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A telecommunications company is looking to bring better wireless connectivity to the oil and gas industry, expanding its network to offer wireless data in the Permian Basin and three other oil and gas regions. Despite having some of industry’s most hazardous working environments, a sector that pioneered the adoption of digital technology has been slow to exploit artificial intelligence and machine learning in the area of health and safety. BP says it will supply Amazon Web Services with 170 MW of renewable energy, the equivalent of powering 125,000 homes each year. The size of the digital prize is large. But deploying digital technologies at scale is proving harder than first thought.
Don't miss out on the latest technology delivered to your email every two weeks. If you are not logged in, you will receive a confirmation email that you will need to click on to confirm you want to receive the newsletter. Data Science and Digital Engineering in Upstream Oil and Gas (DSDE) is an online publication from SPE that presents the evolving landscape of data management and data use in the industry. Sign up for the monthly newsletter to receive the latest from DSDE in your Inbox. Studies conducted through collaboration between an operator that knows the physical reality and a data-science company that knows the best machine-learning methods yield good practical results.
GHGSat announced a new service for visualizing greenhouse gas emissions. The interactive online resource will be freely available and will be formally launched during COP26 in November. Many forms of remote sensing imagery can be used, along with data sets and the resultant products, to improve the efficiency and safety of upstream oil and gas operations on the North Slope of Alaska. This paper updates a previous case study and presents the results of actual implementation of an optimized steam-injection plan based on the model framework. This paper details how artificial intelligence was used to capture analog field-gauge data with a dramatic reduction of cost and an increase in reliability.
What Is the Most Important Question for Data Science (and Digital Transformation)? With so many buzzwords surrounding artificial intelligence and machine learning, understanding which can bring business value and which are best left in the laboratory to mature is difficult. This article outlines 10 top trending technologies for 2019, a list that covers diverse topics such as security, the Internet of things, reinforcement learning, energy sustainability, and smart cities. Joelle Pineau, a machine-learning scientist at McGill University, is leading an effort to encourage artificial-intelligence researchers to open up their code. What is explainability in artificial intelligence, and how can we leverage different techniques to open the black box of AI and peek inside? This practical guide offers a review and critique of the various techniques of interpretability.
Baker Hughes is still a GE company, but it has partnered with a second company for artificial intelligence expertise, C3.ai. The deal is expected to speed the integration of AI into oilfield operations by the company which also markets GE’s device analytics platform, Predix. An assortment of sustainability initiatives shows how the oil and gas industry, leveraging its reach, diversity, and resources, is going well beyond just supplying energy to impact the world for the better. The firm hopes to remedy the cost-, labor-, and time-intensive process of executing offshore projects through deployment of “Subsea Connect,” which it says can cut project development costs by 30%. The deal would raise nearly $4 billion for GE, which plans to reduce its stake in the oilfield services company from 62.5% to at least 50.1% after the transactions.
Service firms are diversifying their portfolios, in part driven by large-scale budget cuts among operators since the industrywide downturn. The 100-year-old service company has joined the effort to achieve net-zero carbon emissions over the next 30 years. The move follows similar companywide efforts by BP and Shell. Subsea advancements in the works include longer tiebacks, an underwater drone that lives on the seafloor, and a robotic manifold capable of actuating dozens of valves. Do these new capabilities, born of necessity, signal a sea change in industrywide technology development?
Concho Resources and Enverus say turning to automated software for tracking invoices could save the upstream business many millions of dollars. Many forms of remote sensing imagery can be used, along with data sets and the resultant products, to improve the efficiency and safety of upstream oil and gas operations on the North Slope of Alaska. The XamXung field offshore Sarawak, Malaysia, is a 47-year brownfield with thin remaining oil rims that have made field management challenging. The dynamic oil-rim movement has been a key subsurface uncertainty, particularly with the commencing of a redevelopment project. This paper details how artificial intelligence was used to capture analog field-gauge data with a dramatic reduction of cost and an increase in reliability.
This paper details how artificial intelligence was used to capture analog field-gauge data with a dramatic reduction of cost and an increase in reliability. Well spacing optimization is one of the more important considerations in unconventional field development. This paper highlights the results of a test campaign for a tool designed to predict the short-term trends of energy-efficiency indices and optimal management of a production plant. Malaysia’s Petronas, Shell Malaysia, and Thailand’s PTTEP are now in the midst of full-scale digital adoption. The companies are beginning to see results, but none is counting on a “big bang” in development of the technology soon.
Upon completion of this course, participants are expected to have a good understanding of the characteristics of the machine learning approaches and be able to use them to identify potential application domains in the upstream oil and gas industry. They will acquire detailed knowledge of the popularly used machine learning algorithms and the workflow to employ these algorithms to solve petroleum engineering problems. Finally, they will see the demonstrations of different machine learning algorithms to reservoir characterization, production analysis, well productivity forcast, and recovery enhancement in tight/shale reservoirs.
This paper focuses on compressor systems associated with major production deferments. An advanced machine-learning approach is presented for determining anomalous behavior to predict a potential trip and probable root cause with sufficient warning to allow for intervention. The objective of this case study is to describe a specific approach to establishing an exploration strategy at the initial stage on the basis of not only uncertainty reduction, but also early business-case development and maximization of future economic value.