A novel exploration technique using the microbial fingerprint of shallow sediment to detect hydrocarbon microseepage and predict hydrocarbon charge — An Argentinian case study

Abstract

The fundamental assumption of many successful geochemical and geomicrobial technologies developed in the past 80 years is that hydrocarbons leak from subsurface accumulations vertically to the surface. Driven by buoyancy, the process involves sufficiently large volumes directly measurable or indirectly inferable from their surface expressions. Even when the additional hydrocarbons are not measurable, their presence slightly changes the environment where complex microbial communities live, and acts as an evolutionary constraint on their development. Because the ecology of this ecosystem is very complicated, we have used the full-microbiome analysis of the shallow sediments (soil and seabed) samples instead of targeting only a selected number of known species and the use of machine learning for uncovering meaningful correlations in these data. We achieved this by sequencing the microbial biomass in each sample and generating its “DNA fingerprint,” and by analyzing the abundance and distribution of the microbes over the data set. Our technology uses machine learning as a fast and accurate tool for determining the detailed interactions among the various microorganisms and their environment due to the presence or absence of hydrocarbons, thus overcoming data complexity. In a proof-of-technology study, we have taken more than a thousand samples in the Neuquén Basin in Argentina over three distinct areas, namely, an oil field, a gas field, and a dry location outside the basin, and created several successful predictive models. We kept a subset of randomly selected samples outside of the training set and asked the client operator to blind them, providing the means for objectively validating the prediction performance of this methodology. Uncovering the blinded data set after estimating the prospectivity for each sample revealed that we correctly predicted most of these samples. This very encouraging result indicates that analyzing the microbial ecosystem in shallow sediment can, with the appropriate training data, be an additional derisking method in assessing hydrocarbon prospects and improve the probability of success of a drilling campaign.