Reservoir
The events will be co-located 3–5 May 2027 at Reliant Park in Houston, Texas.
This paper presents a novel reservoir engineering/reservoir simulation approach—a data-driven interwell-connectivity model augmented as a digital twin—to predict reservoir dynamics and optimize operations in the Changqing oil field of China.
This work describes a study in which distributed data parallel training, paired with a node-local caching pipeline, enabled efficient multigraphics-processing-unit scaling for a CO₂-storage graph-neural-network surrogate while maintaining generalization.
-
While some try to put the two enormous oil producers toe-to-toe, the best thing to do might be to understand why they are different. -
Aker Solutions and FSubsea have agreed to a joint venture, named FASTSubsea, to help operators increase oil recovery.
-
SponsoredMesser and Nissan Chemical recently introduced a new Huff ’n’ Puff process that combines CO2 or N2 gas and nanoparticles for synergistic multi-spectrum recovery enhancement from aging, depleted wells.
-
This paper covers the staged field-development methodology, including analysis and evaluation of various development concepts, that enabled the company to optimize both completion design and artificial-lift selection, reducing downtime and lowering operating costs by nearly 50%.
-
In this work, the authors developed a numerical model of in-situ upgrading (IU) on the basis of laboratory experiences and validated results, applying the model to an IU test published in the literature.
-
This paper shares experience gained in the Ashalchinskoye heavy-oil field with a two-wellhead SAGD modification. As a result of a pilot for this technology in Russia, the accumulated production of three pairs of these wells is greater than 200,000 tons.
-
To enhance the applicability of localization to various history-matching problems, the authors adopt an adaptive localization scheme that exploits the correlations between model variables and observations.
-
Machine-learning methods have gained tremendous attention in the last decade. The underlying idea behind machine learning is that computers can identify patterns and learn from data with minimal human intervention. This is not very different from the notion of automatic history matching.
-
A challenging problem of automated history-matching work flows is ensuring that, after applying updates to previous models, the resulting history-matched models remain consistent geologically.
-
This paper presents a work flow to match the history of reservoirs featuring complex fracture networks with a novel forward model.
