Although the machine-learning model demonstrates high accuracy in wind speed prediction, it struggles to accurately depict the fluctuation range of the predicted values due to the inherent uncertainty in wind speed sequences. To address this limitation and enhance the reliability, we propose an effective wind speed interval prediction model that combines twin support vector regression (TSVR), variational mode decomposition (VMD), and the slime mould algorithm (SMA). In our methodology, the complex wind speed series is decomposed into multiple relatively stable subsequences using the VMD method. The principal component and residual series are then subject to interval prediction using the TSVR model, while the remaining components undergo point prediction. The SMA method is employed to search for optimal parameter combinations. The prediction interval of wind speed is obtained by aggregating the forecasting results of all TSVR models for each subseries. Our proposed model has demonstrated superior performance in various applications. It ensures that the wind speed value falls within the designated interval range while achieving the narrowest prediction interval. For instance, in the spring dataset with 1-period, we obtained a predicted interval with a prediction intervals coverage probability (PICP) value of 0.9791 and prediction interval normalized range width (PINRW) value of 0.0641. This outperforms other comparative models and significantly enhances its practical application value. After adding the residual interval prediction model, the reliability of the prediction interval is significantly improved. As a result, this study presents a novel twin support vector regression model as a valuable approach for multi-step wind speed interval prediction.
Formation density is one of the most important parameters in formation evaluation. Radioisotope chemical sources are widely used in the conventional gamma-gamma density (GGD) logging. Considering security and environmental risks, there has been a growing interest with the pulsed neutron generators (PNGs) in place of the radioactive chemical source in using bulk-density measurement. However, there is still high requirement of the accuracy of the neutron-gamma density (NGD) calculation. Pair production is one of the factors influencing the accuracy of the results, which should be considered. We propose a method, based on the difference between inelastic gamma-ray response of high- and low-energy windows, to reduce the impact of pair production upon calculating bulk density. A new density estimation algorithm is derived based on the coupled-field theory and gamma-ray attenuation law in the NGD logging. We analyze the NGD measurement accuracy with different mineral type, porosity, and pore fluid and discuss the influence of the borehole environment on the NGD logging. The Monte Carlo simulation results indicate that the improved processing algorithm limits the influence of mineral type, porosity or pore fluid. The NGD measurement accuracy is ±0.025 g/cm3 in shale-free formations, which is close to the GGD measurement (±0.015 g/cm3). The results also show that the borehole environment has a significant impact on NGD measurement. So, it is necessary to take the influence of the borehole parameters into account in the NGD measurement. Combined with the Monte Carlo simulation cases, the application results of the new density estimation algorithm in various model wells are presented.
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