How a Unified Data Model Alleviates the Growing Pains of a Merger
- Sep 9, 2022 6:54 pm GMT
As we march through the latter half of 2022, oil and gas deals are expected to rise through the end of the year. As deals begin to close, exploration and production (E&P) companies must adjust for potential mergers or acquisitions.
With any merger, in any industry, there are adjustment periods for all parties involved. In addition to the usual reshuffling and restructuring associated with mergers and acquisitions, E&P companies have an additional step: Connecting and integrating wells and rigs that were previously separate. The acquisition of a portfolio of drill sites is great for any company, but many will face challenges when integrating new wells into current operating systems. To adjust properly to coming mergers and acquisitions, companies must utilize advanced technology like a Unified Data Model (UDM) to bridge interoperability barriers while seamlessly integrating new and existing systems.
Following an acquisition, it can take oil and gas companies several months to properly integrate numerous different platforms into one another. After all, a set of wells and rigs across a single company’s portfolio sometimes operates on a dozen separate systems while working under different processes. Manually implementing changes and integrating new systems can be a tiresome, time-consuming yet critical task that certainly isn’t cheap. Downtime caused by interoperability issues can cost E&P companies hand over fist, especially when barrels are constantly valued over $100. The adjustment period for these changes is an inconsiderate fly in the face of productivity as well. However, a UDM efficiently extrapolates data from a wide variety of sources and presents it in a digestible fashion. Through a series of useful features, a UDM puts merging oil and gas companies on the fast track toward efficiency.
Data Collection and Unification
One of the biggest hurdles oil and gas companies face when it comes to mergers is the unification of data between companies. Data-sharing is already an incredibly fractured process in oil and gas, and an acquisition adds another layer of difficulty. In addition to the sheer quantity of data, the information is often collected through various platforms across all parties. This makes aggregating all the data onto one platform more difficult. Even prior to any merger or acquisition, turning this data into something actionable took loads of man hours to accomplish. A UDM speeds up the process exponentially, creating sophisticated models by combining data from various sources. In an acquisition, a UDM helps operators and engineers bridge data and communication gaps to create a unified view of an entire operation, seamlessly integrating new and current wells.
A significant growing pain with mergers and acquisitions is the decrease in productivity. Due to the changes, it is often a challenge for workers to maintain optimal production while still adjusting to new systems. Using a UDM provides E&P workers a central platform that gives them all the information they need to continue to work efficiently and productively. Having a centralized hub to manage all integrations and automations, so that teams can rapidly iterate and deploy workflows, helps ensure mergers and acquisitions are a painless experience for the workers involved.
Artificial Intelligence and Machine Learning
One of the biggest talking points in the world of upstream oil and gas is the potential for artificial intelligence (AI) and machine learning (ML). As many executives look toward AI as the path of the future, UDM’s can host a wide range of AI software on a single platform. This means that different companies can integrate their software in a much more seamless way.
ML is another great digital tool for companies that want to remain productive during a merger. The main function of ML is to find patterns in large sets of data and predict outcomes. ML provides incredible value to all oil and gas companies, and a merger or acquisition is no different. The overabundance of data that comes with any deal is a certainty for companies looking to bring on new wells or rigs. Machine learning is the perfect tool for weeding through the inbound surplus of data.
Even when it is business as usual for an oil and gas firm, the main goal of the company is to be as efficient as possible while maintaining a safe work environment. Alongside the typical slowdowns associated with a merger, investors are looking toward the industry to increase production rates. This pressure, added to the challenges with merging companies, ratchets up the difficulty three-fold. This perfect storm of conditions means that workers need to be on top of their game. A UDM is the perfect tool for companies to ensure that they are running a tight, efficient operation during a time when optimization is vital.
The oil and gas field has been slow on the draw when it comes to accepting digital transformation – with good reason, too. The field has relied on more traditional methods of data management for decades. As the coming business deals look to shake up the foundation of the industry, now is the perfect time for companies to look toward a UDM to stay afloat during these changing times. A UDM comes equipped with an arsenal of tools designed to keep oil and gas companies lean and operating as efficiently as possible. A UDM is simply a must-have for oil and gas companies to stay optimal during the rapid transitions that many firms will undertake during this period of increased investment.
No discussions yet. Start a discussion below.
Get Published - Build a Following
The Energy Central Power Industry Network is based on one core idea - power industry professionals helping each other and advancing the industry by sharing and learning from each other.
If you have an experience or insight to share or have learned something from a conference or seminar, your peers and colleagues on Energy Central want to hear about it. It's also easy to share a link to an article you've liked or an industry resource that you think would be helpful.