A Dual-Output Soft Sensing Strategy for Hydrogen Sulfide in Oilfield Production Based on PSO-Optimized Extreme Learning Machine

Gas-phase sensors suffer from non-linear drift and range-precision conflicts, while the detection of dissolved H2S in the oil phase relies on discontinuous, offline chemical analysis. To address these challenges, this study proposes a dual-output soft sensing model based on a Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM). Unlike conventional single-output PSO-ELM applications, the proposed framework jointly performs gas-phase sensor drift correction and oil-phase dissolved H2S estimation within a unified soft-sensing structure. By integrating gas sensor array signals with oil-phase process parameters, the model utilizes PSO to globally optimize the input weights and biases of the ELM, effectively overcoming the local minima and overfitting issues inherent in traditional neural networks. Field sampling results showed that the proposed model achieved high predictive accuracy, with coefficients of determination of 0.9949 for gas sensor drift correction and 0.9967 for oil-phase soft sensing. Comparative analysis reveals that the PSO-ELM significantly outperforms Standard ELM, RBF-ELM, and GA-ELM, reducing the Mean Squared Error by approximately 39.7% compared to GA-ELM. Furthermore, 5-fold cross-validation confirms the model’s robustness (R2 average of 0.9810), indicating its potential for real-time hydrogen sulfide monitoring in complex oilfield production environments.