Search

Autonomous Underwater Vehicles (AUVs) are widely used for the inspection of seabed pipelines. To address the issues of low trajectory tracking accuracy in AUV inspection processes due to uncertain ocean current disturbances, this paper designs a new dual-loop controller based on Model Predictive Control (MPC) and Variable Universe S-plane algorithms (S-VUD FLC, where VUD represents Variable Universe Discourse and FLC represents Fuzzy Logic Control) to achieve three-dimensional (3-D) trajectory tracking of an over-actuated AUV under uncertain ocean current disturbances. This paper uses MPC as the outer-loop position controller and S-VUD FLC as the inner-loop speed controller. The outer-loop controller generates desired speed instructions that are passed to the inner-loop speed controller, while the inner-loop speed controller generates control input and uses a direct logic thrust distribution method that approaches optimal energy consumption to distribute the thrust generated by the propellers to the over-actuated AUV, achieving closed-loop tracking of the entire trajectory. When designing the outer-loop MPC controller, the actual control input constraints of the system are considered, and control increments are introduced to reduce control model errors and the impact of uncertain external disturbances on the actual AUV model parameters. When designing the inner-loop S-VUD FLC, the strong robustness of the variable universe fuzzy controller and the easy construction characteristics of the S-plane algorithm are combined, and integral action is introduced to improve the system’s tracking accuracy. The stability of the outer loop controller is proven by the Lyapunov method, and the stability of the inner loop controller is verified by simulation. Finally, simulations show that the over-actuated AUV has fast tracking processes and high tracking result accuracy under uncertain ocean current disturbances, demonstrating the effectiveness of the designed dual-loop controller.

Based on the well logging knowledge graph of hydrocarbon-bearing formation (HBF), a Knowledge-Powered Neural Network Formation Evaluation model (KPNFE) has been proposed. It has the following functions: (1) extracting characteristic parameters describing HBF in multiple dimensions and multiple scales; (2) showing the characteristic parameter-related entities, relationships, and attributes as vectors via graph embedding technique; (3) intelligently identifying HBF; (4) seamlessly integrating expertise into the intelligent computing to establish the assessment system and ranking algorithm for potential pay recommendation. Taking 547 wells encountered the low porosity and low permeability Chang 6 Member of Triassic in the Jiyuan Block of Ordos Basin, NW China as objects, 80% of the wells were randomly selected as the training dataset and the remainder as the validation dataset. The KPNFE prediction results on the validation dataset had a coincidence rate of 94.43% with the expert interpretation results and a coincidence rate of 84.38% for all the oil testing layers, which is 13 percentage points higher in accuracy and over 100 times faster than the primary conventional interpretation. In addition, a number of potential pays likely to produce industrial oil were recommended. The KPNFE model effectively inherits, carries forward and improves the expert knowledge, nicely solving the robustness problem in HBF identification. The KPNFE, with good interpretability and high accuracy of computation results, is a powerful technical means for efficient and high-quality well logging re-evaluation of old wells in mature oilfields.

Based on analysis of NMR T2 spectral characteristics, a new method for identifying fluid properties by decomposing T2 spectrum through signal analysis has been proposed. Because T2 spectrum satisfies lognormal distribution on transverse relaxation time axis, the T2 spectrum can be decomposed into 2 to 5 independent component spectra by fitting the T2 spectrum with Gauss functions. By analyzing the free relaxation response characteristics of crude oil and formation water, the dynamic response characteristics of the core mutual drive between oil and water, the petrophysical significance of each component spectrum is clarified. T2 spectrum can be decomposed into clay bound water component spectrum, capillary bound fluid component spectrum, micropores fluid component spectrum and macropores fluid component spectrum. According to the nature of crude oil in the target area, the distribution range of T2 component spectral peaks of oil-bearing reservoir is 165–500 ms on T2 time axis. This range can be used to accurately identify fluid properties. This method has high adaptability in identifying complex oil and water layers in low porosity and permeability reservoirs.