Solving the Digital Oilfield Data Dilemma – Part 5 – Unlocking Machine Learning & AI with Big Data Management

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In my fifth and final blog in this series on solving the digital oilfield data dilemma I want to talk about “the prize,” i.e., the payoff after you have put everything into place to sustain successful data management. Be sure to read or revisit the earlier blogs that explained why oil & gas companies must master data in the digital oilfield and the 3 dimensions of effective data management:

1) building a digital gathering system with connectors and APIs,

2) aggregating, orchestrating, and quality assuring data, and

3) surfacing decision-ready datasets to people and driving data consumption.

In terms of attaining the prize, there are many business goals data management should achieve for workflows across the energy enterprise, including land, drilling and completions, geoscience, production management, field operations, and even human resources. Think about the impact of poor data management on an E&Ps cash register, from allocations and well tests to sales and financial statements. No matter the price of WTI or Henry Hub, even a little bad accounting data or rounding error can hurt the bottom line.

Then there are new prizes (or burdens, depending on how you look at it) as regulatory and compliance obligations expand. Companies must manage an increasingly complex variety of environmental data for accurate HSE reporting (e.g., audio, visual, olfactory inspection of oilfield assets/AVO). The “E” in ESG is only making the need for effective environmental data management more urgent as greenhouse gas reporting increases in scope and complexity. Part of the recent Inflation Reduction Act, oil & gas companies now face a methane fee of $900 per metric ton starting in 2024 (increasing to $1,500 through 2026), making it absolutely imperative to master regulatory data management to avoid over- or underpaying the government.

Prizes of the future include predictive analytics, machine learning, and artificial intelligence. The oil & gas industry has already seen some success in areas like artificial lift optimization and drilling automation, but many PA, ML, and AI initiatives stall out or plateau because they need big data to thrive. Gartner defines big data like this:

Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.

The digital oilfield of the future runs on big data, underscoring the need for effective “big data management.” It’s a 2 part problem. First, you need robust tech and energy industry-specific capabilities like EnerHub. And second, your tech stack must be able to accommodate accelerating volumes, variety, and velocity of oilfield data now streaming from smart connected devices, SCADA, and the Internet of Things (IoT). It’s about solving big data management as well as big data consumption, bringing digital oilfield data sources together, orchestrating it, then pushing big data sets to machine learning and AI applications.

EnerHub is keeping pace, expanding its reach to include the data coming in from SCADA and IoT systems to achieve the nirvana of AI/ML. and automation. Big data management unlocks a new world of data science. The implications are staggering, from reservoir simulation, well planning, marrying completions to the rock, economic forecasts, enhanced oil recovery, and HSE. The benefits of predictive analytics and ML also extend into midstream, bringing a new level of capability to optimize operations and minimize costs, power transactions, and maximize spreads.

In today’s increasingly complex digital oilfield and energy business, your team must have a data management strategy beyond Excel and homegrown databases. Data management is the new skillset that can make or break an organization and technologies that are purpose-built for energy and can manage even the biggest data sets will set your team up to succeed and grow.

EnerHub can be at the center, connecting your entire digital oilfield ecosystem of data and apps or add value around the edge where you need to harden your own digital infrastructure. EnerHub is deployed at supermajors to bolster hydrocarbon accounting data quality and at large midstream companies to advance digital transformation. And increasingly, EnerHub is powering next generation capabilities in the digital oilfield with big data management for ingesting and consuming the data our industry will run on in the future.

Thank you for following along with this blog series and I hope you found useful insights along the way that your team can leverage in building highly effective digital oilfield data management. Please reach out to me with any questions or ideas – I’m always happy to talk data!