What is it about?
This article describes work done on using microbes in the shallow subsurface to prove hydrocarbon presence at (large) depths. This is done using DNA sequencing and machine learning models, where the causality is a process called microseepage. Microseepage is the fast an nearly vertical transport, or leakage, of tiny amount of hydrocarbons directly above an oil or gas field. Our study shows the result of using the full microbiome, where all microbes present in a sediment sample are analysed, of a teaspoon of soil taken from one foot depth. In this study over 1400 samples are taken in the Neuquén Basin in Argentina, where the technology is proven by means of blinding in a subset of the samples. Uncovering this blinded dataset after estimating the prospectivity revealed that most of these samples were correctly predicted.
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Why is it important?
This article is important as it is a proof of technology of using 'old' surface geochemical principles in a new (geomicrobial) way. Unique in this approach is using the full microbiome instead of only a selection. Because tens of thousands microbes are present, the predictability greatly increases. Furthermore thevery encouraging result shows that analyzing the microbial ecosystem in the shallow sediment can be an additional de-risking method for assessing hydrocarbon prospects and improving the Probability Of Success(POS) of a drilling campaign. This can lead to less drilling, which means less impact on the environment as well as saving cost.
Read the Original
This page is a summary of: A novel exploration technique using the microbial fingerprint of shallow sediment to detect hydrocarbon microseepage and predict hydrocarbon charge — An Argentinian case study, Interpretation, November 2021, Society of Exploration Geophysicists, DOI: 10.1190/int-2021-0068.1.
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Predicting Sweet Spots in Shale Plays by DNA Fingerprinting and Machine Learning
Paper describing how DNA sequencing and microbome analysis combined with machine learning can be used to locate production sweet spots in shale plays.
Eagle Ford and Bakken Productivity Prediction Using Soil Microbial Fingerprinting and Machine Learning
Article describes a pilot project where the hydrocarbon production potential of locations in the Eagle Ford Shale (Texas) and Bakken Shale (North Dakota) were predicted. This was done using DNA sequencing of shallow soil samples, combined with machine learning to process the vast amount of data resulting in the analysis. The method was proven by blinding a subset of the samples.
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