Hengshuo Zhang

In deep and ultra-deep drilling, high downhole temperatures cause appreciable thermal drift in the strain-gauge bridge of the bit parameter recorder, degrading the measurement fidelity of weight-on-bit (WOB) and torque. This paper presents a temperature-drift compensation scheme combining electric eel foraging optimization (EEFO) with a physics-informed extreme learning machine (PIELM). Four physical priors are formulated as constraints: trend consistency and regression deviation are embedded into the output-weight solution through an augmented linear system, while proportional relationship and continuity enter the fitness function as penalty terms. EEFO performs the hyperparameter search; enhancement strategies addressing its population-diversity decay and limited final-stage accuracy are examined under an equal function-evaluation budget, and the deployed configuration is determined by the ablation evidence. Under leakage-free nested cross-validation, hyperparameter optimization reduces the WOB RMSE from 0.712 kN with fixed hyperparameters to 0.246 kN, a 2.9-fold improvement, while a ridge-regularized ELM baseline reaches 0.708 kN, indicating that the gain originates from the hyperparameter optimization itself. Equal-budget ablation on 13 classical benchmark functions ranks the dynamic-boundary quasi-opposition-based learning variant as the most effective single strategy, and the deployed configuration attains the lowest mean fitness on the target problem. Downhole evaluation is formulated as a consistency-verification framework, with no downhole load ground truth and the WOB voltage lying outside the training range. The apparent drift elimination under in-sample mapping does not survive leakage-free mapping protocols (the WOB elimination rates are 0.3%, 0.0%, and 5.1% under P2–P4) and is therefore not interpreted as a compensation-accuracy figure.

Measurement While Drilling (MWD) tools are widely employed in deep and ultra-deep well drilling. In the high-temperature and high-pressure (HTHP) environments characteristic of these wells, structural deformation induced by thermal expansion interferes with the bit parameter recorder’s sensor readings, thereby degrading the measurement accuracy of weight on bit (WOB) and working torque (WT). To address this issue, this paper proposes a temperature compensation method based on thermo-mechanical coupling simulation. This method systematically establishes the quantitative relationships between multiple loads—including WT, WOB, temperature, and make-up torque—and the strain at critical locations of the bit parameter recorder through finite element analysis (FEA). Furthermore, surface calibration experiments have verified a strong linear correlation between the strain gauge voltage signals and the simulated strain. Building upon this foundation, an inversion-based compensation algorithm is developed. This algorithm effectively isolates the interference caused by thermally induced deformation and inversely deduces the true WOB and torque values by utilizing downhole-measured sensor voltage and temperature data. The research results demonstrate that the proposed temperature compensation method significantly improves the measurement accuracy of the bit parameter recorder under harsh, high-temperature operating conditions. The relative errors for both WOB and torque measurements are controlled to within 5%, providing a reliable solution for precise parameter measurement in high-temperature deep wells.