Identification of biogenic gas reservoir zone using log, petrophysics and geochemical data in S-1 well of Nias basin, North Sumatera

Dewanto, Ordas; Maulina,; Nainggolan, Tumpal Bernhard

doi:10.1088/1742-6596/1572/1/012037

Cited by 1 publication

(1 citation statement)

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“…Similarly, the study in 16 explored a model to locate the optimum position of wells in an underground reservoir. In another study 17 , a well located in the Nias Basin (in the west of Sumatra) is studied using geochemical data. In addition, the study in 18 performed medium-term forecasting of salinity rates and groundwater levels using statistical and machine learning-based methods.…”

Section: Related Work and Gap In The Literaturementioning

confidence: 99%

A ubiquitous method for predicting underground petroleum deposits based on satellite data

Newaz

Tahmid

Al-Aboody

et al. 2023

Sci Rep

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The method of finding new petroleum deposits beneath the earth’s surface is always challenging for having low accuracy while simultaneously being highly expensive. As a remedy, this paper presents a novel way to predict the locations of petroleum deposits. Here, we focus on a region of the Middle East, Iraq to be specific, and conduct a detailed study on predicting locations of petroleum deposits there based on our proposed method. To do so, we develop a new method of predicting the location of a new petroleum deposit based on publicly available data sensed by an open satellite named Gravity Recovery and Climate Experiment (GRACE). Using GRACE data, we calculate the gravity gradient tensor of the earth over the region of Iraq and its surroundings. We use this calculated data to predict the locations of prospective petroleum deposits over the region of Iraq. In the process of our study for making the predictions, we leverage machine learning, graph-based analysis, and our newly-proposed OR-nAND method altogether. Our incremental improvement in the proposed methodologies enables us to predict 25 out of 26 existing petroleum deposits within the area under our study. Additionally, our method shows some prospective petroleum deposits that need to be explored physically in the future. It is worth mentioning that, as our study presents a generalized approach (demonstrated through investigating multiple datasets), we can apply it anywhere in the world beyond the area focused on in this study as an experimental case.

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Section: Related Work and Gap In The Literaturementioning

confidence: 99%