modeling

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.

This paper outlines a technique to evaluate formation temperature shifts when closely spaced wells are produced concurrently and applies the technique to validate the design basis for new development.

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.

In this paper, an energy-based 3D fracture-reconstruction method is proposed to derive the complex fracture network from microseismic data in a shale gas reservoir.

This study introduces a cleanup- and flowback-testing approach incorporating advanced solids-separation technology, a portable solution, equipment automation, improved metallurgy, and enhanced safety standards.

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.

This paper focuses on the vital task of identifying bypassed oil and locating the remaining oil in mature fields, emphasizing the significance of these activities in sustaining efficient oilfield exploitation.

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

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.

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.