Insight into the pseudo elastic moduli of geomaterials

Mechanical behaviour of clastic formations at shallow – medium depths which bear hydrocarbon reservoirs could exhibit an important non-linear influence of the strain on the formation stiffness during depletion. Particularly in the early reservoir production stage, characterized by high uncertainty and little ground movement data for back-analysis, reliable determination of formation stiffness at very small strain and its degradation with increasing strain via experimental testing can play a key role in realistic subsidence predictions. The standard set of data acquisition by the oil industry represents a good starting point, but the information must still be corroborated and extended by dedicated lab tests analysis. The scope of this paper is to review a selection of the most used in situ data acquisition as well as laboratory techniques for the determination of the formation stiffness at (very) small strains and its non-linear degradation with increasing strain.

Mechanical behaviour of clastic formations at shallow – medium depths which bear hydrocarbon reservoirs could exhibit an important non-linear influence of the strain on the formation stiffness during depletion. Particularly in the early reservoir production stage, characterized by high uncertainty and little ground movement data for back-analysis, reliable determination of formation stiffness at very small strain and its degradation with increasing strain via experimental testing can play a key role in realistic subsidence predictions. The standard set of data acquisition by the oil industry represents a good starting point, but the information must still be corroborated and extended by dedicated lab tests analysis. The scope of this paper is to review a selection of the most used in situ data acquisition as well as laboratory techniques for the determination of the formation stiffness at (very) small strains and its non-linear degradation with increasing strain.


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

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