The advancement of key aspects of bridge information modeling for software interoperability and data exchange has attracted increasing interest from various stakeholders in the bridge industry. Potential benefits include reductions in project delivery time, errors, and cost. However, a lack of standardized data exchange protocols hinders the development and deployment of such modeling. This situation causes the still-all-too-fragmented bridge industry to waste a large amount of money each year. The research presented in this paper was motivated by the advantages of using interoperability to streamline the approaches used to deliver concrete bridge projects. Every bridge project requires frequent communication; without improved software interoperability, projects can become bogged down with requests for information. This paper develops, demonstrates, and implements key aspects of interoperable data schema protocols that can be used to facilitate the exchange of bridge information in the planning, design, detailing, fabrication, and construction phases of concrete bridge projects. These protocols will be public domain and software neutral with their implementation being considered in various bridge software applications.
Deepwater oil and gas facilities typically encounter on an average up to 5% annual production losses due to unplanned downtime, conservatively estimated at billions of dollars impact for the industry. The existing toolkit and systems in place are not always adequate to identify and predict abnormal events that could lead towards unplanned facility shutdown. The interaction amongst process sub-systems and disturbances that propagate across these sub-systems with changing operating conditions are hard to predict without a fit-for-purpose model (or a digital twin). The focus of current work is on deepwater facility having several oil export pipeline pumps in parallel and several gas compressors in series. The alarm database showed records of several unplanned shutdown events around these critical equipements that resulted in undesirable outcomes such as production deferment, complete facility shutdown, loss of sales volumes and increased operational costs. In this work, an intelligent prognostic solution is proposed using machine learning (ML) framework for automatic prediction of impending facility downtime, and identification of key causative process variables. A systematic workflow was developed to identify, cleanse and process real time data for both model training and prediction. Several ML methods were evaluated; anomaly detection based on Principal Component Analysis (PCA) and Autoencoder (AE) algorithms were found performing better for the type of data available for the deepwater facility. The ML framework also supported analysis of underlying downtime causes to propose suitable mitigation steps. Knowledge based on physical understanding of the process was used to select each sub-system boundary and sensor list on which ML model was trained. These models were then cross-validated to test the accuracy of trained models. Finally, the alarm database was used to confirm the accuracy of the machine leaning models and identify root-causes for unplanned shutdowns. If the operating condition changes over time, the anomaly detection based ML models were setup to adapt to changing conditions by automatic model updates, resulting in significant reduction in false alarms. The adaptive ML models, when applied to one of the sub-system (with 30 different sensor data), predicted 24 unplanned events in 6 months of period, while when applied to another sub-system (with 40 sensor data), predicted only 6 unplanned downtime events. Several predictions were found as early as 30 mins to 2 hours, providing adequate early warning to take proactive actions. Case studies shown in the paper present diagnostic charts and identified early indicators were found in agreement with pre-alarms generated by existing alarm system, thus validating the ML solution. Current toolkit available to identify anomalous process behavior is limited to exception based surveillance with fixed min-max limits on each sensor data. Therefore, proposed adaptive ML solution has shown potential to revolutionize the topside process surveillance. This paper also describes how the ML framework can be scaled for a sustainable solution that provides prediction every minute, keeps the model evergreen utilizing cloud-based model deployment platform to train, predict and trigger automatic model updates and also span multiple process systems and facilities. Finally, we present directions for future work, where the current model can keep predicting various events and over time when sufficient events are collected, more advanced machine learning methods based on supervised ML can be developed and deployed.
The benefits of using building information modeling to integrate the project delivery process by exchange of project data between industry stakeholders stimulate the bridge industry to develop bridge information modeling. However, the existing data exchange methods, formats, and standards that are used by the building industry cannot be directly borrowed and used “as is” for bridge projects. Bridge projects involve specific definitions of structural geometry (e.g., roadway alignment–driven bridge layout) or definitions of bridge member geometry that are not usually used in building projects (e.g., camber data for steel plate girders). These bridge-oriented geometries cannot be described by using current building-oriented data exchange formats. Because bridge geometry depends on the geometry of the roadway carried by the bridge, it is beneficial to define a bridge as a parametric model that can be manipulated by modifying a manageably small number of independent parameters (e.g., station and skew). However, the existing data exchange standards do not support the exchange of parametric geometry. To overcome these technical limitations, this paper presents a software-neutral schema for the exchange of alignment-driven bridge-oriented parametric geometry data using eXtensible Markup Language. The application of this formalism is then illustrated via three high-priority use cases that occur during the steel bridge life cycle.
The industry remains focused on achieving efficiency gains through accessing and processing production, asset and original equipment manufacturer (OEM) data, and applying machine learning (ML) principles to arrive at improved outcomes. As a service provider, we are experiencing an increased activity level related to hybrid analytics which involves embedding high-fidelity physics-based models together with ML models to improve outcomes. Critically, rather than addressing one piece of equipment, or a subset of a facility, companies focus on economies of scale, and seek asset-wide understanding of implications to equipment condition when changing operating parameters. Deployment of a Dynamic Digital Twin provides production and maintenance engineers with a ‘single source of truth’ for information (i.e. P&ID, PFD, OEM information, maintenance history, etc.) integrated with high-fidelity physics-based models for subsea and topsides processes. Field sensors measuring hydrocarbon quantity, quality and other physical properties are integrated to provide real-time and historic data. Physics-based dynamic process models are first calibrated to match the field sensor data and then used to generate synthetic data for training ML models. A high-fidelity model generates virtual measurements where field sensors are not available. Access to such high-quality virtual measurements presents a paradigm shift for upstream analytics, as ML algorithms now have access to larger datasets for training. This improves quality, allowing for proactive planning and improved uptime leading to increased facility uptime by predicting equipment failure and enabling condition-based maintenance (CBM). In our work with major oil and gas operators, we have observed that maintenance engineers until now have struggled, because enough field sensors are not always available to support the ML algorithms, leading to less specific assumptions and lower quality results. By taking advantage of a Dynamic Digital Twin - containing the asset structure, visualization and models - hybrid analytics were applied to continuously improve predictions, thereby increasing facility uptime. In this paper, we present a few case studies of applying hybrid analytics with some oil and gas operators to enable virtual flow metering, prediction of unplanned equipment shutdown and prediction of optimum operating parameters for increased facility uptime. Examples presented demonstrate the integration of historic and real-time measurements with the physics-based process and multiphase flow models, and ML algorithms such as Autoencoder (AE), Long Short-term Memory (LSTM) neural networks and Reinforcement Learning.
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