By: Akanksha Kumari Sinha, Consultant Oil & Gas

The O&G industry is particularly information-intensive, and managing large volumes of data collected at multiple points across an operation or organization is a big challenge to solve. Traditional modeling workflows are typically time-consuming and require well-organized cross-disciplinary integration between geoscientists. However, increasing complexities, and rising cost structure of field development, demand improved and modern workflows for reservoir evaluation.

Real-time data integration is the key to making timely business decisions and driving increased business efficiency throughout the field development lifecycle. It standardizes the overall process, synchronize the data to provide usable, accessible and right information to the right people at the right time. Most oil companies accepted it and have focused on the next-generation oil field and Internet-enabled interfaces to create a real-time, remotely operated digital oilfield environment.

Components of real-time data integration

Decreasing the cycle time between crucial decision points requires people, processes, and technology to work together. For a Digital Oil Field (DOF) project to be successful, a real-time field data infrastructure must be based on four key elements:

1. Instruments and actuators installed appropriately in wells and surface facilities
2. Controllers for gathering all data, perform control, and report accordingly
3. Modern telecom and IT infrastructure to transport and deliver all field data to upper control levels, and also to get control set-points back to the field actuators
4. A real-time, open, and reliable integration middleware to seamlessly transfer all data to the collaborative levels of the DOF architecture using standard industry protocols

Why real-time integration trumps the conventional approach

Efficient operation system: Working as an integrated multi-disciplinary approach, real-time production-operation systems provide the maximum production at lower costs and also, reduce time, risk, and environmental impact. It gives a better understanding of the current scenario of O&G fields and validates online well-production prediction, as well as the integration with a reliable system to identify, quantify, and classify production losses. The consolidation of the whole asset-production data into a single system provides mechanisms to monitor, analyze, and control O&G fields.

Automatic data Validation: Data is automatically uploaded to the system to minimize manual efforts and improve uptime. To avoid spurious data, there is an automatic validation mechanism for the data acquisition process. In addition to production monitoring and the enhanced data quality, historical data, alarms, and statistic tools are used to improve the data analysis and interpretation process.

Application in O&G operations

Real-time operations can be a physical or virtual environment in which E&P companies can adopt new real-time approaches to their drilling, completions, and other operational workflows. Its ability to remotely monitor operations avoids the need for experts or other non-essential people on location. This reduces the exposure risks associated with travel or rig site presence for many. The well is essentially under the discernible watch of several experts at critical points from a remote location, helping to ensure the delivery of a safe and efficient well. New technologies allow integrated reservoir study teams to assess vast amounts of information, including drilling performance, to decide ‘at the last minute’ about the well path and completion architecture.

Experience shows that an enhanced and defined decision-making process reduces non-productive rig time and a streamlined combination of reservoir characterization, drilling, and production operations lead to improved reservoir management plans.

Going forward, the integration of emerging technologies will create new ways to gather and interact with data, provide new perspectives on O&G assets, and ultimately, give companies the ability to lower costs and optimize the performance of their production assets.