ANN based Interwell Connectivity Analysis in CyberPhysical Petroleum Systems In cyberphysical petroleum systems (CPPS), accurate estimation of interwell connectivity is an important process to know reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas (Ou0026G) field. In this study, an artificial neural network (ANN) based analysis method is proposed to estimate interwell connectivity. The generated neural network is used to define the mapping function between production wells and surrounding injection wells based on the historical water injection and liquid production data. Finally, the proposed method is applied to a synthetic reservoir model. Experimental results show that ANN based approach is an efficient method for analyzing interwell connectivity.

Designing and Developing HighConfidence Petroleum Extraction CyberPhysical System Using the ModelDriven Architecture In modern petroleum extraction industry, Internet of Things (IoT) technology is widely used, and various of sensors constitute a typical cyberphysical systems (CPS). There are more requirements of interoperability and interaction between well site device systems. In such industrial critical domain, highconfidence is also important. This paper presents the requirements and running environment of developing highconfidence CPS. To satisfy the requirements of interoperability and data exchange among heterogeneous well site systems, a foundation framework with well defined interfaces and quality of service constraints was established. Based on this framework, designing highconfidence petroleum extraction CPS using ModelDriven Architecture approach is proposed, the longlived models produced in the process that can be applied to any implementation technologies and platforms through model transformation which make the systems more portable. A case study shows the detail of the development process and using communication bridges to build relationships between models. At last some important problems which need to be paid more attention in model transformation process are discussed.