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The complexity and strong heterogeneity of carbonate reservoirs with fracture-vuggy structures present significant challenges in reservoir characterization. To address these challenges, we propose a novel multi-element information fusion modeling approach. This approach is designed to integrate multiple methods and incorporate multi-probability fusion at various facies and scales, thereby bridging the gap between geological information and reservoir modeling. Our methodology involves four key steps. First, the statistics between frequency of karst and geological information are acquired, and we quantify the statistics to regression equations. Second, these regression equations are transferred to probability bodies. The probability bodies can be applied in modeling as a soft control. But just one single body can be input in modeling process. Third, multiple probability bodies are fused into a fusion probability body by a probability fusion algorithm, which can keep the potential information of probability bodies. Finally, we apply the probability body in modeling workflow. By this way, the fusion method bridges the gap between geological information and modeling. The model established through our proposed method showed a significant level of consistency with reservoir re-evaluation, achieving an impressive 90% degree of alignment. Furthermore, the history match analysis revealed a high correlation, indicating the model's reliability. The method effectively integrates various scales and types of geological information, offering an accurate approach to complex carbonate reservoir modeling.

The sliding mode observer (SMO)–based sensorless control is essentially a discontinuous switching control algorithm. Therefore, there is large speed or torque chattering in the permanent magnet synchronous motor (PMSM) with existing sliding mode observer–based control methods. In order to solve this problem well, an algorithm based on an improved sliding mode observer is proposed. In detail, the piecewise power function approach is used to replace the traditional ones, that is, the sgn function or the sigmoid function approach. Simulation verifications are conducted to validate the effectiveness of the proposed observer. It shows that the piecewise power function–based sliding mode observer algorithm has obvious advantages in the stability of the three-phase current, namely, it improves the estimation accuracy of the rotor position, rotor speed, and dynamic response to various loads. For further verification, the proposed sliding mode observer algorithm is implemented and verified in a marine electric propulsion motor with a DSP chip of TMS320F28377D. Experimental results validate that the piecewise power function–based sliding mode observer algorithm has high control accuracy and fewer fluctuations, which are all vital in marine engineering.