The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
K E Gray - One of the best experts on this subject based on the ideXlab platform.
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Rate of Penetration rop optimization in drilling with vibration control
Journal of Natural Gas Science and Engineering, 2019Co-Authors: Chiranth Hegde, Harry Millwater, Michael J Pyrcz, Hugh Daigle, K E GrayAbstract:Abstract Drilling optimization is typically tackled by optimizing the Rate of Penetration (ROP). However, most ROP optimization models do not consider the effect of drilling vibrations, a major ROP inhibitor. To resolve this limitation, this paper introduces a workflow that combines the ROP optimization process with a machine learning-based vibration model. This model determines optimal drilling parameters that not only increase ROP but mitigate excessive vibrations. Analytical ROP models are used to model the ROP in a given formation. An optimization algorithm (gradient ascent with random restarts) is used to find the optimal drilling control parameters, weight on bit (WOB) and revolutions per minute (RPM), required to improve ROP ahead of the bit. The vibrations classification model is used as an equality constraint for the optimization algorithm, to set bounds on the optimization space, ensuring that the selected optimal parameters do not result in excessive vibrations when implemented. The model, when evaluated on field data, shows an improvement of ROP by 14.1% (10 ft/h) on average across all formations when compared to the measured data. A case study has been discussed to illustRate the use of this approach in drilling practice. This model introduces a novel way to couple drilling ROP models with vibration models to improve ROP and to mitigate vibrations.
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analysis of Rate of Penetration rop prediction in drilling using physics based and data driven models
Journal of Petroleum Science and Engineering, 2017Co-Authors: Chiranth Hegde, Harry Millwater, Hugh Daigle, K E GrayAbstract:Abstract Modeling the Rate of Penetration of the drill bit is essential for optimizing drilling operations. This paper evaluates two different approaches to ROP prediction: physics-based and data-driven modeling approach. Three physics-based models or traditional models have been compared to data-driven models. Data-driven models are built using machine learning algorithms, using surface measured input features - weight-on-bit, RPM, and flow Rate – to predict ROP. Both models are used to predict ROP; models are compared with each other based on accuracy and goodness of fit (R 2 ). Based on the results from these simulations, it was concluded that data-driven models are more accuRate and provide a better fit than traditional models. Data-driven models performed better with a mean error of 12% and improve the R 2 of ROP prediction from 0.12 to 0.84. The authors have formulated a method to calculate the uncertainty (confidence interval) of ROP predictions, which can be useful in engineering based drilling decisions.
R. Sampaio - One of the best experts on this subject based on the ideXlab platform.
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Robust optimization of horizontal drillstring Rate of Penetration through a nonlinear stochastic dynamic model
2015Co-Authors: Americo Cunha Jr, Christian Soize, R. SampaioAbstract:A drillstring is a long column under rotation, composed by a sequence of connected drill-pipes and auxiliary equipment, which is used to drill the soil in oil prospecting. During its operation, this column presents a three-dimensional dynamics, subjected to longitudinal, lateral, and torsional vibrations, besides the effects of friction, shock, and bit-rock interaction. The study of the dynamics of this equipment is very important in many engineering applications, especially to reduce costs in the oil exploration process. In this sense, this work aims to formulate and solve a robust optimization problem that seeks to maximize horizontal drillstrings Rate of Penetration into of the soil, subjected to the restriction imposed by the structural limits of the column. To analyze the nonlinear dynamics of drillstrings in horizontal configuration, a computational model, which uses a nonlinear beam theory of Timoshenko type is considered. This model also takes into account the effects of friction and shock, induced by the lateral impacts between the drill-string and borehole wall, as well as bit-rock interaction effects. The uncertainties of the bit-rock interaction model are taken into account using a parametric probabilistic approach. Two optimizations problems (one deterministic and one robust), where the objective is to maximize the drillstring Rate of Penetration (ROP) into the soil, respecting its structural limits, are formulated and solved. In order to optimize the ROP, it is possible to vary the drillstring velocities of translation and rotation. The solutions of these optimization problems provided two different stRategies to maximize the ROP.
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Robust optimization of the Rate of Penetration of a drill-string using a stochastic nonlinear dynamical model
Computational Mechanics, 2010Co-Authors: T.g. Ritto, Christian Soize, R. SampaioAbstract:This work proposes a stRategy for the robust optimization of the nonlinear dynamics of a drill-string, which is a structure that rotates and digs into the rock to search for oil. The nonparametric probabilistic approach is employed to model the uncertainties of the structure as well as the uncertainties of the bit-rock interaction model. This paper is particularly concerned with the robust optimization of the Rate of Penetration of the column, i.e., we aim to maximize the mathematical expectation of the mean Rate of Penetration, respecting the integrity of the system. The variables of the optimization problem are the rotational speed at the top and the initial reaction force at the bit; they are considered deterministic. The goal is to find the set of variables that maximizes the expected mean Rate of Penetration, respecting, vibration limits, stress limit and fatigue limit of the dynamical system.
Abdulaziz Al-majed - One of the best experts on this subject based on the ideXlab platform.
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Coupling Rate of Penetration and mechanical specific energy to Improve the efficiency of drilling gas wells
Journal of Natural Gas Science and Engineering, 2020Co-Authors: Amjed Hassan, Salaheldin Elkatatny, Abdulaziz Al-majedAbstract:Abstract Drilling operations for oil or gas wells are very expensive. Optimizing the drilling efficiency and increasing the Rate of Penetration (ROP) will reduce the overall cost of the drilling operation. Different approaches are utilized to increase the drilling performance including analytical and empirical methods. However, most of the available models have some limitations once they applied for real-time drilling operations. This study presents a new approach for evaluating and improving the drilling operations for real-time applications. Seven ROP models were developed using artificial neural network (ANN) technique. The ANN models were developed using real field data (20,000 data sets) which includes the real-time recording of the surface drilling parameters such as Rate of Penetration (ROP), weight on bit (WOB), torque, rotation speed (RPM) and mud flow Rate (Q). The ANN-based models are able to provide an accuRate estimation for the full ROP profile. Thereafter, the predicted ROP was coupled with the calculated MSE in order to optimize the drilling performance. Moreover, and for the first time, a new (ROP/MSE) ratio is suggested, which can be used to assess the drilling operations in a real-time. A new profile of ROP/MSE ratio can be displayed along with the drilling parameter in order to provide a quick and more reliable evaluation for the ongoing drilling operation. The suggested ratio incorpoRates the impacts of the primary drilling factors which are the drilling speed (ROP) and the required drilling energy (MSE). Also, the seven ANN-based models, developed in this work, can predict the full profile of ROP with an average absolute percentage error (AAPE) of around 7.9% and a correlation coefficient of around 0.92.
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A Robust Rate of Penetration Model for Carbonate Formation
Journal of Energy Resources Technology, 2018Co-Authors: Ahmad Al-abduljabbar, Salaheldin Elkatatny, Mohamed Mahmoud, Khaled Abdelgawad, Abdulaziz Al-majedAbstract:During the drilling operations, optimizing the Rate of Penetration (ROP) is very crucial, because it can significantly reduce the overall cost of the drilling process. ROP is defined as the speed at which the drill bit breaks the rock to deepen the hole, and it is measured in units of feet per hour or meters per hour. ROP prediction is very challenging before drilling, because it depends on many parameters that should be optimized. Several models have been developed in the literature to predict ROP. Most of the developed models used drilling parameters such as weight on bit (WOB), pumping Rate (Q), and string revolutions per minute (RPM). Few researchers considered the effect of mud properties on ROP by including a small number of actual field measurements. This paper introduces a new robust model to predict the ROP using both drilling parameters (WOB, Q, ROP, torque (T), standpipe pressure (SPP), uniaxial compressive strength (UCS), and mud properties (density and viscosity) using 7000 real-time data measurements. In addition, the relative importance of drilling fluid properties, rock strength, and drilling parameters to ROP is determined. The obtained results showed that the ROP is highly affected by WOB, RPM, T, and horsepower (HP), where the coefficient of determination (T2) was 0.71, 0.87, 0.70, and 0.92 for WOB, RPM, T, and HP, respectively. ROP also showed a strong function of mud fluid properties, where R2 was 0.70 and 0.70 for plastic viscosity (PV) and mud density, respectively. No clear relationship was observed between ROP and yield point (YP) for more than 500 field data points. The new model predicts the ROP with average absolute percentage error (AAPE) of 5% and correlation coefficient (R) of 0.93. In addition, the new model outperformed three existing ROP models. The novelty in this paper is the application of the clustering technique in which the formations are clustered based on their compressive strength range to predict the ROP. Clustering yielded accuRate ROP prediction compared to the field ROP.
Chiranth Hegde - One of the best experts on this subject based on the ideXlab platform.
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Rate of Penetration rop optimization in drilling with vibration control
Journal of Natural Gas Science and Engineering, 2019Co-Authors: Chiranth Hegde, Harry Millwater, Michael J Pyrcz, Hugh Daigle, K E GrayAbstract:Abstract Drilling optimization is typically tackled by optimizing the Rate of Penetration (ROP). However, most ROP optimization models do not consider the effect of drilling vibrations, a major ROP inhibitor. To resolve this limitation, this paper introduces a workflow that combines the ROP optimization process with a machine learning-based vibration model. This model determines optimal drilling parameters that not only increase ROP but mitigate excessive vibrations. Analytical ROP models are used to model the ROP in a given formation. An optimization algorithm (gradient ascent with random restarts) is used to find the optimal drilling control parameters, weight on bit (WOB) and revolutions per minute (RPM), required to improve ROP ahead of the bit. The vibrations classification model is used as an equality constraint for the optimization algorithm, to set bounds on the optimization space, ensuring that the selected optimal parameters do not result in excessive vibrations when implemented. The model, when evaluated on field data, shows an improvement of ROP by 14.1% (10 ft/h) on average across all formations when compared to the measured data. A case study has been discussed to illustRate the use of this approach in drilling practice. This model introduces a novel way to couple drilling ROP models with vibration models to improve ROP and to mitigate vibrations.
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analysis of Rate of Penetration rop prediction in drilling using physics based and data driven models
Journal of Petroleum Science and Engineering, 2017Co-Authors: Chiranth Hegde, Harry Millwater, Hugh Daigle, K E GrayAbstract:Abstract Modeling the Rate of Penetration of the drill bit is essential for optimizing drilling operations. This paper evaluates two different approaches to ROP prediction: physics-based and data-driven modeling approach. Three physics-based models or traditional models have been compared to data-driven models. Data-driven models are built using machine learning algorithms, using surface measured input features - weight-on-bit, RPM, and flow Rate – to predict ROP. Both models are used to predict ROP; models are compared with each other based on accuracy and goodness of fit (R 2 ). Based on the results from these simulations, it was concluded that data-driven models are more accuRate and provide a better fit than traditional models. Data-driven models performed better with a mean error of 12% and improve the R 2 of ROP prediction from 0.12 to 0.84. The authors have formulated a method to calculate the uncertainty (confidence interval) of ROP predictions, which can be useful in engineering based drilling decisions.
Hamid Reza Ansari - One of the best experts on this subject based on the ideXlab platform.
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Drilling Rate of Penetration prediction through committee support vector regression based on imperialist competitive algorithm
Carbonates and Evaporites, 2017Co-Authors: Hamid Reza Ansari, Mohammad Javad Sarbaz Hosseini, Masoud AmirpourAbstract:Rate of Penetration (ROP) is an important parameter affecting the drilling optimization during well planning. This is particularly important for offshore wells because, offshore rigs contain daily expensive cost and therefore ROP plays a critical role in minimizing time and cost of drilling. There are many factors that affect the ROP such as mud, formation, bit and drilling parameters. In the first step of this study, the best parameters to predict ROP, are selected by error analysis of multivariate regression and then ROP modeling is performed by means of various support vector regression (SVR) methods. Fundamental difference between the individual models is type of kernel function. Finally, a committee machine is constructed in power law framework and it is optimized with imperialist competitive algorithm (ICA). This novel technique is called committee support vector regression based on imperialist competitive algorithm (CSVR-ICA) in this study. Data set are gathered from three jack-up drilling rigs. Results show that CSVR-ICA model improved the results of individual SVR models and it has a good performance in the ROP estimation.
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Optimized support vector regression for drilling Rate of Penetration estimation
Open Geosciences, 2015Co-Authors: Asadollah Bodaghi, Hamid Reza Ansari, Mahsa GholamiAbstract:Abstract In the petroleum industry, drilling optimization involves the selection of operating conditions for achieving the desired depth with the minimum expenditure while requirements of personal safety, environment protection, adequate information of penetRated formations and productivity are fulfilled. Since drilling optimization is highly dependent on the Rate of Penetration (ROP), estimation of this parameter is of great importance during well planning. In this research, a novel approach called ‘optimized support vector regression’ is employed for making a formulation between input variables and ROP. Algorithms used for optimizing the support vector regression are the genetic algorithm (GA) and the cuckoo search algorithm (CS). Optimization implementation improved the support vector regression performance by virtue of selecting proper values for its parameters. In order to evaluate the ability of optimization algorithms in enhancing SVR performance, their results were compared to the hybrid of pattern search and grid search (HPG) which is conventionally employed for optimizing SVR. The results demonstRated that the CS algorithm achieved further improvement on prediction accuracy of SVR compared to the GA and HPG as well. Moreover, the predictive model derived from back propagation neural network (BPNN), which is the traditional approach for estimating ROP, is selected for comparisons with CSSVR. The comparative results revealed the superiority of CSSVR. This study inferred that CSSVR is a viable option for precise estimation of ROP.
