Smarter Natural Resources - Natural Resources Software Technologies


The mission of the Natural Resources Software Technology area is to investigate new conceptual models and technologies to support software-intensive systems in Natural Resources Discovery and Management. The ultimate goal is to devise new software technologies capable of enabling the development and integration of advanced applications, and speeding up the replication of successful solutions in discovery, exploration and production of natural resources.

The discovery and exploration of natural resources are at the heart of business activities conducted by several companies and enterprises across the world. These companies have been facing an increasing pressure from governments and the society in general to change their exploration and production processes, in order to make a more efficient use of the available natural resources, reduce their environmental impact, and improve their overall sustainability.

The natural resource industries have made significant progress towards a more sustainable operation, and Information, Communication and Operation Technologies have played a fundamental role in this development. The pervasive availability of information, sensing and communication technologies has created a new context for NR operations, where environment, assets and people are instrumented with location-aware technology that collects data and provides real-time metrics on performance and status. Remote sensing combined with image and signal-processing techniques allow the real-time acquisition of huge amount of data about the physical world, reflecting the current business reality. Automated, robotic and remotely controlled equipment and transportation are used to improve productivity and safety, while reducing environmental impact. Multiple sites can be managed remotely and centrally, while synergies and advanced capabilities can be realized by unifying processes, information, control and knowledge. Advances in High-Performance Computing, Data Mining, Machine Learning, Optimization, Parallel Programming, among others, allow the development of large-scale physical and statistical models in order to simulate, predict and optimize a variety of properties and behaviors related to natural resources and their exploration and production.

Despite of all this progress, the replication of a successful solution to another context, company or industry is still a very complex and costly procedure, due to issues ranging from lack of interoperability of sensors, actuators and software systems, to differences in business processes and cultural aspects. In addition to this, there are no cost-effective or well-established common approaches to integrate multiple systems addressing different issues of discovery, exploration and production of natural resources, in order to provide more comprehensive views of a NR business and support insight-driven decision making.

The Natural Resources Software Technology area focuses its research activity on devising new software technologies capable of enabling the development and integration of advanced applications, and speeding up the replication of successful solutions in natural resources. To achieve this goal, and considering the challenges that the Natural Resources companies are facing nowadays, a holistic view must be taken, trying to integrate in a seamless way exploration, engineering, production, and distribution activities and their related processes.

At the core of our research agenda is the investigation of new conceptual models and technologies to support the development of a common software platform for Natural Resources industries. Such platform should offer programming abstractions and shared services to speed up the development of new applications, and integration mechanisms to provide interoperability between multiple solutions. In this sense, our current research interests include the investigation of a variety of technologies, such as:

  • Suitable programming abstractions and tools to speed up application development
  • Runtime services to improve flexibility and resilience, including mechanisms for deployment, orchestration, monitoring, data provenance, and access control
  • Multimedia data processing techniques to improve data acquisition
  • HCI and visualization techniques to improve integrated data analysis and interpretation
  • Middleware and semantic-driven technologies to support interoperability and integration of systems
Figure 1: Outline of a software platform for Natural Resources applications.

Figure 1: Outline of a software platform for Natural Resources applications.

Impacts and benefits:

A common software platform, combined with techniques from diverse knowledge areas such as statistics, machine learning, optimization, weather modeling, geosciences, computational mechanics and finance, can be applied to solve a variety of business problems in industries related to natural resources, reducing the time and the overall costs to develop new advanced applications and to replicate successful solutions.

Exemplary Projects:

By way of illustration, we can consider a case for a software platform to support Intelligent Mining. Similarly to other industries, there is an increasing and urgent demand for decision makers in Mining industry to have access to a timely and comprehensive view of the current state of the mining operation. An infrastructure integrating communication, information and operation technologies can allow multiple mining sites to be managed remotely. A lean, centralized management function can achieve improved control of the enterprise. The reduction of redundant management can also reduce costs, while making governance and coordination more streamlined and effective. Leadership and staff can be located in ideal locations independent of mine sites. When appropriate, automated, robotic and remotely controlled equipment and transportation can be used to improve productivity, safety and boost employee retention.

Multiple information systems should be integrated in order to provide comprehensive views of a mine for insight-driven decision making. Reporting and analysis of operations should be real-time and predictive when possible. Production operators, managers and executives should be able to visualize their entire mining operations via intuitive interfaces that provide synoptic and detailed views of performance, including alerts and events. A broad and rigorous set of key performance indicators should be defined and tracked throughout the enterprise. Operational systems should interconnect and communicate data from mines with other key business systems, enabling administration, finance, sales, service and other functions to respond to mining and supply chain events.

To transform this vision of intelligent mine operations into reality, a flexible software platform is needed to enable and speed up the integration of legacy and new systems used in different, yet correlated, activities in mining operation. A software platform for Intelligent Mining should provide an end-to-end solution, from remote sensing to analytics visualization, enabling the development of integrated control centers for mining operation.

Although we illustrated the impacts and benefits of a common software platform with an example in Intelligent Mining, similar cases can be explored in other Natural Resources industries, such as Oil & Gas, Renewable Energy, Agriculture, Water Management, etc.

Another exemplary case is the project we developed with the City of Rio de Janeiro to build an integrated operation center for the city. The key contribution of our research group in this project was the development, adaptation and integration of weather and flooding models that provide high-resolution forecasts (1Km resolution). These forecasts help the City of Rio to manage the operation procedures related to severe weather events, preventing or mitigating the impacts of such events in the city.

Figure 2: Integrating urban weather and flood forecasting models.

Figure 2: Integrating urban weather and flood forecasting models.

Selected Publications:

Rafael Brando, Paulo Frana, Adriano Medeiros, Felipe Portella, Renato Cerqueira. The CAS Project: A general infrastructure for pervasive Capture and Access systems. In Proceedings of 28th ACM Symposium On Applied Computing 2013 [accepted, waiting for publication].

Lloyd Treinish, Anthony Praino, James Cipriani, Ulisses Mello, Kiran Mantripragada, Lucas Villa Real, Paula Sesini, Vaibhav Saxena, Thomas George, Rashmi Mittal. Enabling High-Resolution Forecasting of Severe Weather and Flooding Events in Rio de Janeiro. IBM Journal of Research and Development [accepted, waiting for publication] (2012).

Lloyd Treinish, Anthony Praino, James Cipriani, Ulisses Mello, Kiran Mantripragada, Lucas Villa Real, Paula Sesini, Vaibhav Saxena, Thomas George, Rashmi Mittal. Enabling High-Resolution Hydro-Meteorological Modelling for Operational Short-Term Forecasting in Rio de Janeiro. 3rd WMO/WWRP International Symposiun on Nowcasting and Very Short Range Forecasting (2012).

Lucas Villa Real, Kiran Mantripragada, Paula Sesini, Ulisses Mello, Lloyd Treinish, Anthony Praino, James Cipriani, Vaibhav Saxena, Thomas George, Rashmi Mittal, Frank Liu. Short-Range and Nowcasting-Scale Flood Prediction. 3rd WMO/WWRP International Symposiun on Nowcasting and Very Short Range Forecasting (2012).

Lloyd Treinish, Anthony Praino, James Cipriani, Ulisses Mello, Lucas Villa Real, Paula Sesini, Kiran Mantripragada, Rashmi Mittal, Vaibhav Saxena, Thomas George. Enabling a High-Resolution, Coupled Hydro-Meteorological System for Operational Forecasting of Severe Weather and Flooding Events in Rio de Janeiro. 93rd American Meteorological Society Annual Meeting (2012).

Rashmi Mittal, Vaibhav Saxena, Thomas George, Lloyd Treinish, Anthony Praino, James Cipriani, Lucas Villa Real, Ulisses Mello, L. Dagar, A. G. Naim, H. Hassan, S. A. Husain. A Numerical Weather Prediction-Based Infrastructure for Tropical Meteorology Research and Operations in Brunei. 93rd American Meteorological Society Annual Meeting (2012).

Maximilien. de Bayser, Renato Cerqueira. A System for Runtime Type Introspection in C++. SBLP 2012: 102-116. (2012).

Luiz Marques Afonso, Renato Cerqueira, Clarisse Sieckenius de Souza. Evaluating application programming interfaces as communication artifacts. In Proceedings of the Psychology of Programming Interest Group Annual Conference 2012 (PPIG'2012): 151-162. (2012).

Ulisses Mello, Lucas Villa Real, Kiran Mantripragada, Paula Sesini, Lloyd Treinish, James Cipriani, Anthony Praino, Vaibhav Saxena, Thomas George, Rashmi Mittal. Flood Forecasting in Rio de Janeiro Using Historical Data. 92nd American Meteorological Society Annual Meeting (2011).

Lloyd Treinish, Anthony Praino, James Cipriani, Ulisses Mello, Kiran Mantripragada, Lucas Villa Real, Vaibhav Saxena, Thomas George, Rashmi Mittal. Enabling an Advanced Numerical Weather Prediction Model for Operational Forecasting in Rio de Janeiro. 92nd American Meteorological Society Annual Meeting (2011)