Digital Transformation in Oil & Gas Sector...miles to go

The tide is turning for traditional onshore and offshore energy industries. New digital ecosystems are pumping new life into tired operational models, enabling exciting new possibilities to improve productivity, safety, security, and environmental impact. It’s time for action. While going digital has mostly become the norm now, it perhaps makes more sense for this sector to seize the opportunity and scale up the impact, especially in today’s lower-for-longer environment that requires new operating and capital cost models. And with a comprehensive road map in hand, the journey may not be so cumbersome after all. Top executives of this sector know this very well that early bird gets the worm, but they would like to be second mouse that gets the cheese. This is because it’s more costly to be the first to adopt new innovations. So now the trickiest question is how can establish Oil & Gas companies tap into new transformational ecosystems?

Digital technologies have shown their potential in this sector. Technological advancements are not new to them. They have been using supercomputers to model reservoirs, pipeline and distribution automation, robotics for drilling operations, and 3-D imaging to find seismic disturbances. The primary hurdle in the digital transformation of this sector is not the technology but organizational deficiencies. They are sceptical about the financial returns on digital investments. They are hesitant to invest in big digital projects like for example investing in AI, ML, Big Data Analytics and Cloud based platforms to interpret and understand seismic data & reservoir models. This would improve the accuracy of pre-drill estimates and thus give a better chance and understanding the nitty-gritty of the entire project before starting.

The Oil & Gas industry is massive, and are split up into three groups: Upstream, Downstream, and Midstream.

Upstream: This is where the work begins. In this group O&G industry is involved in all the steps till oil extraction. All the firms in this stream are standing at a crucial time in digital transformation history. They require greater capital efficiency to achieve their goals. Digitalization becomes pertinent as it will drive operational improvements, which will lead to better results with same capital influx. Exploration part of this stream has been a leader in data and digitization. For years producers of the exploration & production section software platform have been driving data & workflow integration. However still there is huge scope to improve the data integration, interpretation & processing. There is no denying that the wave of digital transformation has already hit this stream, some are ahead while others are still evaluating a cost-effective and efficient way to expand their digital portfolio.

Midstream: The operations in this stream link the Upstream to Downstream section. This stream works on the transportation & storage of the produced resources from the wells to the refineries. Framing a digital strategy for this stream majorly requires enhancements in transportation capacity and transportation infrastructure safety. Technological advancement in this stream will help considerable operational gains. Estimates show that effective and efficient project execution through digital transformation can decrease the cost by over 20%. Technologies such as AI & IOT powered pipeline management systems etc can play significant role in this stream.

Downstream: The end products are processed and sold to the end customers in this stream. Research shows that O&G sector can get lots of benefits in-case digital transformation is rolled out in this stream. i.e., Reduction in the operating cost by 12 % to 20 %, Reduction in unplanned shut down by 15 % to 25 %, Improvement in refinery plants efficiency by 8 % to 12 %, Improvement in the workplace health, safety, security, and environment, Improved workforce efficiency etc. To capture these benefits, the downstream companies need to understand the "why" and the "how" of the digital transformation framework. Digitalization in the downstream Oil and Gas industry is less mature than in other sectors. Improving business operations is necessary to successfully implement a digital transformation framework in the downstream O&G sector.

Companies in this sector need to take a holistic approach to digital transformation. Their strategy must be such that it integrates all the operations in the industry from production sites and transportation to refineries for the end-product, every aspect of the industry must be considered to transform the industry's digital outlook. In some of the recent surveys & research done by leading companies found that the top executives in this sector have opted for technologies that combine data and analysis to enhance operational efficiency. Below are the top five technologies they believe can create a diversified yet correct digital portfolio for their sector.

- Manufacturing Execution System

- Cloud Computing

- Internet of Things

- Machine Learning

- Energy Analytics

 

Today this sectors efficiency and sustainability challenges can be tackled by correctly implementing the above technologies. Thus, building the right digital portfolio is by understanding the true need of the company. By instilling a culture of innovation, breaking down data silos, developing internal resources and leveraging partnerships, companies in  this sector can better position themselves for success through digital transformation.

 

 

*******************
***************
*******

 

Comments

Post a Comment

Popular posts from this blog

Generative AI & Meta-Leaders

In the wake of advances in generative AI and large language models (LLMs), there is excitement about what the technology means for businesses. But we should not forget that this advancement is also posing a catch-22 situation to the traditional leadership. The transformative potential and rapid acceleration of generative AI are creating an imperative for them to act quickly. They should rethink their skillsets and develop new skillsets like – ability to make better sense of data insights, ability to ask better questions, ability to learn from smart mistakes etc. It is true that ChatGPT has taken the world by storm. In just 5 days since its launch in November last year, it gained one million users, a feat that no other popular online services have managed to achieve. But it is not the only generative AI tool, others include Midjourney and Dall-E for images generation, GitHub Copilot for coding, and even conditional generative adversarial networks (cGAN) such as Pix2Pix, developed by
Read more

Good automation! Select right process for RPA

Someone said very rightly – “Automation is good, so long as you know exactly where to put the machine.” In all my previous blogs I wrote about BOT failure, ROI Modelling, Cost saving, RPA Governance, CoE, POC, POV etc. But then I realized that before taking a deep dive into any of these subjects, first we need to have a right RPA process identification & prioritization methodology in place. Everybody across the industry agrees that Robotic process automation (RPA) can make a big difference for organizations tormented by repetitive routine processes – the kind that suck up the productivity of people whose time would be better put toward more important work. But many are confused by what seems to be a deceivingly simple question — which are the right processes for RPA? The poor choice of process for initial pilot is the leading root cause for failure to meet customers’ expectations. Robotic Process Automation (RPA) doesn’t suit every process and in majority of cases we don’t have
Read more

RPA Exception Handling – Be in control

Companies in every major industry are turning to semiautonomous computers (better known as BOTs) to automate large- and small-scale business processes. This technology replaces human intervention in back-office operations, improving operational efficiency, reducing costs and increasing margins. However, organizations cannot employ this technology effectively unless the BOTs are engineered properly. And one of the most important component of this BOT engineering is Exception handling or in simple words right categorization of the errors and then developing solutions around the same to deal with those errors to meet the expectations of the RPA owner. Occasionally, in the course of its work, an RPA BOT definitely encounter a situation it was not programmed to handle – and, in those cases, it will stall. Those cases can be for foreseeable reasons – security or access constraints – or it can be for unforeseeable reasons – like missing or incorrect data. Increasingly, RPA developers are ab
Read more