ArticleScientific reports2024
Hybrid physics-machine learning models for predicting rate of penetration in the Halahatang oil field, Tarim Basin.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Physics-Informed CNN-BiGRU Model for Downhole Weight-on-Bit Prediction Using Surface Measurement-While-Drilling Sensor Data in Horizontal Wells.Sensors (Basel, Switzerland) · 2026Article
- Multi-Pattern Generalization in COACS omega · 2026Article
- Residual-hybrid dynamic response-surface/machine-learning framework for predicting malachite green removal and biodegradation by a bacterial consortium.Journal of biological engineering · 2026Article
- Source identification of sudden water pollution events in the Dongliao River using a hybrid machine learning framework.Scientific reports · 2026Article
- Research on deep well drilling rate of penetration prediction method based on TCN-transformer model.Scientific reports · 2026Article
- Determining acoustic impedance cube by inverting seismic data using feedforward and radial basis neural networks in an Iranian oilfield.Scientific reports · 2026Article
- Experimental Evaluation of the Treatment Effect of High Viscosity Drilling Fluid and Floating Oil Using Ozone Fine Bubble Technology.Nanomaterials (Basel, Switzerland) · 2025Article
- Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data.Scientific reports · 2025Article
- A Physics-Guided Machine Learning Model for Predicting Viscoelasticity of Solids at Large Deformation.Polymers · 2024Article
- Accurate modeling of crude oil and brine interfacial tension via robust machine learning approaches.Scientific reports · 2024Article
- Prediction of soil organic carbon and total nitrogen affected by mine using Vis-NIR spectroscopy coupled with machine learning algorithms in calcareous soils.Scientific reports · 2024Article
- A rate of penetration (ROP) prediction method based on improved dung beetle optimization algorithm and BiLSTM-SA.Scientific reports · 2024Article
- Research on intelligent decision support systems for oil and gas exploration based on machine learning.PloS one · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Rate of penetration (ROP) is a key factor in drilling optimization, cost reduction and drilling cycle shortening. Due to the systematicity, complexity and uncertainty of drilling operations, however, it has always been a problem to establish a highly accurate and interpretable ROP prediction model to guide and optimize drilling operations. To solve this problem in the Tarim Basin, this study proposes four categories of hybrid physics-machine learning (ML) methods for modeling. One of which is residual modeling, in which an ML model learns to predict errors or residuals, via a physical model; the second is integrated coupling, in which the output of the physical model is used as an input to the ML model; the third is simple average, in which predictions from both the physical model and the ML model are combined; and the last is bootstrap aggregating (bagging), which follows the idea of ensemble learning to combine different physical models' advantages. A total of 5655 real data points from the Halahatang oil field were used to test the performance of the various models. The results showed that the residual modeling model, with an R
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