Robotics and artificial intelligence in unconventional reservoirs: Enhancing efficiency and reducing environmental impact.

Shale, tight gas, and coal bed methane reservoirs are unconventional reservoirs that have geometrically complicated geological formations with low permeability, rendering their extraction operations difficult, expensive, and environmentally sensitive. On the other hand, recent developments in Robotics and Artificial Intelligence continue to transform how exploration, production, and maintenance activities are carried out in these reservoirs. This review paper encompasses how Robotics and AI technologies are applied to these unconventional reservoirs, improving operation efficiency, increasing the recovery rate and minimizing environmental damage. It updates on the latest status of autonomous drilling, AI-driven reservoir characterization and robotic fracturing. The paper also discusses future opportunities: emerging technologies, integration of AI with predictive analytics, and innovations toward sustainability. These are the challenges that come with possible solutions discussed in the paper: high costs, technical integration, and data quality. The review thereby presents the thought that robotics and AI will take a transformative role in the unconventional reservoir sector during the next few years, both in economic performance and environmental sustainability.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-30
DOI
https://doi.org/10.5281/zenodo.15051608
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Robotics and artificial intelligence in unconventional reservoirs: Enhancing efficiency and reducing environmental impact.

Zenodo (CERN European Organization for Nuclear Research)
Biomedical Text Mining and Ontologies
article

Robotics and artificial intelligence in unconventional reservoirs: Enhancing efficiency and reducing environmental impact.

article en

Abstract

Shale, tight gas, and coal bed methane reservoirs are unconventional reservoirs that have geometrically complicated geological formations with low permeability, rendering their extraction operations difficult, expensive, and environmentally sensitive. On the other hand, recent developments in Robotics and Artificial Intelligence continue to transform how exploration, production, and maintenance activities are carried out in these reservoirs. This review paper encompasses how Robotics and AI technologies are applied to these unconventional reservoirs, improving operation efficiency, increasing the recovery rate and minimizing environmental damage. It updates on the latest status of autonomous drilling, AI-driven reservoir characterization and robotic fracturing. The paper also discusses future opportunities: emerging technologies, integration of AI with predictive analytics, and innovations toward sustainability. These are the challenges that come with possible solutions discussed in the paper: high costs, technical integration, and data quality. The review thereby presents the thought that robotics and AI will take a transformative role in the unconventional reservoir sector during the next few years, both in economic performance and environmental sustainability.

Zenodo (CERN European Organization for Nuclear Research)
Nile University of Nigeria (NG), Petroleum Training Institute, Obafemi Awolowo University (NG)
Responsible consumption and production
Openalex Percentile: Top 58%
Biomedical Text Mining and Ontologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.