With the continuous upgrading of environmentally friendly production technologies, accurate monitoring of suspended particulates has become a pivotal step toward improving geological and ecological environmental quality. A real⁃time detection paradigm for micro⁃particles in petroleum produced water is proposed.Within the YOLOv8 framework, GSConv is lightweightly coupled with Contextual Anchor Attention (CAA) to form a GSConv⁃CAA joint module. First, an improved YOLOv8 localizes particles and crops the corresponding regions of interest. Next, a frequency⁃domain⁃gradient hybrid blur metric is designed to automatically discard low⁃quality sub⁃images. Finally, a morphological algorithm is employed to rapidly fit the equivalent diameters of the particles. To maintain representational capacity while reducing computational burden, we replace the standard convolutions in the CBS modules with GSConv and embed a CAA module into the detection head, explicitly modeling long⁃range pixel⁃wise dependencies and further enhancing central⁃region features. Experiments on a self⁃built oilfield water sample dataset show that the proposed method achieves a precision of 81.6%, AP50 (average precision calculated at an intersection over union of 0.5) of 82.2%, and an F1 score of 77.6%, with an inference speed of 214 FPS. This provides a feasible solution for real⁃time, high⁃precision monitoring of suspended particles in petroleum produced water.