Sucker-rod pumping systems are the most widely applied artificial lift equipment in the oil and gas industry. Accurate and intelligent working condition recognition of pumping systems imposes major impacts on oilfield production benefits and efficiency. The shape of dynamometer card reflects the working conditions of sucker-rod pumping systems, and different conditions can be indicated by their typical card characteristics. In traditional identification methods, however, features are manually extracted based on specialist experience and domain knowledge. In this paper, an automatic fault diagnosis method is proposed to recognize the working conditions of sucker-rod pumping systems with massive dynamometer card data collected by sensors. Firstly, AlexNet-based transfer learning is adopted to automatically extract representative features from various dynamometer cards. Secondly, with the extracted features, error-correcting output codes model-based SVM is designed to identify the working conditions and improve the fault diagnosis accuracy and efficiency. The proposed AlexNet-SVM algorithm is validated against a real dataset from an oilfield. The results reveal that the proposed method reduces the need for human labor and improves the recognition accuracy.

The increasing pressures faced by manufacturers, the shortening of innovation cycles and the growing importance of high-efficiency manufacturing demand a higher versatility of factory automation. In order to achieve this target, engineering tasks currently performed manually need to be automated. In this case, ontologies emerge as a significant method for representing manufacturing knowledge in a machine-interpretable way. This knowledge can then be used by automated problem-solving methods to reconfigure the control software that coordinates and supervises manufacturing systems. Besides, Ontology can play a very important role in the process of creating as well as managing the knowledge. This paper addresses the important issues in developing domain-specific ontology for manufacturing used in industry 4.0 demonstration production line. A generic ontology is developed considering all the aspects about product from customized order to resulting production.

In petroleum production system, interwell connectivity evaluation is a significant process to understand reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas field. In this paper, a novel long short-term memory (LSTM) neural network based global sensitivity analysis (GSA) method is proposed to analyse injector-producer relationship. LSTM neural network is employed to build up the mapping relationship between production wells and surrounding injection wells using the massive historical injection and production fluctuation data of a synthetic reservoir model. Next, the extended Fourier amplitude sensitivity test (EFAST) based GSA approach is utilized to evaluate interwell connectivity on the basis of the generated LSTM model. Finally, the presented LSTM based EFAST sensitivity analysis method is applied to a benchmark test and a synthetic reservoir model. Experimental results show that the proposed technique is an efficient method for estimating interwell connectivity.

This paper presents an approach to enabling ontology-based web service integration for flexible manufacturing systems based on industry 4.0 demonstration production line. The mass personalized customization requires increased agility and flexibility in manufacturing systems to adapt incessant changes in manufacturing environments and requirements. The fundamental for this method lies in the incorporation of knowledge expressed by ontologies concerning order, products, industrial equipment, manufacturing process, event and service. It is a significant method to use ontology for representing manufacturing knowledge in a computer-interpretable way. This knowledge model can be used by automated decision-making to configure the control component that monitors and harmonizes the whole manufacturing system. The semantic model of the flexible manufacturing systems can be automatically updated based on event notifications sent by the specialized web services. The architecture and knowledge model have been demonstrated in a pilot case named industry 4.0 demonstration production line and can be applied to other flexible manufacturing systems.

In cyber-physical 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 (O&G) 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.

Subsurface flow is the core of reservoir engineering. Research on subsurface flow problems can enhance our understanding of the development status of oilfields, thus enabling the prediction of the distribution of residual oil and the formulation of production plans. Machine learning-based methods have been widely studied in developing data-driven models to solve subsurface flow problem. However, a large amount of labeled data are required to build highly accurate models. It is applicable to embed domain-knowledge into data-driven machine learning methods to reduce data requirements. In this study, we propose a residual-enhanced physics-guided machine learning method with hard constraints (RHC-PGML) to predict reservoir pressure. Specifically, a physics-guided machine learning with hard constraints strategy is proposed, which makes the prediction results strictly satisfy the prior domain knowledge to improve the prediction accuracy of the model. In addition, we integrate residual learning in RHC-PGML model to compensate for systematic errors caused by the inability of the embedded physical mechanism to perfectly describe the complex seepage process and further improve the prediction accuracy. The proposed method is verified by a seepage problem in a heterogeneous reservoir model. The results show that the RHC-PGML method can obtain reliable prediction results in the case of sparse and limited data.