Evidence map›Paper›PMID 37519676›Full record

ArticleHeliyon2023

Deep dive into net pay layers: An in-depth study in Abadan Plain, South Iran.

Ahmad Azadivash, Mehdi Shabani, Vali Mehdipour, Ahmadreza Rabbani

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ahmad AzadivashDepartment of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran.
Mehdi ShabaniDepartment of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran.
Vali MehdipourDepartment of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran.
Ahmadreza RabbaniDepartment of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study focuses on the intricate process of discerning productive layers within reservoir formations, taking the Sarvak Formation as a primary case. The employed methodology combines geology, comprehensive log interpretations, and petrophysical analyses to facilitate the evaluation of these productive layers. Initially, routine well logs are interpreted to determine key petrophysical parameters such as shale content, porosity, and water saturation. Subsequently, core measurement results are utilized for calibrating these log interpretations. The study further determines cut-off values through a calculated method of 5% cumulative hydrocarbon volume against porosity, shale content, and water saturation. These cut-off values are then applied to the petrophysical results to enhance their reliability. To resolve any inconsistencies or uncertainties in petrophysical evaluation, petrographical analyses, including scanning electron microscope imaging and thin section studies, are employed. The Sarvak Formation is categorized into seven distinct subzones, each thoroughly investigated to ascertain their respective productivity potential. The final results illustrate a substantial heterogeneity within the Sarvak Formation, revealing a range of diagenetic processes including compaction, dissolution, and cementation. Despite this complexity, three subzones are identified as the most productive layers with the maximum net pay, demonstrating the efficacy of the integrated approach.

Indexed as

Integrative approachNet payPetrographical studyPetrophysical evaluationReservoir qualitySarvak formation

Identifiers

PMID37519676
PMCPMC10372216

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.