Considering the stratum anti-drilling ability, drill bit working conditions, drill bit application effect and drill bit economic benefits, the similarity of stratum anti-drilling ability was evaluated by grey relational analysis theory to screen out candidate drill bits with reference values. A new comprehensive performance evaluation model of drill bit was established by constructing the absolute ideal solution, changing the relative distance measurement method, and introducing entropy weight to work out the closeness between the candidate drill bits and ideal drill bits and select the reasonable drill bit. Through the construction of absolute ideal solution, improvement of relative distance measurement method and introduction of entropy weight, the inherent defects of TOPSIS decision analysis method, such as non-absolute order, reverse order and unreasonable weight setting, can be overcome. Simple in calculation and easy to understand, the new bit selection method has good adaptability to drill bit selection using dynamic change drill bit database. Field application has proved that the drill bits selected by the new drill bit selection method had significant increase in average rate of penetration, low wear rate, and good compatibility with the drilled formations in actual drilling. This new method of drill bit selection can be used as a technical means to select drill bits with high efficiency, long life and good economics in oilfields.
Keywords：drill bit selection
;
stratum anti-drilling ability
;
grey relational analysis
;
absolutely ideal solution
;
relative distance measurement method
;
entropy weight
;
comprehensive performance of drill bit
YAN Tie, XU Rui, SUN Wenfeng, LIU Weikai, HOU Zhaokai, YUAN Yuan, SHAO Yang. Similarity evaluation of stratum anti-drilling ability and a new method of drill bit selection. [J], 2021, 48(2): 450-459 doi:10.1016/S1876-3804(21)60036-8
Introduction
The drill bit is an important tool for breaking rock to form a borehole during the drilling process. Selecting a high-quality drill bit is the key to reducing drilling costs and improve drilling efficiency[1]. For a long time, drilling engineers have selected drill bit by two methods. The first method is to select a suitable drill bit according to the stratum anti-drilling ability[2]. In this method, lab experiment or acoustic logging data are used to find out drillability of formations, and the type of drill bit is selected according to the drill bit IADC (International Association Drilling Contractor) code[3]. For example, the parameters such as shear strength and compressive strength of rock are taken as the basis for type selection of PDC (Polycrystalline Diamond Compact) drill bit[4,5]; fractal theory is used to describe and establish the statistical relationship between the dimension of stratigraphic sedimentary sequence and the working performance of drill bit, and the type of drill bit is selected by calculating the fractal dimension[6]. This method has certain guiding significance for the selection of drill bits according to the stratum anti-drilling ability. However, in practical application, there are many types of drill bits (or from different manufacturers) suitable for a certain stratum, so it is still difficult to select the appropriate model of drill bit. Moreover, when the stratum anti-drilling ability is unknown, this method can’t be used. The second method is to evaluate the application effect of the drill bits based on the drill bit data of the adjacent area and select the drill bit suitable for the formation to be drilled[7]. For example, Toczek et al.[8] defined Specific Energy (SE, the work done by a drill bit to drill a unit volume of rock) to evaluate a drill bit's drilling effect. Rabia et al.[9] proposed the Cost Per Foot Method (CPF) for drill bit selection. Yang Jin et al.[10] improved the Cost Per Foot Method (CPF) and put forward the drill bit benefit index. Based on the real-time data of drilling fluid and logging instrument, Momeni et al.[11] predicted the expected ROP (rate of penetration) under specific drilling parameters and selected the drill bit with the maximum expected ROP. Sun et al.[12] took the drill bit's dullness as an evaluation index and selected the drill bit with the smallest change in dullness within the same time. This kind of method fully considers the working performance of the drill bit. If the designed well differ greatly in geological conditions from the drilled well, it is difficult to obtain the ideal result of drill bit selection. Moreover, this kind of method is blind for the new area with little or no data on drill bit use.
At present, drill bit selection using machine learning technology has developed rapidly. For example, Zhao Tingfeng et al.[13] used the principal component analysis method to find out the key indexes affecting the drill bit selection from the indexes such as ROP, footage, WOB (Weight on the bit), and RPM (revolutions per minute) and calculated the "comprehensive index" to select the drill bit. Based on the principle of fuzzy mathematics, Deng Rong et al.[14] proposed a multi-factor fuzzy comprehensive evaluation method for drill bit selection. Sui Sheng et al.[15] established a drill bit selection model based on factor analysis theory. Bilgesu et al.[16] designed a three- layer feedback neural network model to select the drill bit. You et al.[17] used the grey relational analysis method to evaluate the drill bit's use effect based on correlation degree. Momeni et al.[18] proposed a back-propagation artificial neural network model with two implicit units for drill bit selection. Edalatkhah et al.[19] worked out a prediction model for drill bit selection by combining an artificial neural network model with genetic algorithm.
The drill bit selection with machine learning combines the stratum anti-drilling ability with the drill bit's actual drilling effect organically, and makes scientific use of available drilling data. This method is fast and high in efficiency. But the existing methods have a strong dependence on the selected sample data, and are complex in calculation, which makes them difficult for geologists to understand. When there is a drill bit selection error, manual intervention is challenging. How to better apply machine learning to drill bit selection is still a problem needs further investigation. In this paper, a new drill bit selection method based on grey correlation analysis theory has been proposed by comprehensively considering the stratum anti-drilling ability, working conditions, application effect and economic benefit of drill bit. In this method, a new evaluation model of bit’s comprehensive performance has been worked out by constructing the absolution ideal solution, changing the relative distance measurement method and introducing entropy weight; the close degrees of the candidate bits and the ideal bit are calculated to select the proper drill bit.
1. Similarity evaluation of stratum anti-drilling ability
The stratum anti-drilling ability is the main factor affecting the application effect of the drill bit. To select drill bit correctly, we must have full understandings on the formation's petrophysical and mechanical properties. Traditional drill bit selection methods mainly refer to the parameters of drill bits used in the drilled formations same or similar to the formation to be drilled. But in practical application, the anti-drilling ability of even the same stratum can vary significantly in different wells, so it is not completely reliable to select the drill bit according to the reference formation. The correct method is to establish the similarity relationship between the anti- drilling ability of drilled stratum and that of the stratum to be drilled, and ensure that the drilled stratum and the stratum to be drilled have high similarity in anti-drilling ability, before selecting drill bit. This method not only expands the scope of drill bit selection and but also has more reference value and practical significance. Grey correlation analysis theory can not only evaluate the similarity of the stratum anti-drilling ability but also exclude the drill bit data quite different from the formation to be drilled.
1.1. Sample data preprocessing
There is a large amount of data in the original drill bit record database, including the data of drill bit pulling of the hole due to drilling to the completion layer, drilling to the coring layer, equipment replacement, and drilling accidents (percussion drill, drilling string free fall, nozzle dropping, and bit balling), etc. These abnormal use data cannot truly reflect the actual application effect of the drill bit. To eliminate effect of these data on the drill bit selection results, this kind of data not suitable for drill bit selection must be excluded.
1.2. Grey correlation analysis
Considering the influences of strength characteristics, deformation characteristics, and surface characteristics of formation on drill bit selection, compressive strength, shear strength, internal friction angle, drillability, and hardness are selected as evaluation indexes for the similarity of stratum anti-drilling ability. Grey correlation analysis theory is used to calculate the correlation coefficient and grey correlation degrees of the anti-drilling ability parameters between the formation to be drilled and the formation drilled[20]. The formation with a higher grey correlation degree (grey correlation degree greater than or equal to 0.6) is selected as the similar formation of the formation to be drilled, and the drill bit used in the similar formation is taken as the candidate drill bit. The specific process includes four steps.
(1) Determine the analysis data column: N stratum anti- drilling ability parameters of the formation to be drilled are set as a reference data column $Y=$ $\left\{ y\left( k \right)\left| k=1,2,\cdots ,N \right. \right\}$. N stratum anti-drilling ability parameters of M drilled formations are set as a comparison data column${{X}_{t}}=\left\{ {{X}_{t}}\left( k \right)\left| k=1,2,\cdots ,N;t=1,2,\cdots ,M \right. \right\}$.
(2) Data dimensionless: the Z-score standardized method is used for the dimensionless processing of sample data[21].
The reference data column:
$y\left( k \right)=\frac{Y\left( k \right)-S(k)}{\sqrt{\frac{{{\left[ Y\left( k \right)-S(k) \right]}^{2}}+\sum\limits_{t=1}^{M}{{{\left[ {{X}_{t}}\left( k \right)-S(k) \right]}^{2}}}}{M}}}$
where
$S(k)=\frac{1}{M+1}\left[ Y\left( k \right)+\sum\limits_{t=1}^{M}{{{X}_{t}}\left( k \right)} \right]$
The comparison data column:
${{x}_{t}}\left( k \right)=\frac{{{X}_{t}}\left( k \right)-S(k)}{\sqrt{\frac{{{\left[ Y\left( k \right)-S(k) \right]}^{2}}+\sum\limits_{t=1}^{M}{{{\left[ {{X}_{t}}\left( k \right)-S(k) \right]}^{2}}}}{M}}}$
(3) Calculate correlation coefficient:
${{\zeta }_{t}}\left( k \right)=\frac{\underset{t}{\mathop{\min }}\,\underset{k}{\mathop{\min }}\,{{\Delta }_{t}}\left( k \right)+\rho \underset{t}{\mathop{\max }}\,\underset{k}{\mathop{\max }}\,{{\Delta }_{t}}\left( k \right)}{{{\Delta }_{t}}\left( k \right)+\rho \underset{t}{\mathop{\max }}\,\underset{k}{\mathop{\max }}\,{{\Delta }_{t}}\left( k \right)}$
${{\Delta }_{t}}\left( k \right)=\left| y\left( k \right)-{{x}_{t}}\left( k \right) \right|$
In the formula, the smaller the resolution coefficient (ρ), the better the resolution, and the resolution coefficient is usually taken as 0.5.
(4) Calculation of grey correlation degree:
${{R}_{\text{G},t}}=\frac{1}{N}\sum\limits_{k=1}^{N}{{{\zeta }_{t}}\left( k \right)}$
The greater the grey correlation degree of anti-drilling ability between the formation to be drilled and the formation drilled, the higher the similarity degree of anti- drilling ability between them.
2. Comprehensive performance evaluation model of drill bit
TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method[22] is an effective method commonly used in multi-attribute decision analysis, which evaluates the evaluation object according to the close degree of the evaluation object to the idealized target. However, this method has some inherent defects, such as no absolute ordering, reverse ordering (using the TOPSIS method to make decisions on G schemes (F1, F2, ..., FG), the result shows that Fp is better than Fq (p≠q). But when the number of schemes is increased or decreased, the result obtained using the same method is that Fq is better than Fp) and unreasonable weight setting, so it cannot be directly used in drill bit selection. Hence, based on the TOPSIS method's principle, a new comprehensive performance evaluation model of the drill bit has been established by constructing an absolutely ideal solution, changing the relative distance measurement method, and introducing entropy weight. The model overcomes the inherent defects of the TOPSIS method while keeps its inherent advantages of simple calculation and easy understanding.
(1) Use vector normalization method[23] to obtain normalized decision matrix: assuming m candidate drill bits and n drill bit evaluation attributes. The decision matrix of the multi-attribute decision problem is $A=\left| {{a}_{i,j}} \right|$, and the normalized decision matrix is $R=\left| {{r}_{i,j}} \right|$$\ \left( i=1,\cdots ,m; \right.$ $\left. j=1,\cdots ,n \right)$, where:
In this study, nine parameters, including WOB, RPM, pump displacement, footage, net drilling time, ROP, the integrity of drill bit at running-in, the integrity of drill bit when pulling-out, and drill bit cost, are selected as evaluation attributes of the candidate drill bit.
(2) Build weighted normalization matrix$X=\left| {{x}_{i,j}} \right|$, where
${{x}_{i,j}}={{r}_{i,j}}{{w}_{j}}$
It is difficult to weigh the influence of each attribute on drill bit selection in the actual drill bit selection process. Using the entropy weight method to calculate the weight of each evaluated attribute of the candidate drill bit can greatly reduce the influence of subjective factors and make the weight distribution more objective and reasonable. The calculation formula of entropy weight is as follows[24]:
(3) Determine the absolutely positive and negative ideal solutions of drill bit: in drill bit selection, we always want to select the drill bit with high rock breaking efficiency, fast drilling speed, low cost, and long service life (ideal drill bit), so the candidate drill bit closest to the ideal drill bit is the optimal drill bit. The optimal drill bit is set as the drill bit positive ideal solution (xPI), and its evaluation attribute values are the best values among all candidate drill bits. The candidate drill bit farthest from the ideal drill bit is set as the drill bit negative ideal solution (xNI), and its evaluation attribute values are the worst among all candidate drill bits. As shown in Fig. 1, the candidate drill bits xA,1, xA,2 have the same distance from the drill bit positive and negative ideal solutions, so the candidate drill bits xA,1, xA,2 are the same in relative superiority. If a new candidate drill bit is added, the drill bit negative ideal solution moves to point A, and the candidate drill bit xA,2 is better than xA,1. On the contrary, if the drill bit negative ideal solution moves to point B, the candidate drill bit xA,1 is better than xA,2. In this case, the problem that the drill bit evaluation results are not absolute in order turns up. The reason is that the drill bit positive and negative ideal solutions selected are relative, not absolute. If the drill bit positive and negative ideal solutions can be fixed, this problem can be solved.
Fig. 1.
Determination of the drill bit absolute ideal solutions.
To solve the above problem, the values of candidate drill bit attributes can be normalized. The normalized method of the benefit-type drill bit evaluation attributes (the larger the attribute value, the better the comprehensive application effect of the drill bit, such as ROP) is:
The normalized method of cost-type drill bit evaluation attributes (the smaller the attribute value, the better the comprehensive application effect of the drill bit, such as drill bit cost) is:
After normalizing by formula (12) and (13), the normalized evaluation attribute value of the candidate drill bit is r[0,1], and the larger the value of r, the better the performance of the drill bit is. Therefore, the drill bit absolute positive ideal solution is${{x}_{\text{PI},\text{abs}}}=\left| 1,1,\cdots ,1 \right|_{n}^{\mathbf{T}}$, and the drill bit absolute negative ideal solution is ${{x}_{\text{NI},\text{abs}}}=$ $\left| 0,0,\cdots ,0 \right|_{n}^{\mathbf{T}}$. When the drill bit absolute positive and negative ideal solutions are fixed, the increase or decrease of the number of candidate drill bits won’t affect the distance between candidate drill bits and the absolute ideal solution, ensuring the absolute order of candidate bits and stability of bit selection results.
(4) The distance from the candidate drill bit to the drill bit absolute positive ideal solution is:
(5) Calculate the close degree between the candidate drill bit and the drill bit absolute ideal solution: From the geometric point of view, the drill bit positive and negative ideal solutions and each candidate drill bit can be regarded as a space vector. Using the projection method to combine the vector's modulus with the cosine of the vector can fully reflect the vector's close degree. As shown in Fig. 2, the distances between the candidate drill bits and the drill bit absolute positive and negative ideal solutions are dPI,1, dPI,2, dNI,1, dNI,2, respectively. The AC is the vertical line projected from the candidate drill bit xA,1 to the line connecting the drill bit absolute positive and negative ideal solutions. H1 is the vertical point. The distances between H1 and the drill bit absolute positive and negative ideal solutions are recorded as hPI,1 and hNI,1. The BD is the vertical line projected from the candidate drill bit xA,2 to the line connecting the drill bit absolute positive and negative ideal solutions, and H2 is the vertical point. The distances between H2 and the drill bit absolute positive and negative ideal solutions are recorded as hPI,2 and hNI,2. The projection method is used to project the candidate drill bit xA,1, xA,2 on the line of the drill bit absolute positive and negative ideal solutions. When hPI,1<hPI,2, there must be hNI,1>hNI,2, the candidate drill bit xA,1 is better than xA,2, and vice versa. The closer the candidate drill bit is to the drill bit absolute positive ideal solution, and the farther away the candidate drill bit from the drill bit absolute negative ideal solution, the better the candidate drill bit is.
Fig. 2.
Determination of the close degree between the candidate drill bit and the ideal drill bit.
P is defined as the close degree between the candidate drill bit xA and the ideal drill bit. The larger the P, the closer the candidate drill bit xA to the drill bit absolute positive ideal solution, and the farther away the candidate drill bit from the drill bit absolute negative ideal solution, the better the candidate drill bit xA is. The formula for calculating the close degree is:
(6) Determine the priority order of the candidate drill bits according to the close degree. Based on the similarity evaluation of the stratum anti-drilling ability and the drill bit's comprehensive performance evaluation model, a new drill bit selection method comprehensively considering the rock anti-drilling ability, drill bit working conditions , drill bit application effect, and drill bit economic benefit has been proposed. The specific process is shown in Fig. 3. Firstly, according to the logging data, mud-logging data, and drill bit usage data, the grey correlation analysis method is used to evaluate the similarity of the anti-drilling ability between the formation to be drilled and the formation drilled already, and the candidate drill bits with reference value are selected. Then, the drill bit's comprehensive performance evaluation model is established by constructing an absolutely ideal solution, measuring relative distance by projection method, and introducing entropy weight. Finally, the close degrees between the candidate drill bits and the ideal drill bit are calculated to obtain the reasonable drill bit selection result.
Well A103 is a horizontal development well in XinJiang oilfield, with a designed well depth of 8 020.88 m and completed well depth of 5 424.87 m. In the first drilling section, sandstone and mudstone layers alternate and are loose in lithology. A 444.5 mm drill bit was used to drill to well depth of 1 499.51 m, at an average ROP of 29.69 m/h, and net drilling time of 41.50 h. The formation in the second drilling section is better in drillability. A 311.2 mm drill bit was used in this section to drill to the well depth of 5 424.87 m at an average ROP of 6.21 m/h in the net drilling time of 644.00 h. The No. I and II formations to be drilled are located in this section. The No. I formation is at the well depth of 5 424.87-5 674.37 m, with an expected thickness of 249.50 m. The No. II formation is at the well depth of 5 674.37-5 844.37 m, with an expected thickness of 170.00 m. The third drilling section is deep, and the formations in this section are complex in lithology, including alternate layers of volcanic rock, sandstone, and mudstone. The No. III and IV formations to be drilled are located in this section. The No. III formation to be drilled is at the well depth of 5 844.37-6 034.37 m, with an expected thickness of 190.00 m. The No. IV formation to be drilled is at the well depth of 6 034.37- 6 109.87 m, with an expected thickness of 75.50 m.
3.2. Determine the similarity of the stratum anti-drilling ability
Based on the data of the drill bit database, the drill bit data under abnormal working conditions such as core drilling, drilling completion, changing drilling tools and sticking were eliminated, and then the similarity of the anti-drilling ability between the formation to be drilled (5 424.87-6 109.87 m) in Well A103 and the formations drilled in adjacent wells at the same depth was analyzed. The analysis showed that the No. I and II formations to be drilled are medium soft formations with low compressive strength. For this type of formation, it is recommended to use PDC drill bits with 5-6 blades and medium cutter set density. The drill bit's crown shape is recommended to be long parabola shape or medium parabola shape, and the cutter size is recommended to be 16-19 mm[25,26,27]. The No. III and IV formations to be drilled are medium-hard formations with high compressive strength and certain degree of abrasiveness. For this type of formation, it is recommended to use PDC drill bits with 5-7 blades and medium cutter set density. The drill bit's crown shape is recommended to be medium parabolic shape or long cone shape, and the cutter size is recommended to be 16-19 mm[25,26,27].
According to the formulas (1)-(5), the correlation coefficients of anti-drilling ability between the formation to be drilled and the formations drilled already were calculated (Fig. 4). It can be seen that the correlation coefficients between the formations to be drilled and the formations drilled are all greater than 0.5, indicating significant correlation. This means it is feasible to evaluate the similarity of the anti-drilling ability between formation to be drilled and formation drilled already through compressive strength, shear strength, internal friction angle, drillability, and hardness. The correlation coefficients of compressive strength, internal friction angle, and drillability between the formation were high, which indicates that the formations to be drilled are similar in strength and abrasiveness to the formations drilled already.
Fig. 4.
Correlation coefficients between anti-drilling ability of the formation to be drilled and that of the formation drilled already.
When the formation to be drilled is quite different in anti-drilling ability from the formation drilled already, the drill bit used in the formation drilled already is not suitable for the formation to be drilled. According to formula (6), the grey correlation degree was calculated, and the drilled formations with grey correlation degrees greater than or equal to 0.6 were taken as the similar formations of the formation to be drilled, and the drill bits used in the similar formations were taken as candidate drill bits.
3.3. Drill bit selection
Considering the basic requirements of high-quality and fast drilling on the comprehensive performance of drill bit, WOB, RPM, pump displacement, footage, net drilling time, ROP, integrity of drill bit running-in the well, the integrity of drill bit pulling-out of the well and drill bit cost were selected as evaluation attributes of drill bit comprehensive performance. These nine evaluation attributes comprehensively reflect the working conditions, application effect, and economic benefits of the drill bit. Based on evaluation attributes of the candidate drill bits, the drill bit's comprehensive performance evaluation model in this paper was used to select the drill bits for the formations to be drilled. The calculation results are shown in Fig. 5. Based on the parameters of candidate drill bits shown in Table 1, the analysis of Fig. 5 shows that: 1) For the No. I and II formations to be drilled, the PDC bits are better in comprehensive performance than roller bits in the candidates, and the larger diameter drill bits are better than smaller diameter drill bits. The HF553 drill bit has the highest close degree and the best comprehensive performance. This drill bit has the largest footage, higher ROP, and lower pump displacement among the candidate drill bits. 2) For the No. III and IV formations to be drilled, the PDC bits are better in comprehensive performance than roller bits, and the smaller diameter drill bits are better than larger diameter drill bits in the candidate bits. The KD351 drill bit among them has the highest close degree and the best comprehensive performance. This drill bit has larger footage, higher ROP, and lower wear rate among the candidate drill bits.
Table 1
Table 1Model and diameter of candidate drill bits.
Fig. 5.
Calculation results of drill bit selection.
In the process of drill bit selection, the weight of each drill bit evaluation attribute was calculated by the entropy weight method (Table 2). From the calculation results, it can be seen that footage, net drilling time, and ROP have greater influences on drill bit selection, followed by WOB and drill bit cost. These five evaluation attributes comprehensively reflect the rock breaking capacity, production time, work efficiency, and quality of the drill bit in the process of drilling. Calculating the weighted standardized evaluation matrix by entropy weights is more objective and reasonable, and can avoid the subjective influence on the evaluation results by traditional expert weight method.
With the advancement of technology, the drill bit database will be updated in real-time. More and more high-quality drill bits will be manufactured, and some drill bits with poor performance will be eliminated. The drill bit selection method must adapt to the dynamic changes in drill bit data. In this work, two candidate drill bits (UR134 and PD125) were added, and the TOPSIS method and the comprehensive performance evaluation model of drill bit presented in this paper were used to select the drill bits for the formations to be drilled (Fig. 6) to verify the adaptability of the method presented in this paper to dynamic changes in drill bit database.
Fig. 6.
Comparison of drill bit selection results.
It can be seen from Fig. 6 that when the candidate drill bit database changes, the calculation results from TOPSIS method of the No. I and III formations to be drilled basically keep the order when the database is not updated, except for adding two new candidate drill bits, while the drill bit selection results of the No. II and IV formations to be drilled show reverse order: 1) When the candidate drill bit database for the formation II to be drilled wasn’t updated, U516M drill bit was better than DS616 drill bit. After adding two candidate drill bits, the DS616 drill bit is better than the U516M drill bit. 2) When the candidate drill bit database for the formation IV to be drilled wasn’t updated, the SR526 drill bit was better than the SF563 drill bit and ES164 drill bit. After adding two candidate drill bits, the SF563 drill bit and ES164 drill bit are better than the SR526 drill bit. In contrast, the calculation results of the new drill bit selection method presented in this paper show that the change of the candidate drill bit database does not affect the ranking of drill bit selection results (except for adding the two new candidate drill bits). It is found through analysis that the reason for the reverse order is that the positive and negative ideal solutions of drill bits in the TOPSIS method are constantly updated with the candidate drill bit database. When the ideal solution changes, the distance between the ideal solution and the candidate drill bit also changes, which is a relative relationship and does not have order preservation property. In contrast, the comprehensive performance evaluation model of the drill bit presented in this paper fixes the ideal solution's position by defining the absolutely positive and negative ideal solutions, which effectively avoids the reverse order problem caused by the drift of the positive and negative ideal solutions in the TOPSIS method. The new method of drill bit selection is more adaptable to drill bit database with dynamic changes.
The drill bit selection method presented in this paper was used to select the drill bits for the formations to be drilled. The results show that the HF553 drill bit is most suitable for the No. I and II formations to be drilled. This drill bit is a 5 blade PDC drill bit with a diameter of 311.2 mm, medium parabola shape of crown, and a cutter size of 19.0 mm. The KD351 drill bit is most suitable for the No. III and IV formations to be drilled. The drill bit is a 6 blade PDC drill bit, with a diameter of 215.9 mm, crown in medium parabolic shape, and a cutter size of 16.0 mm.
HF553 drill bit and KD351 drill bit were used to drill the corresponding formations. The photos of the drill bits running-in and pulling-out of the well are shown in Fig. 7. During the drilling of No. I and II formations, HF553 drill bit had wear rates per meter of 0.07% and 0.10%, respectively, and the drill bit had hardly any wear when pulling-out of the well (Fig. 7a-d). Compared with the drill bits drilling the same formation in adjacent wells, this drill bit increased in ROP by 76.90% and 130.40% respec-tively in the corresponding formations. During the drilling of No. III and IV formations, the KD351 drill bit had wear rates per meter of 0.07% and 0.11%, respectively, and the drill bit had slight bit balling when pulling-out of the well (Fig. 7e-h). Compared with the drill bits drilling the same formation in adjacent wells, this drill bit increased by 204.93% and 61.06%, respectively in ROP in the corresponding formations.
Fig. 7.
Photos of drill bits when running-in and pulling-out of well.
From the overall drilling effect of HF553 drill bit and KD351 drill bit, the selected drill bits had significant increase in ROP and lower wear rates, suggesting they are highly suitable for drilling the formations.
4. Conclusions
By constructing the absolute ideal solution, changing the relative distance measurement method, and introducing entropy weight, the comprehensive performance evaluation model of the drill bit can overcome the inherent defects of the TOPSIS method, such as no absolute ordering, reverse order, and unreasonable weight setting. The method is simple in calculation and easy to understand, and has good adaptability to dynamically changing drill bit database.
Field applications have proved that the drill bits selected by the new method of drill bit selection had higher ROP, lower wear rates, and good compatibility with the drilled formations. The new drill bit selection method can be taken as a technical means to select drill bits with high efficiency, long life, and low costs for oilfields.
Nomenclature
a—evaluation attributes of candidate drill bit;
A—decision matrix;
dNI—distance from candidate drill bit to the absolute negative ideal solution of drill bit, dimensionless;
dPI—distance from candidate drill bit to the absolute positive ideal solution of drill bit, dimensionless;
e—entropy value of candidate drill bit evaluation attribute, dimensionless;
F—TOPSIS method decision scheme;
G—number of decision schemes of TOPSIS method;
hNI—distance between the vertical point on the line of the candidate drill bit projected to the absolute positive and negative ideal solution of the drill bit and the absolute negative ideal solution of the drill bit, dimensionless;
hPI—distance from the vertical point on the line of the candidate drill bit projected to the line connecting the absolute positive and negative ideal solutions to the absolute positive ideal solution of the drill bit, dimensionless;
i—No. of candidate drill bit;
j—No. of drill bit evaluation attribute;
k—No. of anti-drilling property parameters;
M—number of drilled formations;
m—number of candidate bits;
N—number of anti-drilling property parameters;
n—number of drill bit evaluation attributes;
p, q—No. of TOPSIS method decision scheme;
P—close degree between candidate drill bit and absolute ideal solution of drill bit, dimensionless;
Q—weight of candidate bit evaluation attribute, dimensionless;
r—standardized evaluation attribute of candidate drill bit;
R—standardized decision matrix;
RG,t—grey correlation degree, dimensionless;
$S\left( k \right)$—intermediate variable;
t—No. of drilled formation;
u1, u2—space vector directions of candidate drill bit;
w—entropy weight of candidate drill bit evaluation attribute, dimensionless;
x—weighted normalized evaluation attribute;
xA—candidate drill bit;
xNI—negative ideal solution of drill bit, dimensionless;
xNI,abs—absolute negative ideal solution of drill bit, dimensionless;
xPI—positive ideal solution of drill bit, dimensionless;
xPI,abs—absolute positive ideal solution of drill bit, dimensionless;
Xt—comparison data column;
${{X}_{t}}\left( k \right)$—sample data in comparison data column;
${{x}_{t}}\left( k \right)$—dimensionless sample data in comparison data column;
Y—reference data column;
$Y\left( k \right)$—sample data in reference data column;
$y\left( k \right)$—dimensionless sample data in reference data column;
${{\Delta }_{t}}\left( k \right)$—intermediate variable, dimensionless;
${{\zeta }_{t}}\left( k \right)$—correlation coefficient between the formation to be drilled and the formation drilled, dimensionless;
... The drill bit is an important tool for breaking rock to form a borehole during the drilling process. Selecting a high-quality drill bit is the key to reducing drilling costs and improve drilling efficiency[1]. For a long time, drilling engineers have selected drill bit by two methods. The first method is to select a suitable drill bit according to the stratum anti-drilling ability[2]. In this method, lab experiment or acoustic logging data are used to find out drillability of formations, and the type of drill bit is selected according to the drill bit IADC (International Association Drilling Contractor) code[3]. For example, the parameters such as shear strength and compressive strength of rock are taken as the basis for type selection of PDC (Polycrystalline Diamond Compact) drill bit[4,5]; fractal theory is used to describe and establish the statistical relationship between the dimension of stratigraphic sedimentary sequence and the working performance of drill bit, and the type of drill bit is selected by calculating the fractal dimension[6]. This method has certain guiding significance for the selection of drill bits according to the stratum anti-drilling ability. However, in practical application, there are many types of drill bits (or from different manufacturers) suitable for a certain stratum, so it is still difficult to select the appropriate model of drill bit. Moreover, when the stratum anti-drilling ability is unknown, this method can’t be used. The second method is to evaluate the application effect of the drill bits based on the drill bit data of the adjacent area and select the drill bit suitable for the formation to be drilled[7]. For example, Toczek et al.[8] defined Specific Energy (SE, the work done by a drill bit to drill a unit volume of rock) to evaluate a drill bit's drilling effect. Rabia et al.[9] proposed the Cost Per Foot Method (CPF) for drill bit selection. Yang Jin et al.[10] improved the Cost Per Foot Method (CPF) and put forward the drill bit benefit index. Based on the real-time data of drilling fluid and logging instrument, Momeni et al.[11] predicted the expected ROP (rate of penetration) under specific drilling parameters and selected the drill bit with the maximum expected ROP. Sun et al.[12] took the drill bit's dullness as an evaluation index and selected the drill bit with the smallest change in dullness within the same time. This kind of method fully considers the working performance of the drill bit. If the designed well differ greatly in geological conditions from the drilled well, it is difficult to obtain the ideal result of drill bit selection. Moreover, this kind of method is blind for the new area with little or no data on drill bit use. ...
Review and comments on bit selection methods at home and abroad
1
2011
... The drill bit is an important tool for breaking rock to form a borehole during the drilling process. Selecting a high-quality drill bit is the key to reducing drilling costs and improve drilling efficiency[1]. For a long time, drilling engineers have selected drill bit by two methods. The first method is to select a suitable drill bit according to the stratum anti-drilling ability[2]. In this method, lab experiment or acoustic logging data are used to find out drillability of formations, and the type of drill bit is selected according to the drill bit IADC (International Association Drilling Contractor) code[3]. For example, the parameters such as shear strength and compressive strength of rock are taken as the basis for type selection of PDC (Polycrystalline Diamond Compact) drill bit[4,5]; fractal theory is used to describe and establish the statistical relationship between the dimension of stratigraphic sedimentary sequence and the working performance of drill bit, and the type of drill bit is selected by calculating the fractal dimension[6]. This method has certain guiding significance for the selection of drill bits according to the stratum anti-drilling ability. However, in practical application, there are many types of drill bits (or from different manufacturers) suitable for a certain stratum, so it is still difficult to select the appropriate model of drill bit. Moreover, when the stratum anti-drilling ability is unknown, this method can’t be used. The second method is to evaluate the application effect of the drill bits based on the drill bit data of the adjacent area and select the drill bit suitable for the formation to be drilled[7]. For example, Toczek et al.[8] defined Specific Energy (SE, the work done by a drill bit to drill a unit volume of rock) to evaluate a drill bit's drilling effect. Rabia et al.[9] proposed the Cost Per Foot Method (CPF) for drill bit selection. Yang Jin et al.[10] improved the Cost Per Foot Method (CPF) and put forward the drill bit benefit index. Based on the real-time data of drilling fluid and logging instrument, Momeni et al.[11] predicted the expected ROP (rate of penetration) under specific drilling parameters and selected the drill bit with the maximum expected ROP. Sun et al.[12] took the drill bit's dullness as an evaluation index and selected the drill bit with the smallest change in dullness within the same time. This kind of method fully considers the working performance of the drill bit. If the designed well differ greatly in geological conditions from the drilled well, it is difficult to obtain the ideal result of drill bit selection. Moreover, this kind of method is blind for the new area with little or no data on drill bit use. ...
Estimating rock mechanical properties of the Zubair shale formation using a sonic wireline log and core analysis
1
2018
... The drill bit is an important tool for breaking rock to form a borehole during the drilling process. Selecting a high-quality drill bit is the key to reducing drilling costs and improve drilling efficiency[1]. For a long time, drilling engineers have selected drill bit by two methods. The first method is to select a suitable drill bit according to the stratum anti-drilling ability[2]. In this method, lab experiment or acoustic logging data are used to find out drillability of formations, and the type of drill bit is selected according to the drill bit IADC (International Association Drilling Contractor) code[3]. For example, the parameters such as shear strength and compressive strength of rock are taken as the basis for type selection of PDC (Polycrystalline Diamond Compact) drill bit[4,5]; fractal theory is used to describe and establish the statistical relationship between the dimension of stratigraphic sedimentary sequence and the working performance of drill bit, and the type of drill bit is selected by calculating the fractal dimension[6]. This method has certain guiding significance for the selection of drill bits according to the stratum anti-drilling ability. However, in practical application, there are many types of drill bits (or from different manufacturers) suitable for a certain stratum, so it is still difficult to select the appropriate model of drill bit. Moreover, when the stratum anti-drilling ability is unknown, this method can’t be used. The second method is to evaluate the application effect of the drill bits based on the drill bit data of the adjacent area and select the drill bit suitable for the formation to be drilled[7]. For example, Toczek et al.[8] defined Specific Energy (SE, the work done by a drill bit to drill a unit volume of rock) to evaluate a drill bit's drilling effect. Rabia et al.[9] proposed the Cost Per Foot Method (CPF) for drill bit selection. Yang Jin et al.[10] improved the Cost Per Foot Method (CPF) and put forward the drill bit benefit index. Based on the real-time data of drilling fluid and logging instrument, Momeni et al.[11] predicted the expected ROP (rate of penetration) under specific drilling parameters and selected the drill bit with the maximum expected ROP. Sun et al.[12] took the drill bit's dullness as an evaluation index and selected the drill bit with the smallest change in dullness within the same time. This kind of method fully considers the working performance of the drill bit. If the designed well differ greatly in geological conditions from the drilled well, it is difficult to obtain the ideal result of drill bit selection. Moreover, this kind of method is blind for the new area with little or no data on drill bit use. ...
The bit selection research on LS101
1
2017
... The drill bit is an important tool for breaking rock to form a borehole during the drilling process. Selecting a high-quality drill bit is the key to reducing drilling costs and improve drilling efficiency[1]. For a long time, drilling engineers have selected drill bit by two methods. The first method is to select a suitable drill bit according to the stratum anti-drilling ability[2]. In this method, lab experiment or acoustic logging data are used to find out drillability of formations, and the type of drill bit is selected according to the drill bit IADC (International Association Drilling Contractor) code[3]. For example, the parameters such as shear strength and compressive strength of rock are taken as the basis for type selection of PDC (Polycrystalline Diamond Compact) drill bit[4,5]; fractal theory is used to describe and establish the statistical relationship between the dimension of stratigraphic sedimentary sequence and the working performance of drill bit, and the type of drill bit is selected by calculating the fractal dimension[6]. This method has certain guiding significance for the selection of drill bits according to the stratum anti-drilling ability. However, in practical application, there are many types of drill bits (or from different manufacturers) suitable for a certain stratum, so it is still difficult to select the appropriate model of drill bit. Moreover, when the stratum anti-drilling ability is unknown, this method can’t be used. The second method is to evaluate the application effect of the drill bits based on the drill bit data of the adjacent area and select the drill bit suitable for the formation to be drilled[7]. For example, Toczek et al.[8] defined Specific Energy (SE, the work done by a drill bit to drill a unit volume of rock) to evaluate a drill bit's drilling effect. Rabia et al.[9] proposed the Cost Per Foot Method (CPF) for drill bit selection. Yang Jin et al.[10] improved the Cost Per Foot Method (CPF) and put forward the drill bit benefit index. Based on the real-time data of drilling fluid and logging instrument, Momeni et al.[11] predicted the expected ROP (rate of penetration) under specific drilling parameters and selected the drill bit with the maximum expected ROP. Sun et al.[12] took the drill bit's dullness as an evaluation index and selected the drill bit with the smallest change in dullness within the same time. This kind of method fully considers the working performance of the drill bit. If the designed well differ greatly in geological conditions from the drilled well, it is difficult to obtain the ideal result of drill bit selection. Moreover, this kind of method is blind for the new area with little or no data on drill bit use. ...
Prediction of uniaxial compressive strength of underground formations from sonic log parameters
1
2014
... The drill bit is an important tool for breaking rock to form a borehole during the drilling process. Selecting a high-quality drill bit is the key to reducing drilling costs and improve drilling efficiency[1]. For a long time, drilling engineers have selected drill bit by two methods. The first method is to select a suitable drill bit according to the stratum anti-drilling ability[2]. In this method, lab experiment or acoustic logging data are used to find out drillability of formations, and the type of drill bit is selected according to the drill bit IADC (International Association Drilling Contractor) code[3]. For example, the parameters such as shear strength and compressive strength of rock are taken as the basis for type selection of PDC (Polycrystalline Diamond Compact) drill bit[4,5]; fractal theory is used to describe and establish the statistical relationship between the dimension of stratigraphic sedimentary sequence and the working performance of drill bit, and the type of drill bit is selected by calculating the fractal dimension[6]. This method has certain guiding significance for the selection of drill bits according to the stratum anti-drilling ability. However, in practical application, there are many types of drill bits (or from different manufacturers) suitable for a certain stratum, so it is still difficult to select the appropriate model of drill bit. Moreover, when the stratum anti-drilling ability is unknown, this method can’t be used. The second method is to evaluate the application effect of the drill bits based on the drill bit data of the adjacent area and select the drill bit suitable for the formation to be drilled[7]. For example, Toczek et al.[8] defined Specific Energy (SE, the work done by a drill bit to drill a unit volume of rock) to evaluate a drill bit's drilling effect. Rabia et al.[9] proposed the Cost Per Foot Method (CPF) for drill bit selection. Yang Jin et al.[10] improved the Cost Per Foot Method (CPF) and put forward the drill bit benefit index. Based on the real-time data of drilling fluid and logging instrument, Momeni et al.[11] predicted the expected ROP (rate of penetration) under specific drilling parameters and selected the drill bit with the maximum expected ROP. Sun et al.[12] took the drill bit's dullness as an evaluation index and selected the drill bit with the smallest change in dullness within the same time. This kind of method fully considers the working performance of the drill bit. If the designed well differ greatly in geological conditions from the drilled well, it is difficult to obtain the ideal result of drill bit selection. Moreover, this kind of method is blind for the new area with little or no data on drill bit use. ...
A new approach for oil drilling bit selection with fractal theory
1
2012
... The drill bit is an important tool for breaking rock to form a borehole during the drilling process. Selecting a high-quality drill bit is the key to reducing drilling costs and improve drilling efficiency[1]. For a long time, drilling engineers have selected drill bit by two methods. The first method is to select a suitable drill bit according to the stratum anti-drilling ability[2]. In this method, lab experiment or acoustic logging data are used to find out drillability of formations, and the type of drill bit is selected according to the drill bit IADC (International Association Drilling Contractor) code[3]. For example, the parameters such as shear strength and compressive strength of rock are taken as the basis for type selection of PDC (Polycrystalline Diamond Compact) drill bit[4,5]; fractal theory is used to describe and establish the statistical relationship between the dimension of stratigraphic sedimentary sequence and the working performance of drill bit, and the type of drill bit is selected by calculating the fractal dimension[6]. This method has certain guiding significance for the selection of drill bits according to the stratum anti-drilling ability. However, in practical application, there are many types of drill bits (or from different manufacturers) suitable for a certain stratum, so it is still difficult to select the appropriate model of drill bit. Moreover, when the stratum anti-drilling ability is unknown, this method can’t be used. The second method is to evaluate the application effect of the drill bits based on the drill bit data of the adjacent area and select the drill bit suitable for the formation to be drilled[7]. For example, Toczek et al.[8] defined Specific Energy (SE, the work done by a drill bit to drill a unit volume of rock) to evaluate a drill bit's drilling effect. Rabia et al.[9] proposed the Cost Per Foot Method (CPF) for drill bit selection. Yang Jin et al.[10] improved the Cost Per Foot Method (CPF) and put forward the drill bit benefit index. Based on the real-time data of drilling fluid and logging instrument, Momeni et al.[11] predicted the expected ROP (rate of penetration) under specific drilling parameters and selected the drill bit with the maximum expected ROP. Sun et al.[12] took the drill bit's dullness as an evaluation index and selected the drill bit with the smallest change in dullness within the same time. This kind of method fully considers the working performance of the drill bit. If the designed well differ greatly in geological conditions from the drilled well, it is difficult to obtain the ideal result of drill bit selection. Moreover, this kind of method is blind for the new area with little or no data on drill bit use. ...
Improving drilling speed technologies by stratification for No.1 fault-zone of Shunbei block
1
2018
... The drill bit is an important tool for breaking rock to form a borehole during the drilling process. Selecting a high-quality drill bit is the key to reducing drilling costs and improve drilling efficiency[1]. For a long time, drilling engineers have selected drill bit by two methods. The first method is to select a suitable drill bit according to the stratum anti-drilling ability[2]. In this method, lab experiment or acoustic logging data are used to find out drillability of formations, and the type of drill bit is selected according to the drill bit IADC (International Association Drilling Contractor) code[3]. For example, the parameters such as shear strength and compressive strength of rock are taken as the basis for type selection of PDC (Polycrystalline Diamond Compact) drill bit[4,5]; fractal theory is used to describe and establish the statistical relationship between the dimension of stratigraphic sedimentary sequence and the working performance of drill bit, and the type of drill bit is selected by calculating the fractal dimension[6]. This method has certain guiding significance for the selection of drill bits according to the stratum anti-drilling ability. However, in practical application, there are many types of drill bits (or from different manufacturers) suitable for a certain stratum, so it is still difficult to select the appropriate model of drill bit. Moreover, when the stratum anti-drilling ability is unknown, this method can’t be used. The second method is to evaluate the application effect of the drill bits based on the drill bit data of the adjacent area and select the drill bit suitable for the formation to be drilled[7]. For example, Toczek et al.[8] defined Specific Energy (SE, the work done by a drill bit to drill a unit volume of rock) to evaluate a drill bit's drilling effect. Rabia et al.[9] proposed the Cost Per Foot Method (CPF) for drill bit selection. Yang Jin et al.[10] improved the Cost Per Foot Method (CPF) and put forward the drill bit benefit index. Based on the real-time data of drilling fluid and logging instrument, Momeni et al.[11] predicted the expected ROP (rate of penetration) under specific drilling parameters and selected the drill bit with the maximum expected ROP. Sun et al.[12] took the drill bit's dullness as an evaluation index and selected the drill bit with the smallest change in dullness within the same time. This kind of method fully considers the working performance of the drill bit. If the designed well differ greatly in geological conditions from the drilled well, it is difficult to obtain the ideal result of drill bit selection. Moreover, this kind of method is blind for the new area with little or no data on drill bit use. ...
Total specific energy as a new indicator of drilling efficiency
1
2017
... The drill bit is an important tool for breaking rock to form a borehole during the drilling process. Selecting a high-quality drill bit is the key to reducing drilling costs and improve drilling efficiency[1]. For a long time, drilling engineers have selected drill bit by two methods. The first method is to select a suitable drill bit according to the stratum anti-drilling ability[2]. In this method, lab experiment or acoustic logging data are used to find out drillability of formations, and the type of drill bit is selected according to the drill bit IADC (International Association Drilling Contractor) code[3]. For example, the parameters such as shear strength and compressive strength of rock are taken as the basis for type selection of PDC (Polycrystalline Diamond Compact) drill bit[4,5]; fractal theory is used to describe and establish the statistical relationship between the dimension of stratigraphic sedimentary sequence and the working performance of drill bit, and the type of drill bit is selected by calculating the fractal dimension[6]. This method has certain guiding significance for the selection of drill bits according to the stratum anti-drilling ability. However, in practical application, there are many types of drill bits (or from different manufacturers) suitable for a certain stratum, so it is still difficult to select the appropriate model of drill bit. Moreover, when the stratum anti-drilling ability is unknown, this method can’t be used. The second method is to evaluate the application effect of the drill bits based on the drill bit data of the adjacent area and select the drill bit suitable for the formation to be drilled[7]. For example, Toczek et al.[8] defined Specific Energy (SE, the work done by a drill bit to drill a unit volume of rock) to evaluate a drill bit's drilling effect. Rabia et al.[9] proposed the Cost Per Foot Method (CPF) for drill bit selection. Yang Jin et al.[10] improved the Cost Per Foot Method (CPF) and put forward the drill bit benefit index. Based on the real-time data of drilling fluid and logging instrument, Momeni et al.[11] predicted the expected ROP (rate of penetration) under specific drilling parameters and selected the drill bit with the maximum expected ROP. Sun et al.[12] took the drill bit's dullness as an evaluation index and selected the drill bit with the smallest change in dullness within the same time. This kind of method fully considers the working performance of the drill bit. If the designed well differ greatly in geological conditions from the drilled well, it is difficult to obtain the ideal result of drill bit selection. Moreover, this kind of method is blind for the new area with little or no data on drill bit use. ...
A new approach to drill bit selection
1
1986
... The drill bit is an important tool for breaking rock to form a borehole during the drilling process. Selecting a high-quality drill bit is the key to reducing drilling costs and improve drilling efficiency[1]. For a long time, drilling engineers have selected drill bit by two methods. The first method is to select a suitable drill bit according to the stratum anti-drilling ability[2]. In this method, lab experiment or acoustic logging data are used to find out drillability of formations, and the type of drill bit is selected according to the drill bit IADC (International Association Drilling Contractor) code[3]. For example, the parameters such as shear strength and compressive strength of rock are taken as the basis for type selection of PDC (Polycrystalline Diamond Compact) drill bit[4,5]; fractal theory is used to describe and establish the statistical relationship between the dimension of stratigraphic sedimentary sequence and the working performance of drill bit, and the type of drill bit is selected by calculating the fractal dimension[6]. This method has certain guiding significance for the selection of drill bits according to the stratum anti-drilling ability. However, in practical application, there are many types of drill bits (or from different manufacturers) suitable for a certain stratum, so it is still difficult to select the appropriate model of drill bit. Moreover, when the stratum anti-drilling ability is unknown, this method can’t be used. The second method is to evaluate the application effect of the drill bits based on the drill bit data of the adjacent area and select the drill bit suitable for the formation to be drilled[7]. For example, Toczek et al.[8] defined Specific Energy (SE, the work done by a drill bit to drill a unit volume of rock) to evaluate a drill bit's drilling effect. Rabia et al.[9] proposed the Cost Per Foot Method (CPF) for drill bit selection. Yang Jin et al.[10] improved the Cost Per Foot Method (CPF) and put forward the drill bit benefit index. Based on the real-time data of drilling fluid and logging instrument, Momeni et al.[11] predicted the expected ROP (rate of penetration) under specific drilling parameters and selected the drill bit with the maximum expected ROP. Sun et al.[12] took the drill bit's dullness as an evaluation index and selected the drill bit with the smallest change in dullness within the same time. This kind of method fully considers the working performance of the drill bit. If the designed well differ greatly in geological conditions from the drilled well, it is difficult to obtain the ideal result of drill bit selection. Moreover, this kind of method is blind for the new area with little or no data on drill bit use. ...
Research on a new method for type-selection of bit
1
1998
... The drill bit is an important tool for breaking rock to form a borehole during the drilling process. Selecting a high-quality drill bit is the key to reducing drilling costs and improve drilling efficiency[1]. For a long time, drilling engineers have selected drill bit by two methods. The first method is to select a suitable drill bit according to the stratum anti-drilling ability[2]. In this method, lab experiment or acoustic logging data are used to find out drillability of formations, and the type of drill bit is selected according to the drill bit IADC (International Association Drilling Contractor) code[3]. For example, the parameters such as shear strength and compressive strength of rock are taken as the basis for type selection of PDC (Polycrystalline Diamond Compact) drill bit[4,5]; fractal theory is used to describe and establish the statistical relationship between the dimension of stratigraphic sedimentary sequence and the working performance of drill bit, and the type of drill bit is selected by calculating the fractal dimension[6]. This method has certain guiding significance for the selection of drill bits according to the stratum anti-drilling ability. However, in practical application, there are many types of drill bits (or from different manufacturers) suitable for a certain stratum, so it is still difficult to select the appropriate model of drill bit. Moreover, when the stratum anti-drilling ability is unknown, this method can’t be used. The second method is to evaluate the application effect of the drill bits based on the drill bit data of the adjacent area and select the drill bit suitable for the formation to be drilled[7]. For example, Toczek et al.[8] defined Specific Energy (SE, the work done by a drill bit to drill a unit volume of rock) to evaluate a drill bit's drilling effect. Rabia et al.[9] proposed the Cost Per Foot Method (CPF) for drill bit selection. Yang Jin et al.[10] improved the Cost Per Foot Method (CPF) and put forward the drill bit benefit index. Based on the real-time data of drilling fluid and logging instrument, Momeni et al.[11] predicted the expected ROP (rate of penetration) under specific drilling parameters and selected the drill bit with the maximum expected ROP. Sun et al.[12] took the drill bit's dullness as an evaluation index and selected the drill bit with the smallest change in dullness within the same time. This kind of method fully considers the working performance of the drill bit. If the designed well differ greatly in geological conditions from the drilled well, it is difficult to obtain the ideal result of drill bit selection. Moreover, this kind of method is blind for the new area with little or no data on drill bit use. ...
Optimum drill bit selection by using bit images and mathematical investigation
1
2017
... The drill bit is an important tool for breaking rock to form a borehole during the drilling process. Selecting a high-quality drill bit is the key to reducing drilling costs and improve drilling efficiency[1]. For a long time, drilling engineers have selected drill bit by two methods. The first method is to select a suitable drill bit according to the stratum anti-drilling ability[2]. In this method, lab experiment or acoustic logging data are used to find out drillability of formations, and the type of drill bit is selected according to the drill bit IADC (International Association Drilling Contractor) code[3]. For example, the parameters such as shear strength and compressive strength of rock are taken as the basis for type selection of PDC (Polycrystalline Diamond Compact) drill bit[4,5]; fractal theory is used to describe and establish the statistical relationship between the dimension of stratigraphic sedimentary sequence and the working performance of drill bit, and the type of drill bit is selected by calculating the fractal dimension[6]. This method has certain guiding significance for the selection of drill bits according to the stratum anti-drilling ability. However, in practical application, there are many types of drill bits (or from different manufacturers) suitable for a certain stratum, so it is still difficult to select the appropriate model of drill bit. Moreover, when the stratum anti-drilling ability is unknown, this method can’t be used. The second method is to evaluate the application effect of the drill bits based on the drill bit data of the adjacent area and select the drill bit suitable for the formation to be drilled[7]. For example, Toczek et al.[8] defined Specific Energy (SE, the work done by a drill bit to drill a unit volume of rock) to evaluate a drill bit's drilling effect. Rabia et al.[9] proposed the Cost Per Foot Method (CPF) for drill bit selection. Yang Jin et al.[10] improved the Cost Per Foot Method (CPF) and put forward the drill bit benefit index. Based on the real-time data of drilling fluid and logging instrument, Momeni et al.[11] predicted the expected ROP (rate of penetration) under specific drilling parameters and selected the drill bit with the maximum expected ROP. Sun et al.[12] took the drill bit's dullness as an evaluation index and selected the drill bit with the smallest change in dullness within the same time. This kind of method fully considers the working performance of the drill bit. If the designed well differ greatly in geological conditions from the drilled well, it is difficult to obtain the ideal result of drill bit selection. Moreover, this kind of method is blind for the new area with little or no data on drill bit use. ...
Bit optimization for Che 47 in Carboniferous stratum
1
2019
... The drill bit is an important tool for breaking rock to form a borehole during the drilling process. Selecting a high-quality drill bit is the key to reducing drilling costs and improve drilling efficiency[1]. For a long time, drilling engineers have selected drill bit by two methods. The first method is to select a suitable drill bit according to the stratum anti-drilling ability[2]. In this method, lab experiment or acoustic logging data are used to find out drillability of formations, and the type of drill bit is selected according to the drill bit IADC (International Association Drilling Contractor) code[3]. For example, the parameters such as shear strength and compressive strength of rock are taken as the basis for type selection of PDC (Polycrystalline Diamond Compact) drill bit[4,5]; fractal theory is used to describe and establish the statistical relationship between the dimension of stratigraphic sedimentary sequence and the working performance of drill bit, and the type of drill bit is selected by calculating the fractal dimension[6]. This method has certain guiding significance for the selection of drill bits according to the stratum anti-drilling ability. However, in practical application, there are many types of drill bits (or from different manufacturers) suitable for a certain stratum, so it is still difficult to select the appropriate model of drill bit. Moreover, when the stratum anti-drilling ability is unknown, this method can’t be used. The second method is to evaluate the application effect of the drill bits based on the drill bit data of the adjacent area and select the drill bit suitable for the formation to be drilled[7]. For example, Toczek et al.[8] defined Specific Energy (SE, the work done by a drill bit to drill a unit volume of rock) to evaluate a drill bit's drilling effect. Rabia et al.[9] proposed the Cost Per Foot Method (CPF) for drill bit selection. Yang Jin et al.[10] improved the Cost Per Foot Method (CPF) and put forward the drill bit benefit index. Based on the real-time data of drilling fluid and logging instrument, Momeni et al.[11] predicted the expected ROP (rate of penetration) under specific drilling parameters and selected the drill bit with the maximum expected ROP. Sun et al.[12] took the drill bit's dullness as an evaluation index and selected the drill bit with the smallest change in dullness within the same time. This kind of method fully considers the working performance of the drill bit. If the designed well differ greatly in geological conditions from the drilled well, it is difficult to obtain the ideal result of drill bit selection. Moreover, this kind of method is blind for the new area with little or no data on drill bit use. ...
Selection and application of bit type based on PCA-BP
1
2016
... At present, drill bit selection using machine learning technology has developed rapidly. For example, Zhao Tingfeng et al.[13] used the principal component analysis method to find out the key indexes affecting the drill bit selection from the indexes such as ROP, footage, WOB (Weight on the bit), and RPM (revolutions per minute) and calculated the "comprehensive index" to select the drill bit. Based on the principle of fuzzy mathematics, Deng Rong et al.[14] proposed a multi-factor fuzzy comprehensive evaluation method for drill bit selection. Sui Sheng et al.[15] established a drill bit selection model based on factor analysis theory. Bilgesu et al.[16] designed a three- layer feedback neural network model to select the drill bit. You et al.[17] used the grey relational analysis method to evaluate the drill bit's use effect based on correlation degree. Momeni et al.[18] proposed a back-propagation artificial neural network model with two implicit units for drill bit selection. Edalatkhah et al.[19] worked out a prediction model for drill bit selection by combining an artificial neural network model with genetic algorithm. ...
Fuzzy comprehensive evaluation method of cone bit teeth rock- breaking efficiency
1
2016
... At present, drill bit selection using machine learning technology has developed rapidly. For example, Zhao Tingfeng et al.[13] used the principal component analysis method to find out the key indexes affecting the drill bit selection from the indexes such as ROP, footage, WOB (Weight on the bit), and RPM (revolutions per minute) and calculated the "comprehensive index" to select the drill bit. Based on the principle of fuzzy mathematics, Deng Rong et al.[14] proposed a multi-factor fuzzy comprehensive evaluation method for drill bit selection. Sui Sheng et al.[15] established a drill bit selection model based on factor analysis theory. Bilgesu et al.[16] designed a three- layer feedback neural network model to select the drill bit. You et al.[17] used the grey relational analysis method to evaluate the drill bit's use effect based on correlation degree. Momeni et al.[18] proposed a back-propagation artificial neural network model with two implicit units for drill bit selection. Edalatkhah et al.[19] worked out a prediction model for drill bit selection by combining an artificial neural network model with genetic algorithm. ...
Application of principal factor analysis in bit selection
1
2014
... At present, drill bit selection using machine learning technology has developed rapidly. For example, Zhao Tingfeng et al.[13] used the principal component analysis method to find out the key indexes affecting the drill bit selection from the indexes such as ROP, footage, WOB (Weight on the bit), and RPM (revolutions per minute) and calculated the "comprehensive index" to select the drill bit. Based on the principle of fuzzy mathematics, Deng Rong et al.[14] proposed a multi-factor fuzzy comprehensive evaluation method for drill bit selection. Sui Sheng et al.[15] established a drill bit selection model based on factor analysis theory. Bilgesu et al.[16] designed a three- layer feedback neural network model to select the drill bit. You et al.[17] used the grey relational analysis method to evaluate the drill bit's use effect based on correlation degree. Momeni et al.[18] proposed a back-propagation artificial neural network model with two implicit units for drill bit selection. Edalatkhah et al.[19] worked out a prediction model for drill bit selection by combining an artificial neural network model with genetic algorithm. ...
A new approach for drill bit selection
1
2000
... At present, drill bit selection using machine learning technology has developed rapidly. For example, Zhao Tingfeng et al.[13] used the principal component analysis method to find out the key indexes affecting the drill bit selection from the indexes such as ROP, footage, WOB (Weight on the bit), and RPM (revolutions per minute) and calculated the "comprehensive index" to select the drill bit. Based on the principle of fuzzy mathematics, Deng Rong et al.[14] proposed a multi-factor fuzzy comprehensive evaluation method for drill bit selection. Sui Sheng et al.[15] established a drill bit selection model based on factor analysis theory. Bilgesu et al.[16] designed a three- layer feedback neural network model to select the drill bit. You et al.[17] used the grey relational analysis method to evaluate the drill bit's use effect based on correlation degree. Momeni et al.[18] proposed a back-propagation artificial neural network model with two implicit units for drill bit selection. Edalatkhah et al.[19] worked out a prediction model for drill bit selection by combining an artificial neural network model with genetic algorithm. ...
Preferable selection of drill bit for volcanic rock formation based on hierarchy grey relation theory
1
2014
... At present, drill bit selection using machine learning technology has developed rapidly. For example, Zhao Tingfeng et al.[13] used the principal component analysis method to find out the key indexes affecting the drill bit selection from the indexes such as ROP, footage, WOB (Weight on the bit), and RPM (revolutions per minute) and calculated the "comprehensive index" to select the drill bit. Based on the principle of fuzzy mathematics, Deng Rong et al.[14] proposed a multi-factor fuzzy comprehensive evaluation method for drill bit selection. Sui Sheng et al.[15] established a drill bit selection model based on factor analysis theory. Bilgesu et al.[16] designed a three- layer feedback neural network model to select the drill bit. You et al.[17] used the grey relational analysis method to evaluate the drill bit's use effect based on correlation degree. Momeni et al.[18] proposed a back-propagation artificial neural network model with two implicit units for drill bit selection. Edalatkhah et al.[19] worked out a prediction model for drill bit selection by combining an artificial neural network model with genetic algorithm. ...
Bit selection using field drilling data and mathematical investigation
1
2018
... At present, drill bit selection using machine learning technology has developed rapidly. For example, Zhao Tingfeng et al.[13] used the principal component analysis method to find out the key indexes affecting the drill bit selection from the indexes such as ROP, footage, WOB (Weight on the bit), and RPM (revolutions per minute) and calculated the "comprehensive index" to select the drill bit. Based on the principle of fuzzy mathematics, Deng Rong et al.[14] proposed a multi-factor fuzzy comprehensive evaluation method for drill bit selection. Sui Sheng et al.[15] established a drill bit selection model based on factor analysis theory. Bilgesu et al.[16] designed a three- layer feedback neural network model to select the drill bit. You et al.[17] used the grey relational analysis method to evaluate the drill bit's use effect based on correlation degree. Momeni et al.[18] proposed a back-propagation artificial neural network model with two implicit units for drill bit selection. Edalatkhah et al.[19] worked out a prediction model for drill bit selection by combining an artificial neural network model with genetic algorithm. ...
Bit selection optimization using artificial intelligence systems
1
2010
... At present, drill bit selection using machine learning technology has developed rapidly. For example, Zhao Tingfeng et al.[13] used the principal component analysis method to find out the key indexes affecting the drill bit selection from the indexes such as ROP, footage, WOB (Weight on the bit), and RPM (revolutions per minute) and calculated the "comprehensive index" to select the drill bit. Based on the principle of fuzzy mathematics, Deng Rong et al.[14] proposed a multi-factor fuzzy comprehensive evaluation method for drill bit selection. Sui Sheng et al.[15] established a drill bit selection model based on factor analysis theory. Bilgesu et al.[16] designed a three- layer feedback neural network model to select the drill bit. You et al.[17] used the grey relational analysis method to evaluate the drill bit's use effect based on correlation degree. Momeni et al.[18] proposed a back-propagation artificial neural network model with two implicit units for drill bit selection. Edalatkhah et al.[19] worked out a prediction model for drill bit selection by combining an artificial neural network model with genetic algorithm. ...
Application of grey relational analysis in Urumqi mining area reservoir assessment
1
2017
... Considering the influences of strength characteristics, deformation characteristics, and surface characteristics of formation on drill bit selection, compressive strength, shear strength, internal friction angle, drillability, and hardness are selected as evaluation indexes for the similarity of stratum anti-drilling ability. Grey correlation analysis theory is used to calculate the correlation coefficient and grey correlation degrees of the anti-drilling ability parameters between the formation to be drilled and the formation drilled[20]. The formation with a higher grey correlation degree (grey correlation degree greater than or equal to 0.6) is selected as the similar formation of the formation to be drilled, and the drill bit used in the similar formation is taken as the candidate drill bit. The specific process includes four steps. ...
The evaluation of financial risk of YRD based on Z-Score model
1
2020
... (2) Data dimensionless: the Z-score standardized method is used for the dimensionless processing of sample data[21]. ...
An evaluation approach for oil exploration and exploitation investment based on TOPSIS
1
2015
... TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method[22] is an effective method commonly used in multi-attribute decision analysis, which evaluates the evaluation object according to the close degree of the evaluation object to the idealized target. However, this method has some inherent defects, such as no absolute ordering, reverse ordering (using the TOPSIS method to make decisions on G schemes (F1, F2, ..., FG), the result shows that Fp is better than Fq (p≠q). But when the number of schemes is increased or decreased, the result obtained using the same method is that Fq is better than Fp) and unreasonable weight setting, so it cannot be directly used in drill bit selection. Hence, based on the TOPSIS method's principle, a new comprehensive performance evaluation model of the drill bit has been established by constructing an absolutely ideal solution, changing the relative distance measurement method, and introducing entropy weight. The model overcomes the inherent defects of the TOPSIS method while keeps its inherent advantages of simple calculation and easy understanding. ...
Investigation of different normalization methods for TOPSIS
1
2012
... (1) Use vector normalization method[23] to obtain normalized decision matrix: assuming m candidate drill bits and n drill bit evaluation attributes. The decision matrix of the multi-attribute decision problem is $A=\left| {{a}_{i,j}} \right|$, and the normalized decision matrix is $R=\left| {{r}_{i,j}} \right|$$\ \left( i=1,\cdots ,m; \right.$ $\left. j=1,\cdots ,n \right)$, where: ...
Regional economic vitality model based on entropy weight method and factor analysis
1
2020
... It is difficult to weigh the influence of each attribute on drill bit selection in the actual drill bit selection process. Using the entropy weight method to calculate the weight of each evaluated attribute of the candidate drill bit can greatly reduce the influence of subjective factors and make the weight distribution more objective and reasonable. The calculation formula of entropy weight is as follows[24]: ...
Research on formation lithology interpretation and PDC bit selection method
2
2015
... Based on the data of the drill bit database, the drill bit data under abnormal working conditions such as core drilling, drilling completion, changing drilling tools and sticking were eliminated, and then the similarity of the anti-drilling ability between the formation to be drilled (5 424.87-6 109.87 m) in Well A103 and the formations drilled in adjacent wells at the same depth was analyzed. The analysis showed that the No. I and II formations to be drilled are medium soft formations with low compressive strength. For this type of formation, it is recommended to use PDC drill bits with 5-6 blades and medium cutter set density. The drill bit's crown shape is recommended to be long parabola shape or medium parabola shape, and the cutter size is recommended to be 16-19 mm[25,26,27]. The No. III and IV formations to be drilled are medium-hard formations with high compressive strength and certain degree of abrasiveness. For this type of formation, it is recommended to use PDC drill bits with 5-7 blades and medium cutter set density. The drill bit's crown shape is recommended to be medium parabolic shape or long cone shape, and the cutter size is recommended to be 16-19 mm[25,26,27]. ...
... [25,26,27]. ...
Research on PDC bits structure optimization design in middle-deep well of Daqing oilfield
2
2014
... Based on the data of the drill bit database, the drill bit data under abnormal working conditions such as core drilling, drilling completion, changing drilling tools and sticking were eliminated, and then the similarity of the anti-drilling ability between the formation to be drilled (5 424.87-6 109.87 m) in Well A103 and the formations drilled in adjacent wells at the same depth was analyzed. The analysis showed that the No. I and II formations to be drilled are medium soft formations with low compressive strength. For this type of formation, it is recommended to use PDC drill bits with 5-6 blades and medium cutter set density. The drill bit's crown shape is recommended to be long parabola shape or medium parabola shape, and the cutter size is recommended to be 16-19 mm[25,26,27]. The No. III and IV formations to be drilled are medium-hard formations with high compressive strength and certain degree of abrasiveness. For this type of formation, it is recommended to use PDC drill bits with 5-7 blades and medium cutter set density. The drill bit's crown shape is recommended to be medium parabolic shape or long cone shape, and the cutter size is recommended to be 16-19 mm[25,26,27]. ...
... ,26,27]. ...
Optimized design and selection of diamond drill bits according to the lithological characteristics of the formation
2
2003
... Based on the data of the drill bit database, the drill bit data under abnormal working conditions such as core drilling, drilling completion, changing drilling tools and sticking were eliminated, and then the similarity of the anti-drilling ability between the formation to be drilled (5 424.87-6 109.87 m) in Well A103 and the formations drilled in adjacent wells at the same depth was analyzed. The analysis showed that the No. I and II formations to be drilled are medium soft formations with low compressive strength. For this type of formation, it is recommended to use PDC drill bits with 5-6 blades and medium cutter set density. The drill bit's crown shape is recommended to be long parabola shape or medium parabola shape, and the cutter size is recommended to be 16-19 mm[25,26,27]. The No. III and IV formations to be drilled are medium-hard formations with high compressive strength and certain degree of abrasiveness. For this type of formation, it is recommended to use PDC drill bits with 5-7 blades and medium cutter set density. The drill bit's crown shape is recommended to be medium parabolic shape or long cone shape, and the cutter size is recommended to be 16-19 mm[25,26,27]. ...
