There’s an untapped opportunity for upstream oil and gas companies to reinvent and optimize their operations using data science. And, with the pressure increasing for organizations to commit to climate change without compromising on their bottom line, there’s never been a better time than right now.
If you’re in the upstream segment, you’re already acquiring tons of data through drilling operations. Using this data for traditional analytics alone won’t cut it—it’s not enough to meaningfully transform legacy processes. Accounting for unique geological properties in new locations and estimating the yield of a reservoir require more advanced techniques.
Upstream organizations who leverage predictive analytics can make smarter process decisions, improve operational efficiency, and increase profitability— and it’s often in reach without having to capture any additional data. Unsure of where to start? In this post, we’ll dive into three common challenges upstream oil and gas companies face in exploration and drilling, and how data science can help address them.
3 Challenges Upstream Oil + Gas Companies Can Solve with AI
If you don’t have first-hand experience working on a drill site, you wouldn’t have any idea of the complexities the upstream segment faces every day during exploration and drilling. With data science, you can reduce costs and wasted time, all while keeping environmental regulations top of mind.
1. The High Cost of Exploration and Drilling
Drilling an onshore well costs around $5-8 million, while a deepwater oil well often costs over $100 million and requires a huge amount of manpower, equipment, logistics, and materials. Difficult environmental conditions and varying, location-specific geologies make drilling particularly unpredictable for companies that solely rely on historical data to guide their exploration.
No one wants to waste valuable resources looking in the wrong place, especially with such expensive consequences. By leveraging data science, upstream oil and gas organizations can get a more accurate understanding of where to drill so they can proceed with confidence.
Combining historical and seismic data for a specific region helps determine the best drilling locations based on a variety of factors—subsurface structure, rock formations, and possible geologic risks, to name a few. Using seismic and geological data can also increase the accuracy of predicting oil pockets, identifying reservoirs with the highest yield to increase your ROI.
In addition to drilling more productive (and profitable) wells, analyzing this data can minimize geologic risk by identifying potential hazards. Earthquake-prone regions and areas susceptible to sinkholes can easily be avoided, ensuring you act with precision while reducing costs and negative externalities.
2. Costly Equipment Failures and Maintenance
The high cost of drilling doesn’t end with finding the right location. If you run into issues with equipment downtime, the cost goes up significantly. In fact, unplanned downtime costs offshore oil and gas companies an average of $49 million annually, exacerbated by aging assets and budget constraints.
On top of the direct costs related to equipment repair and manual labor, lost production time is a major profit-killer. Not to mention that if critical equipment failure occurs, there are considerable safety concerns and potentially fatal consequences.
To minimize the cost of equipment failure and maintenance, oil and gas companies need to have a proactive, rather than a reactive, approach. Predictive analytics uses sensor data and historical data to forecast and plan for necessary maintenance, rather than risking a costly failure.
Combining predictive analytics with real-time streaming data from rigs can help workers understand operational risks instantaneously, further anticipating and preventing problems on the field.
By investing in predictive analytics, upstream oil and gas providers waste less time on maintenance, lower operational costs, and reduce the risk of catastrophe, all while boosting yield and revenue.
3. Increased Environmental Pressures and Regulations
Today, there’s a greater need than ever before for oil and gas companies to reduce their carbon footprint. Climate change is a global concern, and if your organization isn’t doing all that you can to combat its negative impacts, you’re a liability.
On top of social responsibility and brand reputation, environmental regulations seeking to reduce emissions govern how the oil and gas industry can operate going forward. The EPA’s Clean Air Act and President Biden’s bill reinstating mandatory methane emissions monitoring are two prominent examples of the global initiative to reduce pollution.
Becoming a more data-driven organization isn’t only going to help make your business more profitable—it can help you comply with regulations like those mentioned above, too. NETL, the National Energy Technology Laboratory, has leveraged machine learning to predict and prevent oil spills. BP’s Wamsutter field in Wyoming saw a 74% decrease in their wells’ methane leaks after implementing an AI-powered sensor system.
By reimagining existing processes with AI, the upstream segment can ensure compliance and mitigate environmental risk. In addition, reducing emissions lessens negative health impacts on exposed workers, creating a healthier and safer work environment.
Fuel Innovation with Data Science
Now is the time to apply data science throughout your organization and use it to improve your bottom line as well as your environmental footprint. The best part? Your organization is likely already collecting the data you need to transform your operations. You don’t need to completely reinvent the wheel, just make the most of the information you already have.
AI can do more than solve top challenges for upstream oil and gas—it can spark innovation, too. Going forward, AI can help organizations rethink their processes to maximize workplace safety, ensure profitable operations, and reduce their carbon footprint.
Want to take a closer look at how the upstream segment is transforming their operations with AI? Download the full whitepaper, AI for Upstream Oil and Gas, to read about how RapidMiner users have utilized AI for subsurface classification and PCP pump failure prediction.
This blog post is guest written by Manish Okhade of Wipro. Manish has more than two decades of industry experience across domains, including engineering, R&D, digital, and artificial intelligence. He is presently leading the Predictive Asset Maintenance (PAM) Practice in Wipro’s AI/ML solutions group where his current focus areas are building accelerators for the oil & gas and manufacturing spaces, thought leadership, and forging partnerships with niche startups in the PAM domain. Manish has published numerous research papers into international conferences and symposiums, which have also been published in a book.
