ArticleHeliyon2025
Geothermal potential of a petroleum mature field using machine learning methods: Ecuadorian Amazon region.
Article in Heliyon, 2025. 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Producing oil and extracting geothermal energy from the same reservoir is one type of the multigeneration systems that are garnering global attention. The aim of this research is to use machine learning methodologies to quantify the potential geothermal energy yield within a mature petroleum reservoir located in the Amazon region of Ecuador. The paper covers the complete geothermal simulation process of an area in the central-east region of Sacha Field. The study compares the performance of permeability calculation using the Kozeny and Carman algorithm in conjunction with Principal Component Analysis against permeability is calculated using the K-Nearest Neighbor density estimation algorithm. Historical matching is achieved using the Particle Swarm Optimization Algorithm, emphasizing a water production objective function. Additionally, the potential quantification compares the Volumetric method with Monte Carlo for the simulation of three scenarios until the year 2040. The results demonstrate excellent history matching using the Kozeny-Carman equation with Principal Component Analysis methodology. The historical production of water until the year 2009 was 2.625 million [std m3] and the simulation result was 2.757 million [std m3]. The percentage of error in the volume is 5.02% with the historical production approach. A small area in the central east region of the reservoir has the potential to generate a maximum power of 6.5 MWt based on the Volumetric methodology, which aligns with the average produced power of 0.66 MWt, depending on which of the three future scenarios up to 2040 is applied.
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.