O&G supermajor upgrades subsurface applications to capture data at source
Client Background
Challenge
The O&G supermajor was facing variance in business responsiveness of their subsurface petrotech application. This was partly due to non-standardized business process and ways of working of the subsurface teams across regions, and partly due to the applications being slow. These resulted in longer turnaround time, low stakeholder satisfaction and increased cost of ownership.
Their legacy technology was unable to address the business requirements of cross-functional teams that included geologists, survey and drilling professionals, well delivery teams, etc.
The problem got compounded due to integration of more than 70 regional applications with no visibility into product upgrades and releases. And, associated inter-dependencies on third-party applications further led to outage of systems. Multiple and disparate applications were being used for similar business process.
As a result, the O&G supermajor saw their productivity decline as users were spending considerable time only transferring data across various subsurface applications. It was either very difficult or not possible to produce integrated products by using data that covered various domains – wells, seismic, surfaces. In addition, there were hardly any collaborate tools for the users.
Solution
After examining the complexities and gaps in the O&G supermajor’s existing applications and processes, Wipro proposed Platform-as-a-Service model to help transform their Business Process workflows. The aim was to reduce redundancies as well as standardize practices and application stack.
The solution included:
Business Impact
The Platform-as-a-Service model helped the O&G supermajor reduce costs by 30% over a period of 18 months.
Key benefits delivered:
Unlike conventional computers, artificial intelligence is not programmed. On the one hand, like humans, AI learns from information. Yet on the other, it learns far faster than humanly possible. Machine learning (ML) enables AI to make a correlation between a pattern and an outcome, formulate a hypothesis, take action and then integrate that feedback into its next hypothesis. AI continuously learns, with the goal of predicting future outcomes and events with greater accuracy.
Collaborative Work Centers (CWC) are being widely adopted by upstream oil & gas companies. They play a greater role in helping upstream oil & gas companies realize their goal of optimizing production, reducing cost & improving recovery.
This paper mainly discusses the current business challenges faced by the industry and how an organization adapts to growing technological solutions to overcome the challenges using Centralization of control centers.
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© 2022 Wipro Limited |
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