A novel rate of penetration prediction model with identified condition for the complex geological drilling process
TL;DR: Considering the drilling characteristics of strong nonlinearity, complexity, multiple variables and drilling conditions in drilling process, an online hybrid prediction model based on the drilling data is developed to achieve high accuracy prediction of the ROP as discussed by the authors.
About: This article is published in Journal of Process Control.The article was published on 2021-04-01. It has received 13 citations till now. The article focuses on the topics: Rate of penetration.
Citations
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TL;DR: Wang et al. as discussed by the authors proposed a dynamic model for ROP prediction considering the process characteristics, which consists of three stages, and two steps (modeling and prediction) are executed alternately in the moving drilling depth windows so as to predict the ROP more accurately.
13 citations
TL;DR: In this paper , an attention-based Gated Recurrent Unit network and fully connected neural networks were proposed to predict the rate of penetration in the field with high accuracy and robustness.
9 citations
TL;DR: In this article , a hybrid bat algorithm (HBA) and support vector regression (SVR) were used to predict rate of penetration (ROP) and mud pit volume (MPV).
3 citations
TL;DR: A two-level combination strategy is proposed to improve prediction performance and assessment accuracy, with the using of bootstrap aggregating, linear combination, and majority voting, which has better prediction performance than single NNs.
3 citations
15 Sep 2021
TL;DR: In this paper, a fuzzy neural network (FNN) is applied to the field of drilling engineering for the first time, aiming at the coupling problem to predict the rate of penetration (ROP).
Abstract: The rate of penetration (ROP) is an index used to measure drilling efficiency. However, it is restricted by many factors, and there is a coupling relationship among them. In this study, the random forest algorithm is used to sort influencing factors in order of feature importance. In this way, less influential factors can be removed. A fuzzy neural network (FNN) is applied to the field of drilling engineering for the first time, aiming at the coupling problem to predict the ROP. Fuzzification is an important part of training and realizing FNN, but research on this topic is currently lacking. In this study, K-means are used to divide the data with high similarity into a fuzzy set, which is used as the initialization parameter for the second layer of the FNN. The data of Shunbei No. 1 and 5 fault zones in Xinjiang are collected and trained. The results show that the mean value of the coefficient of determination R2 is 0.9668 under 10 experiments, which is higher than those obtained from a back propagation neural network and multilayer perceptron particle swarm optimization methods. Therefore, the effectiveness and feasibility of the model are verified. The proposed model can improve drilling efficiency and save drilling costs.
2 citations
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Proceedings Article•
01 Jan 2001TL;DR: Dynamic time warping (DTW), is a technique for efficiently achieving this warping of sequences that have the approximately the same overall component shapes, but these shapes do not line up in X-axis.
Abstract: Time series are a ubiquitous form of data occurring in virtually every scientific discipline. A common task with time series data is comparing one sequence with another. In some domains a very simple distance measure, such as Euclidean distance will suffice. However, it is often the case that two sequences have the approximately the same overall component shapes, but these shapes do not line up in X-axis. Figure 1 shows this with a simple example. In order to find the similarity between such sequences, or as a preprocessing step before averaging them, we must "warp" the time axis of one (or both) sequences to achieve a better alignment. Dynamic time warping (DTW), is a technique for efficiently achieving this warping. In addition to data mining (Keogh & Pazzani 2000, Yi et. al. 1998, Berndt & Clifford 1994), DTW has been used in gesture recognition (Gavrila & Davis 1995), robotics (Schmill et. al 1999), speech processing (Rabiner & Juang 1993), manufacturing (Gollmer & Posten 1995) and medicine (Caiani et. al 1998).
1,131 citations
281 citations
TL;DR: A novel SCA-SVR model has been presented where sine cosine algorithm (SCA) is used to select the penalty and kernel parameters in SVR, so that the generalization performance on unknown data can be improved.
Abstract: Support vector regression is employed as a time series prediction model.A sine cosine algorithm based method is proposed for parameter tuning of SVR.The proposed SCA-SVR model is compared to other meta-heuristics algorithms.Benchmarks are selected to cover a range of possible practical situations.The SCA-SVR method has been demonstrated to be feasible efficiently and reliably. Time series prediction is an important part of data-driven based prognostics which are mainly based on the massive sensory data with less requirement of knowing inherent system failure mechanisms. Support Vector Regression (SVR) has achieved good performance in forecasting problems of small samples and high dimensions. However, the SVR parameters have a significant influence on forecasting performance of SVR. In our current work, a novel SCA-SVR model has been presented where sine cosine algorithm (SCA) is used to select the penalty and kernel parameters in SVR, so that the generalization performance on unknown data can be improved. To validate the proposed model, the results of the SCA-SVR algorithm were compared with those of grid search and some other meta-heuristics optimization algorithms on common used benchmark datasets. The experimental results proved that the proposed model is capable to find the optimal values of the SVR parameters and can yield promising results.
140 citations
TL;DR: The authors have formulated a method to calculate the uncertainty (confidence interval) of ROP predictions, which can be useful in engineering based drilling decisions and provide a better fit than traditional models.
115 citations
TL;DR: In this article, the effects of both drilling mud properties and initial reservoir conditions on the wellbore stability were further investigated under the condition of drilling mud invasion into the natural gas hydrate-bearing sediments.
100 citations