Preliminary statistical analysis of borehole and geological data from the Po plain

The Po Plain area in the north of Italy can be considered a natural geological and geophysical laboratory due to its complex geological evolution, particularly from the Miocene to today. Much of our understanding about the subsurface of the Po Plain is due to the large amount of data collected during the period of hydrocarbon exploration in Italy. In total more than 7000 wells have
been drilled and thousands of km of seismic acquisition lines have been acquired. Furthermore, the study of the natural gas fields contributed with additional data facilitating the creation of detailed structural and stratigraphic models of the subsurface. The majority of the “original” data, including well logs, seismic and geological profiles existed in paper format thus posing challenges for their integration into modern models where digital data are incorporated to achieve a sound
description of the subsoil. Livani et al. (2023) have collected and digitized a large number of “original” data and subsequently used them to recreate the overall subsurface architecture of the Po plain and extract the physical properties of the main geological units. In this study, we use the results of the work of Livani et al. and we perform a preliminary statistical analysis on them. We explore relationships between rock density and geological formations, we compare log data (GR, sonic) with lithologies and we investigate the lithological content for each of geological formations. Ultimately, we compare some of our results with previously published research. The Po Plain area in the north of Italy can be considered a natural geological and geophysical laboratory due to its complex geological evolution, particularly from the Miocene to today. Much of our understanding about the subsurface of the Po Plain is due to the large amount of data collected during the period of hydrocarbon exploration in Italy. In total more than 7000 wells have
been drilled and thousands of km of seismic acquisition lines have been acquired. Furthermore, the study of the natural gas fields contributed with additional data facilitating the creation of detailed structural and stratigraphic models of the subsurface. The majority of the “original” data, including well logs, seismic and geological profiles existed in paper format thus posing challenges for their integration into modern models where digital data are incorporated to achieve a sound
description of the subsoil. Livani et al. (2023) have collected and digitized a large number of “original” data and subsequently used them to recreate the overall subsurface architecture of the Po plain and extract the physical properties of the main geological units. In this study, we use the results of the work of Livani et al. and we perform a preliminary statistical analysis on them. We explore relationships between rock density and geological formations, we compare log data (GR, sonic) with lithologies and we investigate the lithological content for each of geological formations. Ultimately, we compare some of our results with previously published research.

ISSN 1121-9041

CiteScore:
2020: 3.8
CiteScore measures the average citations received per peer-reviewed document published in this title.
CiteScore values are based on citation counts in a range of four years (e.g. 2016-2019) to peer-reviewed documents (articles, reviews, conference papers, data papers and book chapters) published in the same four calendar years, divided by the number of these documents in these same four years (e.g. 2016 —19).
Source Normalized Impact per Paper (SNIP):
2019: 1.307
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR)
2019: o.657
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal's impact.
Journal Metrics: CiteScore: 1.0 , Source Normalized Impact per Paper (SNIP): 0.381 SCImago Journal Rank (SJR): 0.163

Supported by


Edited by


GEAM - Associazione Georisorse e Ambiente c/o Dipartimento di Ing.dell’Ambiente, del Territorio e delle infrastrutture Politecnico di Torino
Copyright @ GEAM - Designed by DESIGN GANG - Privacy Policy