Construction and application of a platform for on-site detection data perception, analysis, and intelligent decision-making of fluids and materials entering the well

Abstract

With the transformation of unconventional oil and gas resource development towards digitalization and intelligence, massive, multi-source, and dynamic well-entry fluid and material detection data have become core assets driving engineering decision-making. Traditional digital platforms aimed at process onlineization have limitations in deep perception, intelligent analysis, and value mining of data. This paper systematically expounds the next-generation on-site detection intelligent platform built around the core of "global data perception-fusion analysis-intelligent decision-making". The platform achieves high-frequency, automated real-time perception and collection of key fluid performance parameters such as drilling fluid and fracturing fluid through Internet of Things (IoT) and edge computing technology; utilizes a big data technology stack to construct a multidimensional data analysis model that integrates detection data, engineering parameters, and geological information; and develops intelligent decision-making support modules for quality prediction, anomaly warning, and formula optimization based on machine learning algorithms. Application practice shows that the platform not only realizes digital control of detection operations but also shifts quality control from "post-judgment" to "pre-prediction" and "in-process intervention" through data intelligence, significantly enhancing the precise control ability and construction optimization level of well engineering quality. The development direction of the platform is to build a "digital twin" of oil and gas well engineering quality, ultimately forming a smart quality ecosystem featuring autonomous perception, intelligent analysis, and closed-loop optimization.