(WO) Oilfield service providers have long played a critical role in the technical advancement of the upstream oil and gas industry. As the frontline provider of equipment and expertise, operators from the largest national oil companies to the smallest of independents count on these firms to drive technical innovation, improving performance in the field under a broad range of economic conditions.
Every innovation the upstream oil and gas sector has seen, from the evolution of ever more durable poly diamond crystalline drill bits to subsea blowout preventers capable of 20,000 psi working pressures, all share a common genesis—the drive to improve return on investment. Fluctuating output prices, coupled with pressure to reduce CAPEX, are key drivers. The industry’s deep cultural commitment to the safety of its operations also plays a role in the development of new technologies and processes.
The renaissance of the Fourth Industrial Revolution comes at a critical time for the oilfield service industry. As drilling technologies mature and siloed computerization processes reach their limits, this evolved digital technology space is creating a new horizon for innovations in production, uptime and efficiency, based on more effective integration of data from multiple sources.
The upstream oil and gas industry is investing heavily in the search for new reserves or resource plays, new extraction technologies, and advancements in operational automation. Even though this involves some risk, tech-savvy businesses understand that today’s investment can deliver massive returns in the future. Regardless of the asset type, the need of the hour is to implement emerging technologies to optimize oil recovery and maximize output, says GlobalData, a leading data and analytics company.
Abhishek Paul Choudhury, Disruptive Tech Analyst at GlobalData, said, “Oil and gas companies are increasingly adopting intelligent automation and other digital enablers to synthesize large amounts of data and derive useful insights to ease complex field activities that have defined the upstream value chain. IoT technologies coupled with AI algorithms are in action to screen and discover optimal acreage options, improve subsurface modeling, and enhance drilling performance.”
As in any technology-driven industry, oilfield service companies had to overcome some key to fully access the value of the system data that were available. This was sometimes referred to as “dark data”—systems were capable of generating more information than was immediately applicable, so it was frequently left unexplored. Other times, systems were created in a vacuum, designed to work in a proprietary manner with not much thought as to how they might integrate with products developed elsewhere.
The data challenges manifested themselves in a few different ways, namely:
- High-quality data were not easily integrated across products;
- Isolated products meant siloed data were very deep, but had no breadth;
- That lack of breadth reinforced a “not-invented-here” mindset, resulting in duplication of innovative effort between companies.
What the Fourth Industrial Revolution has brought to oilfield service companies is the capability to integrate data across these silos, and between engineering disciplines. Modern, open-architecture programming is unlocking the value of all the upstream data generated, from seismic surveys to drilling program design and artificial lift performance, to deliver more efficient, and safer, workflows that meet the objective of improving return on investment.
INTELLIGENT AUTOMATION: OPEN-ARCHITECTURE ENVIRONMENTS TO DESIGN, DEVELOP AND DEPLOY
Oilfield service companies are developing their own open-architecture environments, where clients’ digital teams can outline their own journeys through the digitalization life cycle.
Halliburton recently introduced its DS365.ai cloud service to accelerate digital transformation via intelligent automation. Industry-specific artificial intelligence and machine learning models run on the company’s OSDU™ Data Platform, utilizing the scalable architecture of the iEnergy® Cloud. Data engineers and data scientists are able to design, develop and deploy AI models at scale, or quickly train ML models.
In the DS365.ai space, participants can access and modify open-source data models, and share their work across the community—complete with a familiar five-star ranking system.
Models are available as stand-alone services, or they can be integrated into broader applications, including Assisted Lithology Interpretation, Seismic Engine and Real-Time Well Engineering.
“We are excited to introduce DS365.ai, an industry first approach to quickly extract insights from data silos at a time when data scientists need domain-specific accelerators to deepen insights, and operations require data science investments to scale and interoperate with existing tools,” said Nagaraj Srinivasan, senior vice president of Landmark, Halliburton Digital Solutions and Consulting. “The DS365.ai models deliver operational value across dozens of successful projects for customers of all sizes.”
Halliburton’s DS365.ai delivers rapid return on investment, with more than 70 projects and over 60 AI/ML models deployed at scale. Positive financial outcomes include a national oil company predicting an artificial lift failure, proactively saving $4 million in repair costs across 60 wells. In Latin America, an independent oil company leveraged DS365.ai to deploy an ML seismic conversion methodology, reducing uncertainty that led to a 70 percent reduction in modeling cycle time.
Baker Hughes has made its JewelSuite applications available in the Microsoft Azure Marketplace, streamlining deployment and management of AI and ML applications on the Azure cloud platform.
The online marketplace offering delivers immediate access to Baker Hughes’ integrated field development and well construction applications through a “freemium” model, with tiered service plans for more complex application modules.
JewelSuite in Azure Marketplace is served as part of the customer’s subscription to Azure, operating with the customer’s own technical software infrastructure to apply subsurface data to the JewelSuite applications. This subscription model enables the customer to reduce software licensing and support costs by as much as 30 percent.
The JewelSuite Subsurface Modeling application enables rapid creation of precise geological models, readily updated and modified with new well information to facilitate optimal field development and improved production. Shell is among the more than 30 client companies presently utilizing JewelSuite.
“JewelSuite, available on Microsoft Azure Marketplace, is backed not only by the combined expertise of Baker Hughes and Microsoft, but also by its long history of co-development and practical use by Shell,” said Shan Jegatheeswaran, senior vice president of digital for Oilfield Services at Baker Hughes. “With this ability, JewelSuite is now a truly differentiated digital offering for our customers and delivers a functional experience in a flexible and cost-effective manner for our customers. This milestone is another strong example of our strategy to digitally transform upstream operations with more efficient, productive, predictive and smarter outcomes.”
Schlumberger is partnering with AVEVA to link edge, AI and cloud digital systems in its DELFI™ cognitive E&P environment, optimizing how energy companies acquire, process and apply field data to improve wellsite efficiency, better manage equipment health, and improve performance.
The IoT and cloud capabilities of both companies are delivered to the upstream market through this collaboration, including AVEVA’s Pi System data management platform capabilities, and Schlumberger’s domain expertise and analytics capabilities provided by Agora edge and IoT solutions in the DELFI environment.
“This partnership brings together our edge and cloud solutions with the AVEVA PI System to seamlessly liberate access to data accelerating insights and action,” said Rajeev Sonthalia, president, Digital & Integration, Schlumberger. “By integrating our domain expertise, secure edge technology and digital applications in the DELFI environment with AVEVA, we will enable customers to increase efficiency and transform their production operations.”
IOT TECHNOLOGY: LEARNING MORE FROM REAL-TIME OPERATIONS
Improving Machine-to-Machine (M2M) communication, and gathering new insights from virtual sensors, is enabling oilfield service providers to increase the volume, and variety, of data to fuel the decision-making process while finding new ways to improve operational performance.
Weatherford is seeing renewed interest in its remote operating, visualization, edge automation, and AI offerings related to artificial lift operations. This comes as additional sensors or controllers are being introduced to measure pressure, temperature, vibrations and flowrates. However, budget constraints are proving challenging to the significant capital investment needed to fully integrate these new technologies.
While a significant percentage of artificially-lifted wells already have sensors and automation equipment, most of it is built on outdated technology designed only to perform basic control functions at pre-set operating setpoints, specified by the production engineer at the wellsite through a SCADA software tool. Limitations of SCADA communication means these controllers often operate with outdated setpoints, optimized to run at safe limits to account for changes in reservoir conditions or other physical changes.
Weatherford is applying its ForeSite Edge smart device to these existing installations, maximizing the use of existing digital equipment at the wellsite while integrating proven lift optimization and well-modeling technologies. Whether ForeSite Edge is implemented with controllers already at the website, or installed as a stand-alone device, the system applies modern, proven control models to the well, along with advanced artificial intelligence capabilities to increase production while lowering operating costs.
NOV is addressing a common concern when introducing digital products for processing technologies—a lack of sufficient sensors across the facility. While advanced online characterization of process fluids is a valuable outcome, many instruments in a process system are not fully utilized for process optimization.
Virtual sensors can be digitally generated to measure parameters where physical sensors are not available, or where existing systems are not working to their full potential. For example, monitoring of monoethylene glycol (MEG) buildup, used in flowlines to prevent hydrate buildup, enables operators to reduce the risk of system blockages and associated downtime.
NOV’s MEG Process Intelligence Manager reduces operational expenses and facilitates lower-manned operations. A virtual sensor predicts the accumulation of key ions and precipitation of solids in the reclaimer. The simulator is correlated to available secondary sensor data on the feed to the reclaimer and calibrated to results from actual fluids measurements taken at discrete intervals. This allows for continuous updates to the model between fluids samples, based on the input data from the secondary sensor.
PREDICTIVE MAINTENANCE: SOLVING PROBLEMS BEFORE THEY OCCUR
Unplanned downtime in offshore drilling operations can be reduced or even eliminated by digitally understanding the state of the mechanical systems at work.
Baker Hughes is working to integrate the people, processes and technologies at work offshore to find a baseline of reliability and certainty, with minimizing of rig downtime as the primary goal. With modern deepwater drilling projects, millions of dollars can be at stake, due to an unplanned outage.
Baker Hughes’ Sealytics integrates with the subsea drilling control system, delivering the fidelity required to link the maintenance programs that the OEM prescribes, and the equipment owner executes. The result is a comprehensive look at the system’s condition, and what is being done to the system, establishing a chain of care, custody and control.
“Once you have that, this is where you can start to apply simple things, like cycle counts. We can monitor cycles, and where in the drilling system the cycles are occurring. Then, you can start to integrate that with the engineering disciplines of mean time to failure, to begin to improve the performance of the equipment,” said Chuck Chauviere, vice president of subsea drilling systems at Baker Hughes. “Prior to this, we just had time-based prescriptions.”
Sealytics provides the visibility to integrate maintenance requirements from operational results into future planned system downtimes. This data-driven knowledge enables planned maintenance events to include preventive tasks, based on how the systems are being used in real world conditions.
The edge device is connected to the subsea blowout preventer (BOP) control system, and it can access a number of direct sensors. Sealytics can also access a range of indirect sensors, measuring pressure, temperature and time. Based on these data, the Sealytics algorithms are able to model the physical state of the complete BOP system, based on user inputs.
“Sealytics uses those data to say, ‘when you tell the system to do this one thing, another chain of events occurs.’ It is then that we can utilize our subject matter experts to understand the picture that the system is creating, a multifaceted data set,” Chauviere said. “Those data can then be sent onshore, to be put into a broader data lake, so we can get a richer data experience to then make a broader assessment of a fleet of equipment that is similar, so we can then begin to develop a much more rich dataset.”