Sergey Alyaev

NORCE · Energy

The complexity of geomodelling workflows is a limiting factor for quantifying and updating uncertainty in real-time during drilling. We propose Generative Adversarial Networks (GANs) for parametrization and generation of geomodels, combined with Ensemble Randomized Maximum Likelihood (EnRML) for rapid updating of subsurface uncertainty. This real-t...

The rate of penetration (ROP) is a key performance indicator in the oil and gas drilling industry as it directly translates to cost savings and emission reductions. A prerequisite for a drilling optimization algorithm is a predictive model that provides expected ROP values in response to surface drilling parameters and formation properties. The hig...

The advent of fast sensing technologies allows for real-time model updates in many applications where the model parameters are uncertain. Bayesian algorithms, such as ensemble smoothers, offer a real-time probabilistic inversion accounting for uncertainties. However, they rely on the repeated evaluation of the computational models, and deep neural...

The advent of fast sensing technologies allow for real-time model updates in many applications where the model parameters are uncertain. Once the observations are collected, Bayesian algorithms offer a pathway for real-time inversion (a.k.a. model parameters/inputs update) because of the flexibility of the Bayesian framework against non-uniqueness...

Hydrocarbon reservoirs are often located in spatially complex and uncertain geological environments, where the associated costs of drilling wells for exploration and development are notoriously high. These costs may be reduced with an optimized well-placement strategy based on real-time geological information, known as well geosteering. To effectiv...

Geosteering of wells requires fast interpretation of geophysical logs which is a non-unique inverse problem. Current work presents a proof-of-concept approach to multi-modal probabilistic inversion of logs using a single evaluation of an artificial deep neural network (DNN). A mixture density DNN (MDN) is trained using the "multiple-trajectory-pred...

During drilling, to maximize future expected production of hydrocarbon resources, the experts commonly adjust the trajectory (geosteer) in response to new insights obtained through real-time measurements. Geosteering workflows are increasingly based on the quantification of subsurface uncertainties during real-time operations. As a consequence, ope...

Quantitative workflows utilizing real-time data to constrain uncertainty have the potential to significantly improve geosteering. Fast updates based on real-time data are particularly important when drilling in complex reservoirs with high uncertainties in pre-drill models. However, practical assimilation of real-time data requires effective geolog...

During a geosteering operation the well path is intentionally adjusted in response to the new data acquired while drilling. To achieve consistent high-quality decisions, especially when drilling in complex environments, decision support systems can help cope with high volumes of data and interpretation complexities. They can assimilate the real-tim...

The cost of drilling wells on the Norwegian Continental Shelf are extremely high, and hydrocarbon reservoirs are often located in spatially complex rock formations. Optimized well placement with real-time geosteering is crucial to efficiently produce from such reservoirs and reduce exploration and development costs. Geosteering is commonly assisted...

During a geosteering operation the well path is intentionally adjusted in response to the new data acquired while drilling. To achieve consistent high-quality decisions, especially when drilling in complex environments, decision support systems can help cope with high volumes of data and interpretation complexities. They can assimilate the real-tim...

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...

The costs for drilling offshore wells are high and hydrocarbons are often located in complex reservoir formations. To effectively produce from such reservoirs and reduce costs, optimized well placement in real-time (geosteering) is crucial. Geosteering is usually assisted by an updated formation evaluation obtained by well-log interpretation while...

Deviant geometry of nasal channels results in significant changes to nasal aerodynamics that alter flow resistance, sensation, and the ability to filter aerosols. The invasive, operative modification of nasal geometry might alter the intranasal flow patterns, and this must be considered in advance at the stage of surgical planning. The present work...

Geosteering workflows are increasingly based on the quantification of subsurface uncertainties during real-time operations. As a consequence operational decision making is becoming both better informed and more complex. This paper presents an experimental web-based decision support system, which can be used to both aid expert decisions under uncert...

The direct photothermal boiling in suspensions of nano- and microscopic particles finds multiple applications in concentrated solar power: turbine-based combined heat and power (CHP) generation, solar distillation, energy storage, and chemical synthesis. However, the most promising application is solar desalination. There have been multiple studies...

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...

With the steady growth of the amount of real-time data while drilling, operational decision-making is becoming both better informed and more complex. Therefore, as no human brain has the capacity to interpret and integrate all decision-relevant information from the data, the adoption of advanced algorithms is required not only for data interpretati...

Geosteering is a sequential decision process under uncertainty. The goal of geosteering is to maximize the expected value of the well, which should be defined by an objective value-function for each operation. In this paper we present a real-time decision support system (DSS) for geosteering that aims to approximate the uncertainty in the geologic...

Real-time signals exchanged during a drilling operation are in constant evolution and provided by multiple stakeholders that have distinct perspectives on the drilling process. To achieve an improved drilling management and control, it is desirable that real-time drilling data can be exchanged seamlessly without necessitating any human intervention...

Geosteering is a sequential decision process under uncertainty. The goal of geosteering is to maximize the expected value of the well, which should be defined by an objective value-function for each operation. In this paper we present a real-time decision support system (DSS) for geosteering that aims to approximate the uncertainty with an ensemble...

Upscaling of flow from pore to Darcy scale is a long-standing research field within flow in porous media. It is well known that non-linearities can occur in near-well regions and high-porosity or fractured media. At the same time, the upscaled non-linear effects associated with high flow rates are hard to quantify a priori with single-scale models....

We present a systematic geosteering workflow for decision support under uncertainty. The uncertainty is captured in an earth model represented as an ensemble of realizations of the geology. The realizations are updated each time new real-time measurements become available. Thereafter, the up-to-date ensemble of realizations is used as input for dec...

The aim of geosteering is to place a well optimally in the reservoir, based on the available measurements and interpretations of the geology. The interpretations are captured in an earth model that is used for decision support. However, real-time decision processes such as geosteering are poorly supported by current earth modelling methods. The geo...

In this work we present a systematic geosteering workflow that automatically integrates a priori information and the real-time measurements for updating of geomodel with uncertainties, and uses the latest model predictions in a Decision Support System (DSS). The DSS supports geosteering decisions by evaluating production potential versus drilling a...

The process of initial ice formation in brine is a highly complex problem. In this paper, we propose a mathematical model that captures the dynamics of nucleation and development of ice inclusions in brine. The primary emphasis is on the interaction between ice growth and salt diffusion, subject to external forcing provided by temperature. Within t...

Traditionally, reactive measures have been used to prevent drilling problems. This included monitoring on the rig and attempting to resolve situations as they occurred. During the last decades, pre-drill modelling techniques were developed to estimate pore pressure and formation stresses, thus mitigating drilling risks. To make the best use of pred...

Proactive geosteering workflows include a number of steps that are repeated as the drilling operation progresses. First, various measurements such as gamma ray, gravity or electromagnetic (EM) data are collected while drilling. Second, the recorded data samples are processed and used to update the geomodel, including parameters around the well rele...

The important task of managing the drilling fluid during drilling operations is traditionally performed by highly qualified mud engineers at the rig site, following the constraints of the drilling fluid program, designed prior to commencing drilling operations. Due to uncertainty in planning data, the fluids specification may in many cases be unsui...

The standard approximation for the flow-pressure relationship in porous media is Darcy's law that was originally derived for infiltration of water in fine homogeneous sands. Ever since there have been numerous attempts to generalize it for handling more complex flows. Those include upscaling of standard continuum mechanics flow equations from the f...

The formation of capillary bridge formed by a liquid adsorbate is one of the main reasons for agglomeration in multiphase flows. Agglomeration takes place when the relative momentum of two colliding particles is fully consumed by the bridge. This article presents a theoretical study of the collisions of particles with adsorbed liquid taking into ac...