Decarbonization
Cella’s approach borrows established EOR practices to mineralize pure‑phase CO2 in basalt rocks while reducing water requirements.
Experts and industry leaders gathered in The Woodlands, Texas, recently to sift through the challenges of carbon capture, utilization, and storage. The puzzle is coming together, but some critical pieces are still needed before the results look like the picture on the box.
An investigative study examines the use of creeping shale formations as a more durable alternative to conventional cement barriers in carbon dioxide storage wells, potentially enabling safer long-term underground carbon storage.
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This study highlights how the recovery and reuse of existing hydrocarbon infrastructure can contribute to the diffusion of district-heating projects that implement the principles of the circular economy. -
The floating production, storage, and offloading vessel is the first in the world with a postcombustion carbon capture pilot system.
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The plant is designed to capture up to 100,000 tonnes of carbon dioxide annually for the Netherlands-based sustainable energy supplier Twence.
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A new modeling tool examines the pathways and technology necessary for California to reach carbon neutrality economywide by 2045.
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Aramco expects to complete Phase 1 construction of the 9-mtpa facility in 2027.
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The modular facility will process associated gas to produce electricity for up to 200,000 households in the Basra region.
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As Southeast Asia’s third-largest gas producer, PTTEP is investing in its energy security by prioritizing gas production and building up a global LNG supply chain.
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In this paper, a dynamic multiphase-flow simulator is used to evaluate the effectiveness and suitability of using a subsea capping stack to respond to a CO₂ well blowout.
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The objective of this study is to develop an explainable data-driven method using five different methods to create a model using a multidimensional data set with more than 700 rows of data for predicting minimum miscibility pressure.
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The authors of this paper propose hybrid models, combining machine learning and a physics-based approach, for rapid production forecasting and reservoir-connectivity characterization using routine injection or production and pressure data.
