ReviewiScience2026
AI-driven and advanced sensing technologies for subsurface oil exploration.
Review in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Subsurface oil exploration is becoming increasingly challenging due to the complexity of geological formations, growing exploration depths, and the limitations of conventional interpretation methods. A thorough analysis of current developments in artificial intelligence and sophisticated sensing technologies for subsurface oil prospecting is presented. With special emphasis on their uses, advantages, and implementation difficulties, this paper assesses advancements in seismic imaging, electromagnetic surveys, remote sensing, machine learning, and integrated data interpretation frameworks. The results show that while advanced sensing technologies offer higher-resolution subsurface information and lower exploration uncertainty, artificial intelligence greatly increases exploration efficiency by enhancing data processing, pattern recognition, reservoir characterization, and decision-making. Nonetheless, there are lots of issues with data quality, model dependability, scalability, and field deployment. The review offers a thorough reference for researchers and industry practitioners looking to increase exploration accuracy, efficiency, and sustainability. It also emphasizes future research possibilities for intelligent, integrated exploration systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.