DSDE: In Theory

The authors propose a hybrid virtual flow and pressure metering algorithm that merges physics-based and machine-learning models for enhanced data collection.

The authors introduce a novel framework combining dynamic mode decomposition, a data-driven model-reduction technique, with direct data assimilation to streamline the calibration of carbon-dioxide plume evolution models.

A detailed comparison of two leading software platforms demonstrates the effective application of these platforms in modeling complex reservoir dynamics and biochemical reactions in geological formations for risk assessment in underground hydrogen storage.

The authors of this paper present a novel joint-domain full waveform inversion framework optimizing travel-time accuracy in both data and model domains.

The main goal of this research work was to determine subseismic faults and fracture corridors and their characteristics, including density and orientation, for a Paleocene fractured carbonate reservoir.

The authors make the case that data science captures value in well construction when data-analysis methods, such as machine learning, are underpinned by first principles derived from physics and engineering and supported by deep domain expertise.

Regional pore-pressure variations in the Leonardian- and Wolfcampian-age producing strata in the Midland and Delaware basins are studied using a variety of subsurface data.

A numerical simulation study based on experimental data of 2D and 3D models is presented to examine immiscible fingering during field-scale polymer-enhanced oil recovery.

This paper tests several commercial large language models for information-retrieval tasks for drilling data using zero-shot, in-context learning.

Given the diversity of coiled tubing well-intervention data, many acquisition labels are often missing or inaccurate. The authors of this paper present a multimodal framework that automatically identifies job type and technologies used during an acquisition.