Digital technologies serve as a primary theme of this year’s group, with a few environmentally conscious firms included in the mix. The large independent put together a team of data scientists, software developers, and petrotechnical staff to create a forward-looking vision for how to use digital technology to solve problems. Do women in academia face the same challenges as their peers in industry? Using maglev technology, a new artificial lift system seeks to boost production output by sucking down reservoir pressure from inside the wellbore and from inside the reservoir. The projects are designed to reduce technical risks in enhanced oil recovery and expand application of EOR methods in conventional and unconventional reservoirs.
The international major has been playing with intelligent programs for years, but this new deal shows that it is now ready to scale those efforts up to cover hundreds of thousands of pieces of equipment. Pioneer's Analytics Project Reveals the Good and Bad of Machine Learning A recent research effort has shown that the digital journey is full of stumbling blocks. Just like humans, advanced computing technology will get some things right and some things wrong. Some operating companies are now enlisting engineers as foot soldiers in their analytics army. It is not required yet, but those looking to get ahead would be wise to get involved.
The large independent put together a team of data scientists, software developers, and petrotechnical staff to create a forward-looking vision for how to use digital technology to solve problems. Funding for startups in the upstream industry does not always guarantee that oil and gas companies will want to test the new technology. A new venture and accelerator model hopes to change this through guaranteed pilots. How Do Oil and Gas Investors Pick Entrepreneurs?
Petrobras says it can produce oil for a lower break-even price than onshore shale plays, including the Permian Basin. Brazil’s offshore sector has cut the cost of deepwater production but comparisons based on break-even prices are slippery. Analytics, sensors, and robots are changing the way one of the world’s largest oil and gas companies does business. Underpinning all the new technology though is a shift in how BP thinks, and what it means to be a supermajor in the 21st century. When a rig is stacked, its owner has two choices: spend millions to keep it in good shape, or let it rust out.
The large independent put together a team of data scientists, software developers, and petrotechnical staff to create a forward-looking vision for how to use digital technology to solve problems. The value of hidden-danger data stored in text can be revealed through an approach that can help sort and interpret information in an ordered way not used previously in safety management. 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. To analyze the status of digital transformation strategies and the pace of implementation in the Middle East, an SPE Applied Technology Workshop brought together operating and service companies and consulting firms for a discussion. Findings from Kayrros suggest the average Permian well is both less productive and more expensive than reflected in public data.
A reduced-order modeling framework is developed and applied to simulate coupled flow-geomechanics problems. The reduced-order model is constructed using POD-TPWL, in which proper orthogonal decomposition (POD), which enables representation of the solution unknowns in a low-dimensional subspace, is combined with tra jectory piecewise linearization (TPWL), where solutions with new sets of well controls are represented via linearization around previously simulated (training) solutions. The over-determined system of equations is pro jected into the lowdimensional subspace using a least-squares Petrov-Galerkin procedure, which has been shown to maintain numerical stability in POD-TPWL models. The states and derivative matrices required by POD-TPWL, generated by an extended version of Stanford's Automatic-Differentiation-based General Purpose Research Simulator, are provided in an offline (pre-processing or training) step. Offline computational requirements correspond to the equivalent of 5-8 full-order simulations, depending on the number of training runs used. Runtime (online) speedups of O(100) or more are typically achieved for new POD-TPWL test-case simulations. The POD-TPWL model is tested extensively for a 2D coupled problem involving oil-water flow and geomechanics. It is shown that POD-TPWL provides predictions of reasonable accuracy, relative to full-order simulations, for well-rate quantities, global pressure and saturation fields, global maximum and minimum principal stress fields, and the Mohr-Coulomb rock failure criterion, for the cases considered. A systematic study of POD-TPWL error is conducted using various training procedures for different levels of perturbation between test and training cases. The use of randomness in the well bottom-hole pressure profiles used in training is shown to be beneficial in terms of POD-TPWL solution accuracy. The procedure is also successfully applied to a prototype 3D example case.
History of the PetroBowl ® Program The PetroBowl® competition was founded in 2002, and was created, organized, and administered by the SPE Gulf Coast Section taking place once a year during the SPE Annual Technical Conference and Exhibition (ATCE). As its popularity grew, a two stage pilot program was introduced in 2013 to transition the contest into a truly global event. This included two regional qualifiers taking place in Africa and Asia in 2014, and an expansion to six regional qualifiers in 2015. Following the successful completion of the pilot program, PetroBowl is now recognized as an SPE International global program. Unfortunately, due to VISA issues, four teams had to withdraw from the contest resulting in 28 teams participating on the day.
By International Petroleum Technology Conference (IPTC) Monday, 25 March 0900-1600 hours Instructors: Olivier Dubrule and Lukas Mosser, Imperial College London Deep Learning (DL) is already bringing game-changing applications to the petroleum industry, and this is certainly the beginning of an enduring trend. Many petroleum engineers and geoscientists are interested to know more about DL but are not sure where to start. This one-day course aims to provide this introduction. The first half of the course presents the formalism of Logistic Regression, Neural Networks and Convolutional Neural Networks and some of their applications. Much of the standard terminology used in DL applications is also presented. In the afternoon, the online environment associated with DL is discussed, from Python libraries to software repositories, including useful websites and big datasets. The last part of the course is spent discussing the most promising subsurface applications of DL.
In their outlook for 2019, SPE’s technical directors suggest petroleum engineers take a moment to reflect on the industry’s great feats, and then get back to work to do things better. A contest where teams of college students design and build an automated drilling rig able to deal with hazardous obstacles in a test block, showed how a small change can be engineered to matter. How Close Is Too Close? The ideal well spacing is in the eye of the beholder. The decision depends on so many factors that machine learning is now trying to determine the best combination of ingredients.
Mark McClure, Founder and CEO of McClure Geomechanics, gives his pitch at the 4th Annual Rice Alliance Startup Round Up which was hosted by the 2018 Offshore Technology Conference (OTC). Dozens of other startups also made their case for why the oil and gas industry's largest corporate venture groups should invest in their budding firms. Being a startup in the upstream industry does not make for an easy existence. For starters, the target customer base is famously known for its collective skepticism of new and unproven technologies. And even if an innovation passes technical muster, it must go well beyond creating nominal value to entertain wide interest from oil and gas companies.