Drilling Efficiency Improvement and Rate of Penetration Optimization by Machine Learning and Data Analytics

Chandrasekaran, Sridharan; Kumar, G. Suresh

doi:10.33889/ijmems.2020.5.3.032

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…The main reason for this is the loss of energy in overcoming "drill string-well bore" friction. This situation does not correlate with the sustainable development strategy regarding the model of energy resources' efficient use [10,11].…”

Section: Introductionmentioning

confidence: 93%

An Analytical–Numerical Model for Determining “Drill String–Wellbore” Frictional Interaction Forces

Bembenek,

Grydzhuk,

Gajdzik

et al. 2024

Energies

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Currently, drilling of directional oil and gas wells under complex technical-technological and mining-geological conditions requires the use of drill pipes made of various materials. In turn, to choose rational modes of strengthening drill pipes and drill string layouts, information on the contact forces and friction forces of the drill string pipes on boreholes is necessary. Drill pipe curved sections friction with boreholes and drill bit resistance moment changes are the main causes of uneven rotation of a drill string during rotary or combined drilling methods and the occurrence of parametric oscillations. To reduce the cost of mechanical energy for well wiring, it is necessary to take into account the “drill string–borehole rocks” force interaction to estimate the magnitude of the frictional forces and their influence on the technological parameters of the drilling process. To solve this problem, mathematical models of “conventionally vertical and inclined drill string sections–borehole” were built. Based on the industrial data, an analysis of the force interaction of a deformed drill string composed of pipes made of different materials (aluminum, titanium, steel) was carried out. Analytical dependences were obtained for determining the contact forces and friction of the pipes on boreholes. A numerical study of the change of these power factors depending on the depth of the well under conditions of intensive vibration loading was carried out. The amplitude values of these forces, the frequency of their change for good sections, as well as the places for the most rational installation of drill pipes in the layout of the drill string were estimated. It was established that the intensity of contact and friction forces for steel drill pipes is greater than for titanium or aluminum ones. It is shown that the greater impact of a solid steel string on contact forces and frictional forces compared to a layout with sections of titanium or aluminum pipes in the range of vibration frequencies of 8–22 Hz corresponds to a bit rotation frequency of 70–80 rpm. The practical application of the obtained research results will contribute to the improvement of technical and economic indicators of the well drilling process.

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Section: Introductionmentioning

confidence: 93%

An Analytical–Numerical Model for Determining “Drill String–Wellbore” Frictional Interaction Forces

Bembenek,

Grydzhuk,

Gajdzik

et al. 2024

Energies

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“…For efficient groundwater acquisition, there is a need for an optimized drilling process such as rate of penetration. Article [22], [23] proposed a machine learning model for predicting the rate of penetration based on data analytics using a real-time data set. The authors consider seven input parameters for the proposed work including surface parameters and others, including torque, stand pipe pressure, differential pressure etc.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Optimization-Based Voting Classifier and Chained Multi-Objective Regressor for Effective Groundwater Resource Management

Rizwan¹,

Khan

Iqbal

et al. 2021

IEEE Access

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Water is an essential source of life for every living thing, and drilling is the only source to gain water from underground. Different advanced technologies have been used to minimize the time factor and labor force. Along with technology to be used, some other factors are equally essential to be considered, like water level, the hardness level of the land, and the number of days spent on the whole process. The study proposed a weighted voting classifier based on Differential Evaluation (DE) to classify the regions with different soil colors and land layers. The weights are assigned to the candidate classifiers based on their performance for each class. For the assignment of the optimal weight, the DE optimization algorithm is used. Moreover, the study presents a chained multi-objective regression model to simultaneously predict the water level and total depth on different locations. The proposed work facilitates the drilling industry to increase the rate of penetration (ROP) by selecting the region with soft soil and land layer. The prediction of depth and water level allows the industry to estimate water levels in different areas at different depths. The dataset is provided by the research organization, which contains information of different drilling points. The results of the proposed weighted voting classifier are compared with the traditional machine learning models (kernel Naive Bayes, Gaussian SVM, Quadratic SVM, and Bilayered Neural network) and state of the art voting classifier in terms of precision, recall, and accuracy. Moreover, the proposed regression model is evaluated by well-known evaluation metrics, including Mean Absolute Error, Mean Square Error, and R2 score. Finally, the comparison verifies the effectiveness of the enhanced optimization-based classifier and multi-objective regressor.

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Drill bit deterioration estimation with the Random Forest Regressor

Góralczyk

Michalak

Śliwiński

2021

IOP Conf. Ser.: Earth Environ. Sci.

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Blastholes drilling performance is crucial for ensuring good performance of the whole excavation process, the correctness of which demands ‘healthy’ drill bit and appropriate behavior of an operator. Given the large volume of non-linear parameters describing the process, it appears reasonable to employ supervised learning methods to obtain drilling performance insights. Random Forest Regressor model has been trained on the dataset corresponding to correct performance of blastholes drilling and its hyperparameters have been tuned to obtain the highest possible accuracy. It has been later tested on three datasets corresponding to a good performance of drilling, and two cases of its non-optimal execution. Estimation errors are proposed to be used as bit technical state condition indicators (or more generally - process performance indicators). Root Mean Squared Error has been proven to differ significantly when compared estimation based on datasets corresponding to execution of drilling with ‘healthy’ drill bit, and its execution with worn-off one, however, it has been not sufficient to distinguish non-optimal drilling when additional feed pressure has been exerted by an operator to compensate the reduced pace of drilling. It has been, however, possible when the mean of absolute estimation errors has been used.

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Drilling Efficiency Improvement and Rate of Penetration Optimization by Machine Learning and Data Analytics

Cited by 7 publications

References 12 publications

An Analytical–Numerical Model for Determining “Drill String–Wellbore” Frictional Interaction Forces

An Analytical–Numerical Model for Determining “Drill String–Wellbore” Frictional Interaction Forces

Enhanced Optimization-Based Voting Classifier and Chained Multi-Objective Regressor for Effective Groundwater Resource Management

Drill bit deterioration estimation with the Random Forest Regressor

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