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Seyedmohammadvahid Mousavi

Bio: Seyedmohammadvahid Mousavi is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Support vector machine & Least squares support vector machine. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Papers
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Journal ArticleDOI
TL;DR: Analysis indicates that the MELM-PSO model provides the highest μc prediction accuracy achieving a root mean squared error (RMSE) of 0.0035 cP and a coefficient of determination (R2) for a dataset of 2269 data records compiled from gas-condensate fields around the world.

24 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper , empirical equations along with machine learning methods, namely random forest algorithm, support vector regression (SVR), artificial neural network (ANN) algorithm, and decision tree (DT) algorithm were employed for PP prediction applying well log data.

16 citations

Journal ArticleDOI
TL;DR: In this paper , the authors combined analytic equations with intelligent algorithms (IAs) in an integrated workflow for estimating the pore pressure (PP) from petrophysical logs at offset wells.

15 citations

Journal ArticleDOI
01 Oct 2022-Fuel
TL;DR: In this article , the authors developed and applied hybrid ML-optimizer models to a large, high-resolution, dataset (10 petrophysical variables; 3395 data records; ∼12% of the records displaying fractures) from the Asmari fractured carbonate reservoir in Iran's Marun oil and gas field.

14 citations

Journal ArticleDOI
TL;DR: In this article , the authors used hybrid machine learning (HML) and deep learning (DL) algorithms for predicting shear wave velocity (V S ) from sedimentary rock sequences.
Abstract: Abstract Shear wave velocity ( V S ) data from sedimentary rock sequences is a prerequisite for implementing most mathematical models of petroleum engineering geomechanics. Extracting such data by analyzing finite reservoir rock cores is very costly and limited. The high cost of sonic dipole advanced wellbore logging service and its implementation in a few wells of a field has placed many limitations on geomechanical modeling. On the other hand, shear wave velocity V S tends to be nonlinearly related to many of its influencing variables, making empirical correlations unreliable for its prediction. Hybrid machine learning (HML) algorithms are well suited to improving predictions of such variables. Recent advances in deep learning (DL) algorithms suggest that they too should be useful for predicting V S for large gas and oil field datasets but this has yet to be verified. In this study, 6622 data records from two wells in the giant Iranian Marun oil field (MN#163 and MN#225) are used to train HML and DL algorithms. 2072 independent data records from another well (MN#179) are used to verify the V S prediction performance based on eight well-log-derived influencing variables. Input variables are standard full-set recorded parameters in conventional oil and gas well logging data available in most older wells. DL predicts V S for the supervised validation subset with a root mean squared error (RMSE) of 0.055 km/s and coefficient of determination (R 2 ) of 0.9729. It achieves similar prediction accuracy when applied to an unseen dataset. By comparing the V S prediction performance results, it is apparent that the DL convolutional neural network model slightly outperforms the HML algorithms tested. Both DL and HLM models substantially outperform five commonly used empirical relationships for calculating V S from V p relationships when applied to the Marun Field dataset. Concerns regarding the model's integrity and reproducibility were also addressed by evaluating it on data from another well in the field. The findings of this study can lead to the development of knowledge of production patterns and sustainability of oil reservoirs and the prevention of enormous damage related to geomechanics through a better understanding of wellbore instability and casing collapse problems. Graphical abstract

13 citations

Journal ArticleDOI
TL;DR: In this article , the most influential set of input features are developed to predict pore pressure, including rate of penetration (ROP), deep resistivity (ILD), density (RHOB), photoelectric index (PEF), corrected gamma ray (CGR), compression-wave velocity (Vp), weight on bit (WOB), shear-wave velocities (Vs) and pore compressibility (Cp).

12 citations