Effects of PDC-Bit Selection and Mud Chemistry on Drilling Rates in Shale

Hemphill, Terry; Clark, Richard H.

doi:10.2118/22579-pa

Cited by 14 publications

(5 citation statements)

References 8 publications

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“…Maidla and Ohara [24] introduced a computer software for optimum selection of roller-cone bit type, bit rotational speed, weight on bit, and bit wearing for minimizing drilling costs. Hemphill and Clark [25] studied the effect of mud chemistry on ROP through tests conducted with different types of PDC bits and drilling muds. Fear [26] conducted a series of studies using geological and mud logging data and bit properties in order to develop a correlation for estimating ROP.…”

Section: Literature On Drilling Operationsmentioning

confidence: 99%

Applications of Artificial Intelligence Techniques in Optimizing Drilling

Koopialipoor¹,

Noorbakhsh²

2020

Emerging Trends in Mechatronics

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Artificial intelligence has transformed the industrial operations. One of the important applications of artificial intelligence is reducing the computational costs of optimization. Various algorithms based on their assumptions to solve problems have been presented and investigated, each of which having assumptions to solve the problems. In this chapter, firstly, the concept of optimization is fully explained. Then, an artificial bee colony (ABC) algorithm is used on a case study in the drilling industry. This algorithm optimizes the problem of study in combination with ANN modeling. At the end, various models are fully developed and discussed. The results of the algorithm show that by better understanding the drilling data, the conditions can be improved.

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Section: Literature On Drilling Operationsmentioning

confidence: 99%

Applications of Artificial Intelligence Techniques in Optimizing Drilling

Koopialipoor¹,

Noorbakhsh²

2020

Emerging Trends in Mechatronics

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“…Maidla and Ohara (1991) developed a computer software for optimizing the type of roller cone bit, bit speed, bit weight and bit wear to minimize the drilling cost per foot of a single bit. Hemphill and Clark (1994) considering the chemical properties of drilling mud, the effect of mud on ROP was studied by testing different types of PDC bit and drilling mud. Ritto et al (2010) carried out robust optimization of drilling speed, and introduced a new method to optimize ROP, which is a function of drilling speed and initial reaction force of bit, vibration, stress and fatigue limit of power system.…”

Section: Introductionmentioning

confidence: 99%

Real-time prediction of multivariate ROP (rate of penetration) based on machine learning regression algorithms: Algorithm comparison, model evaluation and parameter analysis

Liu

Tang

et al. 2023

Energy Exploration & Exploitation

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ROP (Rate of Penetration) is a comprehensive indicator of the rock drilling process and how efficiently predicting drilling rates is important to optimize resource allocation, reduce drilling costs and manage drilling hazards. However, the traditional model is difficult to consider the multiple factors, which makes the prediction accuracy difficult to meet the real drilling requirements. In order to provide efficient, accurate and comprehensive information for drilling operation decision-making, this study evaluated the applicability of four typical regression algorithms based on machine learning for predicting pore pressure in Troll West field, namely SVR (Support Vector Regression), Linear regression, Regression Tree and Gradient Boosting regression. These methods allow more parameters input. By comparing the prediction results of these typical regression algorithms based on R2(R-Square), explained variance, mean absolute error, mean squared error, median absolute error and other performance indicators, it was found that each method predicted different results, among which Gradient Boosting regression has the best results, their prediction accuracy is high and the error is very low. The prediction accuracy of these methods is positively correlated with the proportion of the training data set. With the increase of logging features, the prediction accuracy is gradually improved. In the prediction of adjacent wells, the ROP prediction methods can achieve a certain prediction effect, which shows that this method is suitable for ROP prediction in Troll West field.

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“…In summary, the impacts of diverse design features on the breaking process were discerned via a semi-quantitative analysis using the FEM method. Experimental methods are considerably more costly, often depending on intricate manufacturing techniques, the prototyping of various PDC bits, and engineering trials with an extremely low margin for error and complicated manual interference [6][7][8]. However, most of the highlighted simulation studies have centered primarily on effects related to the profile, single cutter, and drilling parameters of a singular type of bit, with only a handful exploring bit selection and further design considerations.…”

Section: Introductionmentioning

confidence: 99%

Selection and Optimization Design of PDC Bits Based on FEM Analysis for Drilling Long Horizontal Sections of Shale Formations

Kong,

Wang,

Wang

et al. 2023

Processes

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Well structures with ultra-long sections have become one of the most applied technologies in the field of shale gas development. While there have been many technical challenges, enhancing the breaking efficiency and stability of polycrystalline diamond compact (PDC) bits has become an essential issue of focus. Since 2013, the well structure in the Duvernay area has been optimized multiple times, and the rate of penetration (ROP) of the entire wellbore has nearly doubled. However, there are significant differences in terms of the performances of different PDC bits, and there is still room for improvement to optimize these drill bits. For this reason, a confined compressive strength test was conducted to obtain the rock mechanical parameters from shale cores extracted from the long horizontal section. Using these data, a finite element model (FEM) was developed with a corresponding scale. A calibration of the elastic-plastic damage constitutive models was then performed using the FEM. The breaking mechanism of three different PDC bits was examined using a “PDC bit-bottom hole” interaction FEM model, facilitating guidance for bit selection and design optimization: (1) The type B PDC bit, which has four blades and 20 cutters, exhibited the highest mechanical specific energy (MSE) and the lowest vibration across three directional mechanical characteristics. This design is recommended for engineering applications. (2) Lower axial vibrations were produced when the CDE was used as the rear element when compared to those when using the BHE. However, an increase within an acceptable range was observed in the TOB and circumferential vibrations. Thus, for redesigning work on the type B bit, the assembly of the CDE is suggested. (3) A decrease in the MSE and vibration in three directional mechanical characteristics was observed when the depth of cut (DOC) was varied between 1.5 and 2.0 mm. A broadening in the range of lateral forces was noted when a DOC of 2.0 mm was used. Therefore, for the redesign of the type B bit, the assembly of CDEs as rear elements at a DOC of 1.5 mm is recommended. In conclusion, a new practical method for the selection and optimization of PDC bit design, based on rock mechanics and the FEM theory, is proposed.

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Effects of PDC-Bit Selection and Mud Chemistry on Drilling Rates in Shale

Cited by 14 publications

References 8 publications

Applications of Artificial Intelligence Techniques in Optimizing Drilling

Applications of Artificial Intelligence Techniques in Optimizing Drilling

Real-time prediction of multivariate ROP (rate of penetration) based on machine learning regression algorithms: Algorithm comparison, model evaluation and parameter analysis

Selection and Optimization Design of PDC Bits Based on FEM Analysis for Drilling Long Horizontal Sections of Shale Formations

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