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These awards recognize those members who have contributed exceptional service and leadership to the Society, as well as those who have made major professional contributions to their technical disciplines at the regional level.
The company behind a 42-mile sand conveyor system is now leaning into autonomous vehicles for proppant logistics.
Join us in congratulating the International Award recipients.
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VR provides an immersive and cost-effective approach to essential oilfield training. -
Automated workflow unifies geological, completion, and production data to inform speedier, better investment decisions for nonoperated assets.
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SPE President Terry Palisch is joined by SPE CEO Simon Seaton to discuss the member value survey and what is new with ATCE.
Implemented for the first time offshore, the technology uses artificial intelligence to operate wells autonomously.
The planned long-term partnership aims to digitally transform Aker BP’s subsurface workflows in an effort to lower costs, shorten planning cycles, and increase production.
BMT and Ocius plan to deploy 1,000 autonomous vessels to collect data as “satellites of the sea.”
This paper outlines how one company uses digital technologies to manage HSE risks in project delivery, developing an artificial intelligence (AI) predictive model to predict HSE risks and incidents based on historical incident data.
The two companies announced a 10-year partnership to work on digital solutions to challenges including carbon capture, storage, and use.
The RoboWell technology for well control will be available globally through Halliburton’s Landmark iEnergy hybrid cloud.
Machine learning is refining gas lift production optimization with scalable automated workflow.
The chief operating officer of Chesapeake Energy tells the Unconventional Resources Technology Conference that small wins can pave the path to big achievements.
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Fundamental research conducted to derive a transport model for ideal and partitioning tracers in porous media with two-phase flow that will allow fast and efficient characterization and selection of the correct tracer to be used in field applications. -
A self-updating and customizable data-driven strategy for real-time monitoring and management of screenout, integrated with proppant filling index and safest fracturing pump rate, is proposed to improve operational safety and efficiency at field scale. -
This work presents an integrated multiphase flow model for downhole pressure predictions that produces relatively more-accurate downhole pressure predictions under wide flowing conditions while maintaining a simple form. -
This paper details how the reservoir modeling workflow can be accelerated, and uncertainty reduced, even for challenging greenfield prospects by constructing multiple small fit-for-purpose integrated adaptive models.
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Geo Oceans has achieved class sign off for the world’s first entirely robotic FPSO cargo oil tank special survey. -
The authors of this paper review the advantages of machine learning in complex compositional reservoir simulations to determine fluid properties such as critical temperature and saturation pressure. -
This study describes a technique combining borehole acoustic reflection imaging with production logging in a horizontal well experiencing high water cut with the aim of enabling a mitigation plan. -
Operators tell an audience at the Unconventional Resources Technology Conference how a hybrid expandable liner system and machine-learning-based analysis improve the bottom line.
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It’s hard to create corporate or master data strategies when every user group you talk to has a different idea of what “good” should look like from their perspective.
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A seismic prediction model is developed and presented in a case study to simulate the magnitude and timing of triggered seismic events with the intent to manage and mitigate environmental impacts resulting from induced seismicity during subsurface development activities.
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A universal, automated approach to condition-based maintenance of drilling rig mud pumps is developed using acoustic emission sensors and deep learning models for early detection of pump failures to help mitigate and reduce costs and nonproductive time generally associated with catastrophic pump failures.
