Reza Yousefzadeh

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Reza verified their affiliation via an institutional email.

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• Doctor of Philosophy
• Researcher at Amirkabir University of Technology

Choosing a representative subset of realizations can reduce significantly the number of simulations and the computational cost associated with optimization under geological uncertainty. Methods that use dynamic criteria, such as full flow simulators, can choose effectively representative realizations and reduce the number of simulations during opti...

Optimization of the placement and operational conditions of oil wells plays an important role in the development of the oilfields. Several automatic optimization algorithms have been used by different authors in recent years. However, different optimizers give different results depending on the nature of the problem. In the current study, a compari...

Well placement is one of the challenging steps in field development optimization. Finding optimal locations with fewer function evaluations and less time is crucial when dealing with giant models. In this study, we propose a method based on the combination of Fast Marching Method and Particle Swarm Optimization to decrease the number of function ev...

One of the challenging issues during underbalanced drilling (UBD) is the prediction of frictional loss in the presence of three-phases of drilling fluid, cuttings and air. In the current work, three innovative computer-based algorithms based on Gradient tree boosting (GTB), Adaptive neuro-fuzzy inference system (ANFIS), and Extreme learning machine...

One of the main challenges in screening of enhanced oil recovery (EOR) techniques is the class imbalance problem, where the number of different EOR techniques is not equal. This problem hinders the generalization of the data-driven methods used to predict suitable EOR techniques for candidate reservoirs. The main purpose of this paper is to propose...

Chemicals transfer from the packaging materials and their dissolution in food and water can create health risks. Due to the costly and time-intensive nature of experimental measurements, employing artificial intelligence (AI) methodologies is beneficial. This research uses five renowned AI-based techniques (namely, long short-term memory, gradient...

This study assesses seven CO2-type Enhanced Oil Recovery (EOR) methods in a stratified dipping reservoir, including CO2 injection and combinations. It aims to compare their effectiveness in terms of recovery factor, water-cut, and carbon storage capacity using reservoir simulation. The scope encompasses evaluating the efficacy of these techniques f...

Optimal placement of wells is a time-consuming task, which is further made problematic when dealing with geological uncertainties. Although many researchers have attempted to address the intricacies of the problem, they have all taken the single-time optimization of all wells, while the wells are drilled sequentially in real-life fields. This paper...

This paper compares the performance of two data-driven methods, Signal-Matching Predictor (SMP) and Long Short-Term Memory (LSTM), for predicting drilling strength (Es) ahead of the bit based on drilling data from nearby offset wells. The comparison is based on the accuracy, applicability, complexity, and computational cost of the methods with the...

The large number of geological realizations and well trajectory parameters make field development optimization under geological uncertainty a time-consuming task. A novel deep learning-based surrogate model with a novel well trajectory parametrization technique is proposed in this study to optimize the trajectory of wells under geological uncertain...

Capillary/residual CO2 trapping is one of the main mechanisms of CO2 storage in underground formations. Therefore, it is required to estimate the brine/CO2 interfacial tension under different conditions. Although many methods have been proposed so far, the error of estimation is still high. This paper proposes a novel deep learning method to estima...

Accurate estimation of CO2-brine Interfacial Tension (IFT) is of great importance since it is required to model the residual/capillary trapping of CO2 in geological formations. In this paper, a novel correlation is proposed based on the classical orthogonal polynomials to estimate the CO2-brine IFT of monovalent salts with common anion. To do so, C...

The released carbon monoxide (CO) into the atmosphere is a threat to human life and environmental safety. CO storage in surface and underground seawater/water may be viewed as a potential scenario to decrease the concentration of this dangerous gas in the atmosphere. A reliable tool to calculate CO solubility in aqueous media is a prerequisite for...

Optimizing the placement of wells is a crucial step in field development as it directly impacts production and cost. Inappropriate well placement can lead to decreased production and higher costs due to the expensive drilling process. Vertical well placement focuses on optimizing wellhead coordinates (x and y parameters), while horizontal and devia...

This paper proposes a novel parametrization technique for deviated and horizontal wells and investigates the potential of combining the Fast Marching Method (FMM) with Particle Swarm Optimization (PSO) to facilitate optimization of the trajectory of vertical, deviated, and horizontal wells. The parametrization technique is based on practical drilli...

Most of the geological parametrization techniques used in history matching of sub-surface formations including the deep learning-based methods could not capture the non-linear and non-Gaussian and were limited to facies realizations. This paper proposes a novel method for parametrization of permeability realizations using ConvLSTM layers to build a...

Geo-cellar models contain millions of gridblocks and are very time-consuming to simulate. This challenge necessitates upscaling geo-cellular models to obtain fit-for-purpose models for simulation. Simulation-based upscaling methods are computationally demanding, especially if there is a large number of geological realizations. On the other hand, st...

Due to the importance of the compressive strength of masonry (CSM) prisms made of clay bricks and cement mortar in the design of masonry structures, it is crucial to develop a reliable method for accurate prediction. This paper presents an innovative approach for estimating CSM by integrating an optimized convolutional neural network (OCNN), extrem...

Almost all activities in real life entail different kinds of uncertainty. From daily decisions to complicated problems, such as petroleum reservoir characterization, suffer from uncertainties. Uncertainty can have different roots, including incomplete observation of the system, incomplete modeling of the system because of our limited knowledge and...

As mentioned earlier, one of the challenges in history matching and field development optimization under geological uncertainty is the high computational cost of the process. The majority of the computational burden is associated with numerous reservoir simulations required to calculate the misfit/objective function over a large number of realizati...

Whenever there are observed dynamic data obtained from the reservoir understudy, we can reduce the geological uncertainty by conditioning the prior geological realizations to the observed data (Oliver and Chen in Computational Geosciences. 15:185–221, 2010; Ghoniem et al. in Applied Mathematical Modelling. 8:282–287, 1984; Heidari et al. in Compute...

Our knowledge from underground reservoirs is not complete and is limited to some sparse core and log data, seismic data, geological interpretations, etc. This limited knowledge leads to a significant extend of uncertainty that is common in reservoir modeling and characterization. This kind of uncertainty is known as the geological uncertainty since...

Decision making about field development plans has to consider the inherent uncertainties of sub-surface hydrocarbon reservoirs; therefore, the decisions would be stable under different geological scenarios. As described in Sect. 1.5.1, the aim of this kind of uncertainty management is to propagate the uncertainty from inputs to the outputs. Therefo...

As discussed in Sect. 3.4, one of the challenges in history matching is the high dimensionality (large number of model parameters) of the reservoir model realizations that raises two challenges: 1- more data assimilation iterations are required to get a satisfactory match, 2- preserving the geologic realism becomes harder as there are an infinite n...

This book explores methods for managing uncertainty in reservoir characterization and optimization. It covers the fundamentals, challenges, and solutions to tackle the challenges made by geological uncertainty. The first chapter discusses types and sources of uncertainty and the challenges in different phases of reservoir management, along with gen...

Scale precipitation in petroleum equipment is known as an important problem that causes damages in injection and production wells. Scale precipitation causes equipment corrosion and flow restriction and consequently a reduction in oil production. Due to this fact, the prediction of scale precipitation has vital importance among petroleum engineers....

There have been several attempts to reduce the computational costs associated with well placement optimization under geological uncertainty by selecting a representative subset of realizations or by reducing the number of function evaluations to speed up the optimization. Although previous researchers have studied this issue, they have used static...

One of the most complex problems in the field of upstream oil and gas industry is to optimally determine the location of production and injection wells. To do so, a variety of tools have been employed by reservoir engineers, including simplified reservoir models, reservoir quality maps, and automatic optimization techniques. Although the use of aut...

The purpose of this work is to propose a dynamic approach to select a representative subset of realizations to reduce the number of function evaluations in the injection well optimization under geologic uncertainty. The suitable subset of realizations should capture the range of uncertainty in the fullset. A realization is selected if its dynamic c...

Well placement optimization is an important step in field development plans. Optimum location of injection wells in a waterflood project ought to be around the drainage boundary of production wells. Therefore, restricting the optimization algorithm domain to the drainage boundary of wells can reduce the number of function evaluations and as a resul...

Estimating the volume of the leaked fluid into the formation in a specific time is one of the challenging subjects in petroleum industry. Different static and dynamic models have been proposed. Standard API method proposed by American Petroleum Institute is one of the methods to estimate leak-off coefficient of porous media and mud properties. This...

Estimating the amount of fluid leakage into a formation is one of the important issues in petroleum industry. Lack of knowledge about this amount will affect the production overview (plan) negatively. Different static and dynamic models have been proposed to estimate leakage. Standard API method has been proposed to estimate leak-off coefficient of...

Leakage of drilling fluid into formation is one of the undeniable facts in drilling which results in some problems, however infiltration of some of the fluid is necessary to form a thin impermeable layer of solid materials called filter cake in order to stop more leakage into the formation. Hence, calculation of the leak-off coefficient plays a pro...

Leakage of drilling fluid into formation is one of the undeniable facts in drilling which results in some problems, however infiltration of some of the fluid is necessary to form a thin impermeable layer of solid materials called filter cake in order to stop more leakage into the formation. Hence, calculation of the leak-off coefficient plays a pro...