Hole Cleaning Prediction in Foam Drilling Using Artificial Neural Network and Multiple Linear Regression

Rooki, Reza; Ardejani, Faramarz Doulati; Moradzadeh, Ali

doi:10.4236/gm.2014.41005

Cited by 27 publications

(10 citation statements)

References 24 publications

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“…Rooki et al [22] described a simple and more reliable ANN method and multiple linear regressions (MLR) for the prediction of cutting concentration during foam drilling operation. The results indicated the high ability in the prediction of ANN methods.…”

Section: Methodsmentioning

confidence: 99%

Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies

2018

SV-JME

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Many developed approaches for the improvement of sustainability during machining operations; one of which is the optimized utilization of cutting tools. Increasing the efficient use of cutting tool results in better product quality and longer tool life. Drilling is one of the most popular manufacturing processes in the metal-cutting industry. It is usually carried out at the final steps of the production process. In this study, the effects of cutting parameters (cutting velocity, feed rate) and tool diameter on thrust force (Fz) and torque (Mz) are investigated in the drilling of an Al7075 workpiece using solid carbide tools. The full factorial experimental design is implemented in order to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and response surface methodology (RSM) approaches are used to acquire mathematical models for both the thrust force (Fz) and torque (Mz) related to the drilling process. RSM-and ANN-based models are compared, and it is clearly determined that the proposed models are capable of predicting the thrust force (Fz) and torque (Mz). Nevertheless, the ANN models estimate in a more accurate way the outputs used in comparison to the RSM models.

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Section: Methodsmentioning

confidence: 99%

Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies

2018

SV-JME

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“…A comparison of the results obtained from BPNNs and MLR depicts pronounced error reduction using BPNNs, showing improved ability of ANNs in prediction compared to other computational techniques (Rooki et al, 2014). Drillstring vibrations (axial, lateral, and torsional) are a frequent problem hindering smooth and optimum drilling conditions.…”

Section: Figure 7 Important Applications Of Ai In Drilling Optimizationmentioning

confidence: 99%

Application Of Artificial Intelligence Methods In Drilling System Design And Operations: A Review Of The State Of The Art

Bello

Holzmann

Yaqoob

et al. 2015

Journal of Artificial Intelligence and Soft Computing Research

111

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Artificial Intelligence (AI) can be defined as the application of science and engineering with the intent of intelligent machine composition. It involves using tool based on intelligent behavior of humans in solving complex issues, designed in a way to make computers execute tasks that were earlier thought of human intelligence involvement. In comparison to other computational automations, AI facilitates and enables time reduction based on personnel needs and most importantly, the operational expenses.Artificial Intelligence (AI) is an area of great interest and significance in petroleum exploration and production. Over the years, it has made an impact in the industry, and the application has continued to grow within the oil and gas industry. The application in E & P industry has more than 16 years of history with first application dated 1989, for well log interpretation; drill bit diagnosis using neural networks and intelligent reservoir simulator interface. It has been propounded in solving many problems in the oil and gas industry which includes, seismic pattern recognition, reservoir characterisation, permeability and porosity prediction, prediction of PVT properties, drill bits diagnosis, estimating pressure drop in pipes and wells, optimization of well production, well performance, portfolio management and general decision making operations and many more.This paper reviews and analyzes the successful application of artificial intelligence techniques as related to one of the major aspects of the oil and gas industry, drilling capturing the level of application and trend in the industry. A summary of various papers and reports associated with artificial intelligence applications and it limitations will be highlighted. This analysis is expected to contribute to further development of this technique and also determine the neglected areas in the field.

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“…These dimensionless numbers are functions of inclination angle, feed cutting concentration, fluid density, fluid viscosity, average velocity, pipes dimensions and the wellbore. Rooki et al (2014) and Rooki and Rakhshkhorshid (2017) used the BPNN and the radial basis neural networks (RBFN) for hole-cleaning prediction in foam drilling. In both studies, the authors used experimental data containing the foam quality, foam velocity, eccentricity, pipe rotational speed (RPM) and subsurface conditions (e.g.…”

Section: Applications Of Artificial Intelligence In Petroleum Industrymentioning

confidence: 99%

Cutting concentration prediction in horizontal and deviated wells using artificial intelligence techniques

Al-Azani

Elkatatny

Ali

et al. 2019

J Petrol Explor Prod Technol

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Improper hole cleaning or drilled-cutting transportation impacts drilling operations. To illustrate, inefficient cleaning of the wellbore can lead to many drilling problems such as low drilling rate (i.e. low ROP), early bit wear and, in the severe cases, a complete loss of the well due to stuck pipe. To understand efficiency in cutting transport in drilling and to provide solutions for the problem, many studies have been conducted. In all cases, they provide empirical models based on experimental data. In this study, different artificial intelligence (AI) techniques are employed to estimate the concentration of cuttings present in the wellbore. The purpose of this study is to indirectly measure the hole-cleaning efficiency in order to predict the cutting concentration from drilling parameters using artificial intelligence techniques. The study is based on 116 experimental data records from the studies. Two AI techniques were selected, namely artificial neural network (ANN) and support vector machine (SVM), to estimate the cutting concentration in the wellbore. The input parameters comprise mud density and mud rheological properties (yield point and plastic viscosity) in addition to drilling parameters including the hole inclination angle, pipe eccentricity (i.e. location of the drill pipe from the axis of the well), the rate of penetration (ROP), flow rate (GPM), drill pipe rotary speed (RPM) and temperature. The results obtained show the ability of the two employed techniques to accurately predict the cutting concentration in the wellbore with average absolute errors (AAE) less than 5% and correlation coefficients (R) higher than 0.9. Comparison of these results with a literature model showed that the AI techniques provide better predictions of cutting concentration and higher accuracy than that model. Applying the developed AI technique will help the drilling engineers to assess the hole cleaning in a real time.

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Hole Cleaning Prediction in Foam Drilling Using Artificial Neural Network and Multiple Linear Regression

Cited by 27 publications

References 24 publications

Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies

Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies

Application Of Artificial Intelligence Methods In Drilling System Design And Operations: A Review Of The State Of The Art

Cutting concentration prediction in horizontal and deviated wells using artificial intelligence techniques

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