Digital Transformation in Oil and Gas

The traditional Oil and Gas Industry is being transformed by a new wave of digital disruptive technologies. Developments in technologies such as the cloud, social media, sensors, mobility, artificial intelligence, virtual reality, big data and analytics are driving trends that have immense potential for Digital Transformation.

The essence of digitization lies in re-platforming core business processes and bringing together transactions and analytics in real time to be smarter, faster, and simpler; achieving supplier collaboration to accelerate growth innovation, harness assets and the Internet of Things (IoT) to drive real-time insights and new business models which will lead to lower costs, greater efficiency, and deeper customer relationships.

This article explores several of these technologies, and provides an overview of how they have been applied in combination with the development of new or improved business processes.

Technology Innovation

In regards to technology Innovations, lot of avenues can be explored and improved to optimize operational costs and transition from CAPEX to OPEX. Some of the areas include legacy modernization, cloud adoption, adoption of agile methodologies, optimization of supply chain logistics and asset management. To emerge leaner and more agile, outsourcing specific technology or business process functions is a proven way of reducing both capital and operating costs. This enables the company to transfer the need to make significant capital expenditure, such as large investments in technology infrastructure, to its vendor at much lower costs. 

To remain profitable, oil and gas companies must also consider how to gain operational efficiencies through improved use of technology like cloud adoption where possible, adoption of agile methodologies. Innovative customer engagement models offer flexibility and a personalized experience, opening up new revenue opportunities for Oil and Gas operators, and new services for customers.

IoT-enabled Digital Operations

Industrial IoT has built a bridge between operational technology (OT) and information technology (IT), which enables unstructured, machine-generated data to be analyzed for insights that drive improvements in design and execution, and lead to smarter, faster decision-making and machine-to-machine communications.

Deployment of IoT-based smart energy solutions results in better field communication, reduced cost of maintenance, real-time monitoring, mine automation, and greater safety and security of assets. This leads to higher productivity. Some of the key challenges in oil and gas operations include managing uptime of oil and gas installations. Uptime is critical at oil and gas installations, where the cost of developing a well is $10 per second ($864,000 per day), shutdowns can cost up to $5 million per day and nonproductive time (NPT) costs can exceed $500 million a year. With capacities that barely outstrip demand, delays that extend shutdowns and reduce uptime are unacceptable.

Another challenge is Digital Asset Life Cycle Management. New digital technologies combined with data-driven insights can transform operations, boosting agility and strategic decision-making, and resulting in new business models. Monitoring of an asset’s status in real time and predictive forecasting enabled by analytics and robotics results in lower repair and maintenance costs and lower assets downtime. Predictive maintenance, pipeline and equipment monitoring, location intelligence, emissions monitoring and control and release management, real-time machine and sensor integration, real-time alerts, link to enterprise resource planning data to trigger maintenance workflow, plant dashboards and trend analysis and limiting data leakages can improve operational efficiency, reliability, safety and reduce maintenance costs. Parameters like pressure, volume, and temperature can be collected and analyzed together and compared with the past history of equipment failure, advanced analytics can be applied to predict potential failures.

Sample Scenario: Predictive Maintenance, Analysis and Troubleshooting for Machinery

An example scenario will demonstrate some of these concepts of how cloud and IoT technology can drive transformation, and we have chosen predictive maintenance, analysis and troubleshooting for pipes and machine parts of oil rigs and related machinery. This system collects data from IoT sensors installed into pipes and machine parts on oil rigs and related machinery, using in this example, Azure IoT Suite. This allows engineers and maintenance staff to see any potential problems in the oil and gas supply chain and fix them before they escalate into full equipment failures.

The connectivity also enables remote troubleshooting in real time as the depth of the information from sensors fed back into the system allows companies to see exactly where a problem is, or will occur and thus enable predictive maintenance. Microsoft Azure IoT Suite is an enterprise-grade solution that enables to get started quickly through a set of extensible preconfigured solutions that address common IoT scenarios, such as remote monitoring and predictive maintenance. Leveraging a cloud vendor end-to-end IoT-based solution helps to connect, monitor and manage devices on a massive scale, and enables horizontal scaling for all technology layers, handling new device types and business applications, and can handle growth in device base and activity.

Two important aspects that need to be taken when architecting an IoT solution are scalability and security. The Azure IoT Hub solution provides the required scalability to support unpredictable traffic surge while ensuring security at the device level to ensure it is “hack-proof”. An IoT Hub provides the reliability to secure the connection between device and cloud and vice-versa, but scalability has to be implemented at the architecture level. The platform also leverages Microsoft's visual analytics Power BI tool at its core. This provides an easy to use User Interface (UI) with internationalization in compliance to customer branding guidelines, and can be developed very quickly to provide meaningful insights.

IoT Reference (courtesy of MS Azure Remote Monitoring Architecture)

After an IoT device registers with the cloud gateway, it can send and receive the data to and from the hubs. There will be huge data that needs to be managed with multiple messages being received per second from huge number of devices, which would result in tens of thousands to possibly millions of messages a day. The Azure IoT Suite platform provides high-volume message ingestion using a single logical endpoint.  Once the messages arrive, the platform provides a mechanism to select, transform, and route messages to various storage mediums for the purpose of archival and staging for downstream processing. The Event Hub is queried by a Web Job running an event processor host, and this determines where an alarm or alert needs to be pushed to. Logic Apps are used to create more complex workloads to backend systems. Document DB stores all device registry information. Output of stream analytics is commonly a permanent storage location in Blob storage, database tables or a “data lake”. Stream Analytics creates and manages jobs to recognize threshold values or detect alarm triggers, and bespoke web applications can be used as the monitoring dashboard.

Artificial intelligence can be leveraged to help diagnose issues, to issue press releases on outages, to provide personalized usage reports, etc. By replicating certain tasks generally handled by key operations staff, we can speed up the identification of issues, and deliver actions and alerts to the field. Real-time analysis and communication improves uptime, decreases risk, and improves customer satisfaction. The gap of time between an issue occurring, analysis of that issue by a qualified engineer, and completion of a work order to sort the issue will be cut down from hours to minutes. It enables rapid and accurate decisions helps in improving productivity.

How AR/VR can be applied in Oil and Gas to improve productivity and safety

Challenges to meet training requirements are compounded at offshore oil and gas rigs accessible only by helicopter or boat, which drives up transportation costs for personnel and equipment helps in improved training. A growing number of companies in the energy industry are leveraging 3D virtual planning, simulation and visualization technologies to plan and schedule operational procedures, train workers and meet health and safety requirements. This is achieved by interacting with a computer simulated 3D environment, including cranes, plant assets and workers to determine the best process to minimize costly project delays and mitigate project execution risk. Video collaboration can be leveraged by connecting field workers with more experienced colleagues who can see what the field worker is seeing to improve collaboration. This in turn also improves the workforce safety since the field workers have guidance and mentorship not restricted by time, distance or location.

Predictive Analytics and machine Learning in Oil and Gas

Predictive analytics, machine learning and robotics can improve capital project execution, installation and decommissioning, which will ensure smarter asset planning. Exploration efforts can be enhanced using historical drilling and production data from nearby to help geologists and geophysicists verify their assumptions in their analysis of a field where environmental regulations restrict new surveys. Analytics applied to geospatial data, news feeds, oil and gas reports, or other syndicated feeds to provide competitive intelligence on where to submit bids for leases help in Acreage assessment and prospect generation. Predicting drilling success, beyond monitoring and alerting based on limited data, apply to real-time "big" drilling data to identify anomalies based on multiple conditions or predict the likelihood of drilling success help in Drilling and Completions.

Performance forecasting is also vitally important. Aging wells where the forecast does not meet a predetermined production threshold are flagged for immediate remediation help in Production and Operations. Analytics applied to a variety of Big Data — seismic, drilling, and production data — could help reservoir engineers map changes in the reservoir over time and provide decision support to production engineers for making changes in lifting methods help in Enhanced oil recovery

An Approach to Digital Transformation

The digital transformation process goes beyond embracing specific digital capabilities. An organization needs to have a comprehensive outlook while defining the roadmap towards digital transformation. The recommended transformation approach would typically consist of the below phases:

Organizational change management is a very critical aspect when adopting digital transformation. A strong vision is required to articulate compelling need for embarking on a single platform company-wide, and an awareness and buy-in among stakeholders into one consistent vision is a prerequisite to moving the organization towards common processes. A solid change strategy needs to be defined that drives an organizational transition from the current state to the future state throughout an agreed program with clear KPIs, and there should be agreement and commitment to the change among business leadership that demonstrates active and visible support that includes program leaders and key company decision-makers.

There should also be active stakeholder engagement throughout the process, which includes the building of commitment and advocacy as well as ownership of the change among managers and employees throughout the organization and the alignment of organizational structures and functions with associate roles, responsibilities and accountabilities for program assimilation will ensure successful adoption of the change. The organization should be ready to support the new skills and competencies and culture shift. The deployment of performance criteria aligned to the ‘to-be’ environment will help in measuring in the overall successful adoption of the change.

Acknowledgements

Venkata Guru: We wish to gratefully acknowledge Venkata Guru Prasad Kandarpi, General Manager, for painstakingly going through the paper and giving us the right guidance to refine the content.

About the Authors

Rekha Kodali is an Enterprise Architect with more than 20 years of experience in Microsoft Technologies. Her focus areas include .Net, ASP.NET, Azure, Micro services, API Management, BizTalk Server, Web Services, SharePoint, Office 365, SQL Server, ESB and SOA.

Md Tahir is a Consultant. He works on Integrated Deals providing solutions in the Business Application Services domain. He has a prior IT experience of 3 years.