Mostafa Shahriari

Mostafa Shahriari
Software Competence Center Hagenberg | SCCH

Doctor of Philosophy

About

17
Publications
2,844
Reads
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88
Citations
Introduction
My research interest is to use advanced AI algorithms especially deep learning for engineering problems. I am also interested in the numerical solution of the partial differential equation such as finite element method. So far, my work has been mostly dedicated to the application in computational geophysics and petroleum engineering.
Additional affiliations
September 2019 - present
Software Competence Center Hagenberg
Position
  • Senior Researcher
February 2019 - September 2020
Euskampos Fundazioa
Position
  • PostDoc Position
November 2018 - May 2019
Basque Center for Applied Mathematics
Position
  • PostDoc Position
Education
November 2014 - November 2018
November 2011 - November 2013
Isfahan University of Technology
Field of study
  • Numerical Analysis

Publications

Publications (17)
Article
Full-text available
In some geological formations, borehole resistivity measurements can be simulated using a sequence of 1D models. By considering a 1D layered media, we can reduce the dimensionality of the problem from 3D to 1.5D via a Hankel transform. The resulting formulation is often solved via a semi-analytic method, mainly due to its high performance. However,...
Preprint
Full-text available
We use borehole resistivity measurements to map the electrical properties of the subsurface and to increase the productivity of a reservoir. When used for geosteering purposes, it becomes essential to invert them in real time. In this work, we explore the possibility of using Deep Neural Network (DNN) to perform a rapid inversion of borehole resist...
Preprint
Full-text available
Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. This work presents a deep neural network (DNN) model trained to reproduce the full set of extra-deep real-time EM logs consisting of 22 measurements per logging position. The model is trained in a 1D layered environment and has sensitivity...
Preprint
Full-text available
Deep learning (DL) can be employed as a numerical method to approximate functions. Recently, its use has become attractive for the simulation and inversion of multiple problems in computational mechanics, including the inversion of borehole logging measurements for oil and gas applications. In this context, DL methods exhibit two key attractive fea...
Article
Deep Neural Network (DNN)-based methods are suitable for the rapid inversion of borehole resistivity measurements. They approximate the forward and the inverse problem offline during the training phase and they only require a fraction of a second for the online evaluation (aka prediction). Herein, we propose a DNN-based iterative algorithm to desig...
Preprint
Full-text available
Deep neural networks (DNNs) offer a real-time solution for the inversion of borehole resistivity measurements to approximate forward and inverse operators. It is possible to use extremely large DNNs to approximate the operators, but it demands a considerable training time. Moreover, evaluating the network after training also requires a significant...
Article
Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We present a methodology to construct a deep neural network (DNN) model trained to reproduce a full set of extra-deep EM logs consisting of 22 measurements per logging position. The model is trained in a 1D layered environment consisting o...
Preprint
Full-text available
Borehole resistivity measurements recorded with logging-while-drilling (LWD) instruments are widely used for characterizing the earth's subsurface properties. They facilitate the extraction of natural resources such as oil and gas. LWD instruments require real-time inversions of electromagnetic measurements to estimate the electrical properties of...
Article
Full-text available
When simulating borehole resistivity measurements in a reservoir, it is common to consider an oil-water contact (OWC) planar interface. However, this consideration can lead to an unrealistic model since in the presence of capillary actions, the mix of two immiscible fluids (oil and water) often appears as an oil-water transition (OWT) zone. These t...
Article
Full-text available
Borehole resistivity measurements are routinely employed to measure the electrical properties of rocks penetrated by a well and to quantify the hydrocarbon pore volume of a reservoir. Depending on the degree of geometrical complexity, inversion techniques are often used to estimate layer-by-layer electrical properties from measurements. When used f...
Article
Full-text available
A transformation of unimodal multivariate data is introduced for increased precision in the estimation of the exponential decay type of the underlying density. The transformation renders the contour lines of the probability density function more uniformly spherical and enables conservation of unimodality, without assuming the ability to efficiently...
Article
Full-text available
Inverse problems appear in multiple industrial applications. Solving such inverse problems require the repeated solution of the forward problem. This is the most time-consuming stage when employing inversion techniques, and it constitutes a severe limitation when the inversion needs to be performed in real-time. In here, we focus on the real-time i...
Preprint
Full-text available
Inverse problems appear in multiple industrial applications. Solving such inverse problems require the repeated solution of the forward problem. This is the most time-consuming stage when employing inversion techniques, and it constitutes a severe limitation when the inversion needs to be performed in real-time. In here, we focus on the real-time i...
Article
Full-text available
In inverse geophysical resistivity problems, it is common to optimize for specific resistivity values and bed boundary positions, as needed, for example, in geosteering applications. When using gradient-based inversion methods such as Gauss-Newton, we need to estimate the derivatives of the recorded measurements with respect to the inversion parame...
Article
Full-text available
In this paper, we develop some mesh-free particle methods for magneto-hydrodynamics equations. Our focus is on the problems with high Hartmann numbers, which generate unstable solutions in most numerical methods. Several numerical tests validate the efficiency of our methods. Copyright © 2015 John Wiley & Sons, Ltd.

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Projects

Projects (3)
Project
The key objectives of the Group on Applied Mathematical Modeling, Statistics, and Optimization (MATHMODE) are: 1) Develop knowledge on the numerical simulation of ordinary and partial differential equations as well as on optimization problems and statistics. 2) Transfer this mathematical knowledge to the industry. 3) Train new researchers in the area.
Project
The main objective of this Marie Curie RISE Action is to improve and exchange interdisciplinary knowledge on applied mathematics, high performance computing, and geophysics to be able to better simulate and understand the materials composing the Earth's subsurface. This is essential for a variety of applications such as CO2 storage, hydrocarbon extraction, mining, and geothermal energy production, among others. All these problems have in common the need to obtain an accurate characterization of the Earth's subsurface, and to achieve this goal, several complementary areas will be studied, including the mathematical foundations of various high-order Galerkin multiphysics simulation methods, the efficient computer implementation of these methods in large parallel machines and GPUs, and some crucial geophysical aspects such as the design of measurement acquisition systems in different scenarios. Results will be widely disseminated through publications, workshops, post-graduate courses to train new researchers, a dedicated webpage, and visits to companies working in the area. In that way, we will perform an important role in technology transfer between the most advanced numerical methods and mathematics of the moment and the area of applied geophysics.
Project
The detection, diagnosis, and prediction of anomalies in process data comprises the conventional predictive maintenance program used in production environments. In this project, state of the art machine learning methods are to be equipped with an interpretation in the framework of a physical or probabilistic interpretable 'base-model' allowing for optimised process parameter tuning. The goal is to provide qualitative information about the process from the quantitative corrections supplied by the predictive process analysis.