Rate of penetration modeling using hybridization extreme learning machine and whale optimization algorithm

Youcefi, Mohamed Riad; Hadjadj, Ahmed; Bentriou, Abdelhak; Boukredera, Farouk Said

doi:10.1007/s12145-020-00524-y

Cited by 17 publications

(5 citation statements)

References 60 publications

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“…A code verification is done to identify and remove the errors in the implementation process. A theory validation has been performed using a validated Artificial neural network model for ROP calculations [31] as the system was not employed on a real rig. The Simulation outcomes are compared to the real ROP to determine how the model can improve and extend the parametric range.…”

Section: Methodsmentioning

confidence: 99%

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New automated drilling controller for drill string vibrations mitigation and rate of penetration optimization using a fuzzy logic system.

BOUKREDERA,

Youcefi,

Hadjadj

2023

Preprint

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Drill string vibration is one of the limiting factors that affect the maximum drilling performance, and at the same time causes premature failure of drill string components. Optimizing the drilling process in order to enhance efficiency requires a valuable vibration mitigation scheme to increase penetration rate. This article introduces a novel strategy to control drilling parameters to reduce drill string vibration and optimize ROP. In the system development process, various interesting topics have been studied, such as the performance of the controller (using MATLAB Fuzzy Logic toolbox), the application of artificial neural networks in ROP prediction, and drill string modeling. The proposed strategy uses multiple inputs such as surface drilling parameters variation (RPM, WOB, and Torque) together with predictive vibration severity estimate to detect drilling vibrations and adjust related parameters to suppress severe oscillations and avoid unexpected events that lead to non-productive time. The fuzzy logic controller shows overall stability and robustness, the controlled parameters follow the rules used in the fuzzy set which are developed by analyzing data from Algerian oil wells and simulating the "experience and expertise" of decision-makers. The system is multi-objective optimization; can detect inefficiencies, mitigate vibration, and enhance ROP. The artificial neural network ROP model, when simulated using the field data, shows an improvement of ROP by 12% on average across all the drilled formations when compared to the recorded data. A case study is presented to illustrate the application of this method in drilling practice.

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Section: Methodsmentioning

confidence: 99%

“…Then, their summation is subjected to a transformation through a function called transfer function or activation function. Outputs of each layer are taken as inputs for the next layer [38]. The output of MLP neural network can be explained as in the equation:…”

Section: Multi-layer Perceptron Neural Networkmentioning

confidence: 99%

New automated drilling controller for drill string vibrations mitigation and rate of penetration optimization using a fuzzy logic system.

BOUKREDERA,

Youcefi,

Hadjadj

2023

Preprint

Self Cite

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“…In the whole recommended algorithm work process, users do not need to provide any explicit requirements. The algorithm predicts the targets that are likely to be interested in the future based on the user's historical behavior data, and then makes recommendations [5][6]. Neighborhood collaborative filtering, also known as memory collaborative filtering, is characterized by intuitiveness, easy implementation, and a process that does not require too long training, and has been fully applied and developed [7][8].…”

Section: Principle Of Collaborative Filtering Recommendation Algorithmmentioning

confidence: 99%

Collaborative Filtering Algorithm based on Hybrid Machine Learning Optimization

2020

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Under the influence of the mobile Internet era, users' patience is increasingly limited. Future recommendation algorithms should quickly respond to users' urgent needs to save users' time. Under the background of big data, how to ensure the relatively low complexity and high accuracy of information push and display is a very valuable topic. Therefore, this paper discusses and analyzes the collaborative filtering algorithm (CFA) based on hybrid machine learning (ML) optimization. In this paper, the research background and significance of CFA are first described, including the development status of collaborative filtering recommendation algorithm, the research status of association rule recommendation algorithm and particle swarm optimization algorithm, and the problems in collaborative filtering recommendation algorithm. A new hybrid CFA model is proposed based on the hybrid ML optimization. This paper designs a simulation table of three recommendation algorithms to verify the proposed CFA. The experiment shows that the recall rate of the CFA proposed in this paper based on the hybrid ML optimization is not lower than other recommendation algorithms, which verifies the effectiveness of this algorithm.

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“…Those factors are classified as the controllable and uncontrollable factors. The adjustable factors, e.g., revolutions per minute (RPM), mud-flow rate (Q), torque (T), and weight on bit (WOB), can be changed and controlled by the operators [4]. By contrast, some other factors cannot be changed due to technological limitations, or geological conditions.…”

Section: Introductionmentioning

confidence: 99%

A Survey on the Shortcomings of the Current Rate of Penetration Predictive Models in Petroleum Engineering

Khalilidermani

Knez

2023

Energies

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In drilling engineering, the rate of penetration (ROP) is a prevalent indicator to evaluate the energy efficiency of drilling operations. Nowadays, ROP prediction has become more critical since the production from deeper hydrocarbon resources is unprecedentedly escalating. So far, a wealth of theoretical and practical investigations has been conducted to develop ROP models; however, the existing models have not been adequately updated with the new technological advancements or geological restrictions. This research strives to integrate the latest advancements, restrictions, and future requirements in ROP prediction. To do this, the existing empirical and data-driven ROP models are elaborated and compared. From the conducted research, it was deduced that four uncontrollable factors, including the rock permeability, wellbore inclination, temperature, and rock hardness, have not been adequately considered in ROP models. Moreover, although data-driven ROP models deliver more accurate results than the empirical models, the determination of the number and type of the input parameters is still challenging. To tackle this issue, it is recommended to develop a formation-based classification system of input parameters for future investigations. This inclusive review can be adopted by the companies and engineers involved in drilling operations to update and reform their drilling strategies.

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Rate of penetration modeling using hybridization extreme learning machine and whale optimization algorithm

Cited by 17 publications

References 60 publications

New automated drilling controller for drill string vibrations mitigation and rate of penetration optimization using a fuzzy logic system.

New automated drilling controller for drill string vibrations mitigation and rate of penetration optimization using a fuzzy logic system.

Collaborative Filtering Algorithm based on Hybrid Machine Learning Optimization

A Survey on the Shortcomings of the Current Rate of Penetration Predictive Models in Petroleum Engineering

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