Evidence map›Paper›PMID 40251204›Full record

ArticleScientific reports2025

A robust pressure drop prediction model in vertical multiphase flow: a machine learning approach.

Fahd Saeed Alakbari, Mohammed Abdalla Ayoub, M A Awad, Tarek Ganat, Mysara Eissa Mohyaldinn, Syed Mohammad Mahmood

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Fahd Saeed AlakbariCentre of Advanced Process Safety (CAPS), Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia.
Mohammed Abdalla AyoubChemical & Petroleum Engineering Department, United Arab Emirates University, Al Ain, United Arab Emirates. ma.ayoub@uaeu.ac.ae.
M A AwadPetroleum Engineering Department, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia.
Tarek GanatDepartment of Petroleum Chemical Engineering, Sultan Qaboos University, Al-Khod, 123, P. O. Box 50, Muscat, Oman.
Mysara Eissa MohyaldinnPetroleum Engineering Department, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia.
Syed Mohammad MahmoodPetroleum Engineering Department, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia.

Funding

United Arab Emirates University G00004623Yayasan Universiti Teknologi PETRONAS (YUTP) 015LC0-451
6 · The paper itself

Abstract

Predicting pressure drop in multiphase flow is crucial for optimizing tubing size, wellhead pressure (WHP), completion design, cost management, and other production-phase objectives. While various correlations and models exist to calculate pressure drops, their accuracy diminishes significantly when applied to outlier datasets beyond their intended parameter ranges. Not only that, as reported by many studies, most of the empirical correlations and mechanistic models have an error, which is quite high and intolerable. This study introduces a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) model for accurately predicting pressure drops in vertical wells carrying multiphase fluids. A comprehensive dataset of 335 experimental records was compiled from diverse sources to encompass a wide range of parameters, ensuring the robustness of the proposed model. Key input parameters include WHP, oil rate, water rate, gas rate, inner diameter, surface temperature, and flow length. The ANFIS model was rigorously evaluated using different methods, such as cross plot, error distribution, Kruskal-Wallis (KW) test, error boxplot and violin graphs, Confidence Interval (CI), and statistical error metrics. The latter includes Average Absolute Percentage Error (AAPE), Root Mean Square Error (RMSE), and the coefficient of determination (R²) to prove the ANFIS model's performance. The proposed ANFIS model was compared with the most commonly used published models. Results demonstrate that the ANFIS model outperforms existing methods, achieving an AAPE of 2.92%, an RMSE of 1.9638%, and an R² of 0.9645. KW test, error boxplot, violin graphs, and CI results indicated that the best model for predicting pressure drops is the proposed ANFIS model. These findings establish the ANFIS model as a reliable and superior tool for predicting pressure drops in vertical wells with multiphase flow, offering significant advancements in design accuracy and operational efficiency.

Indexed as

Adaptive Neuro-Fuzzy inference system (ANFIS)Multiphase flowPressure drop in vertical wellsRobust pressure drop model.

Identifiers

PMID40251204
PMCPMC12008291

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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.