modeling

This paper proposes a novel approach toward drilling maximum-reservoir-contact wells by integrating automated drilling and geosteering software to control the downhole bottomhole assembly, thereby minimizing the need for human intervention.

This paper offers an exploration into the field applications of multiphase flowmeters (MPFMs) across global contexts and the lessons learned from implementation in a smart oil field that uses several types of MPFM.

This paper describes a data-driven well-management strategy that optimizes condensate recovery while preserving well productivity.

This paper explores the evolving role of the digital petroleum engineer, examines the core technologies they use, assesses the challenges they face, and projects future industry trends.

This study illustrates the new capabilities, tailored for carbon-dioxide storage applications, of a modeling framework that provides a quantitative, risk-based assessment of the long-term integrity of legacy plugged and abandoned wells.

This paper describes an auto-adaptive workflow that leverages a complex interplay between machine learning, physics of fluid flow, and a gradient-free algorithm to enhance the solution of well-placement problems.

This paper details a data-driven methodology applied in Indonesia to enhance flare-emission visibility and enable targeted reduction strategies by integrating real-time process data with engineering models.

This paper assesses the technical feasibility of geological carbon storage in the operator’s Brazilian brownfields, focusing on mature oil fields and associated saline aquifers.

This paper addresses the difficulty in adjusting late-stage production in waterflooded reservoirs and proposes an integrated well-network-design mode for carbon-dioxide enhanced oil recovery and storage.

This work presents the development of fast predictive models and optimization methodologies to evaluate the potential of carbon-dioxide EOR and storage operations quickly in mature oil fields.