bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using DNA-Logging to Determine Inflow Profile in Horizontal Wells
Authors: Posdyshev Arsenii Stanislavovich , Shelyakin Pavel Vladimirovich , Shaikhutdinov Nurislam Maratovich , Popov Aleksey Alekseevich , Logacheva Maria Dmitrievna , Tutukina Maria Nikolaevna , Gelfand Mikhail Sergeevich 
Published: October 2021

Abstract
The purpose of this work is to adapt and apply Next Generation Sequencing methods in oil and gas well field studies. Relatively recent NGS methods provide a description of a geological formation by analyzing millions of DNA sequences and represent an entirely new way to obtain information about oil and gas reservoirs and the composition of their fluids, which could significantly change the approach to exploration and field development.
We present the results of pilot work to determine the inflow profile in a horizontal well based on DNA markers. The technology is based on the comparison of bacterial DNA from drill cuttings obtained while drilling with DNA from microorganisms of fluids obtained during production at the wellhead. Because of their high selectivity, individual microbes live only under certain conditions (salinity, oil saturation, temperature) and can be used as unique natural biomarkers. The comparison of DNA samples of drilling cutting and produced fluid allows for identification of the segment of the horizontal well from which the main flow comes, as well as identifying the type of incoming fluid (water, oil, gas) without stopping the operation process and without conducting expensive downhole operations.
As a result of these studies, the microbial communities of the oil-bearing sands and formation fluids of the Cretaceous deposits (group BS) in Western Siberia were identified, and the relative numerical ratio of microorganisms in the formations was determined. It was shown that the microbiome diversity changes with depth, and depends on the lithological composition, and sequencing data obtained from cuttings samples correlate with data from wellhead samples of produced fluid. Thus, the practical applicability of DNA sequencing for solving field problems in oil and gas field development, in particular for determining the inflow profile in horizontal wells, was confirmed.
Restoration of Seismic Data Using Inpainting and EdgeConnect
Authors: Radosavljevic Marko , Naugolnov Mikhail , Bozic Milos , Sukhanov Roman 
Published: October 2021

Abstract
Missing seismic data is largely present problem in the world. Lack of seismic data usually occurs due to some form of natural obstacle or legislative prohibitions of seismic exploration. Restoration of seismic data would allow locating of new oil traps and reduce the risk of unsuccessful drillings. The approach is based on deep learning (image inpainting) techniques, which will be applied on inline and crossline sections of a given 3-d seismic cube, in order to restore missing parts of sections. The study was provided for non-commercial purpose for the aims of scientific research. Data used in our experiments comes from open source typical Western Siberia field. Our approach uses Generative Adversarial Networks (GANs) for completing missing parts of images (sections), based on known parts. Method can be used for restoration of arbitrarily-shaped missing parts of seismic cube, but also for extrapolation purposes. Metrics used for model evaluation are correlation coefficient and mean absolute percentage error (MAPE) between original and inpainted parts of data.
This paper applies modern approach from growing image inpainting field to restore missing data, even if it's irregularly-shaped and very large. Using very powerful GANs is what gives this model ability to learn difficult inpainting scenarios, but also implicates challenging and time-consuming training process. Accurate estimation of model performances in different scenarios provides an exact instruction manual for a geologist, which helps him to identify cases where our model should be applied.
Digital Slot: A Tool for Optimization and Development of New Hydraulic Fracturing Technologies
Authors: Idimeshev Semen Vasilievich , Isaev Vadim Ismailovich , Tikhonov Alexey Alexandrovich , Semin Leonid Georgievich , Bannikov Denis Viktorovich , Velikanov Ivan Vladimirovich , Ivanov Maxim Grigorievich , Kuznetsov Dmitry Sergeevich , Belyakova Ludmila Sergeevna 
Published: October 2021

Abstract
We present the digital slot — a tool for the development of new hydraulic fracturing technologies via digitization of slurry flow in narrow channels. We consider slurry containing fluid, proppant, and fiber components. The flow is described by a continuum mathematical model based on the lubrication theory. The numerical algorithm utilizes Lagrangian approach with finite volume pressure solver. We present the results of laboratory validation and simulation examples showing the key effects affecting solids transport in hydraulic fracturing: settling, bridging, gravity slumping, materials degradation, viscosity contrast, and bank formation.
A Case Study for the Development and Use of a Well Control Simulator as a Digital Twin of a Real Scenario
Authors: Curina Francesco , Talat Qushchi Ali , Aldany Ahmad 
Published: October 2021

Abstract
Simulators in the petroleum industry have been used mainly for training purposes even though they present different applications like digital twins. In this regard, a simulator must approximate the well environment to reflect operative actions and reactions. This paper describes a case study where a well control simulator has been developed to be used as a digital twin where operators may try different scenarios in a safe environment before applying them to the physical well.
To cover all aspects of the operation, the simulator should simulate surface equipment as well as a downhole environment. Numerical modeling techniques and hydraulic simulators are used to design the well response to operations. Different scenarios were established to cover most of the possible downhole problems and equipment malfunctions including electrical and hydraulic failures. The study compares a pre-determined set of KPIs common to three different types of simulation: well control, procedural and an integration of both.
The target of the study is to collect the data resulting from the use of the simulator while it replicates a real-life situation. This virtual model of the rig and the well can be used to calibrate the main drilling parameters like SPM, RPM and WOB. The digital twin is also used to optimize operational procedures and improve performance and efficiency of rig crews as well as reduce their response time to possible problems.
The results show an increase in performance when the knowledge of the rig is combined with the downhole feedback experience. This proves that training of the crew by reproducing their own equipment allows for a major jump in readiness and faster response with minimal mistakes.
In addition, conducting the operation virtually allows the crew to uncover any possible issues before tackling the physical well. This in turn helps to reduce errors and safeguard both well and equipment integrity.
This paper discusses the integration of the use of downhole environment behavior into a complete digital twin which will play an important role for providing a source of data for regular case studies concerning well control, Maintenance, Scheduling and other critical decisions. This new method candidates itself as a major contender for the future of simulation in the drilling business and shows the importance of that for reducing risks and errors.
Field Development Optimization Using Machine Learning Methods to Identify the Optimal Water Flooding Regime
Authors: Timonov Alexey Vasilievich , Shabonas Arturas Rimo , Schmidt Sergey Alexandrovich 
Published: October 2021

Abstract
The main technology used to optimize field development is hydrodynamic modeling, which is very costly in terms of computing resources and expert time to configure the model. And in the case of brownfields, the complexity increases exponentially.
The paper describes the stages of developing a hybrid geological-physical-mathematical proxy model using machine learning methods, which allows performing multivariate calculations and predicting production including various injection well operating regimes. Based on the calculations, we search for the optimal ratio of injection volume distribution to injection wells under given infrastructural constraints.
The approach implemented in this work takes into account many factors (some features of the geological structure, history of field development, mutual influence of wells, etc.) and can offer optimal options for distribution of injection volumes of injection wells without performing full-scale or sector hydrodynamic simulation.
To predict production, we use machine learning methods (based on decision trees and neural networks) and methods for optimizing the target functions.
As a result of this research, a unified algorithm for data verification and preprocessing has been developed for feature extraction tasks and the use of deep machine learning models as input data.
Various machine learning algorithms were tested and it was determined that the highest prediction accuracy is achieved by building machine learning models based on Temporal Convolutional Networks (TCN) and gradient boosting.
Developed and tested an algorithm for finding the optimal allocation of injection volumes, taking into account the existing infrastructure constraints. Different optimization algorithms are tested. It is determined that the choice and setting of boundary conditions is critical for optimization algorithms in this problem.
An integrated approach was tested on terrigenous formations of the West Siberian field, where the developed algorithm showed effectiveness.
Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells
Authors: Abdrakhmanov Ildar Radikovich , Kanin Evgenii Alekseevich , Boronin Sergei Andreevich , Burnaev Evgeny Vladimirovich , Osiptsov Andrei Aleksandrovich 
Published: October 2021

Abstract
We propose a novel approach to data-driven modeling of a transient production of oil wells. We apply the transformer-based neural networks trained on the multivariate time series composed of various parameters of oil wells measured during their exploitation. By tuning the machine learning models for a single well (ignoring the effect of neighboring wells) on the open-source field datasets, we demonstrate that transformer outperforms recurrent neural networks with LSTM/GRU cells in the forecasting of the bottomhole pressure dynamics. We apply the transfer learning procedure to the transformer-based surrogate model, which includes the initial training on the dataset from a certain well and additional tuning of the model's weights on the dataset from a target well. Transfer learning approach helps to improve the prediction capability of the model. Next, we generalize the single-well model based on the transformer architecture for multiple wells to simulate complex transient oilfield-level patterns. In other words, we create the global model which deals with the dataset, comprised of the production history from multiple wells, and allows for capturing the well interference resulting in more accurate prediction of the bottomhole pressure or flow rate evolutions for each well under consideration. The developed instruments for a single-well and oilfield-scale modelling can be used to optimize the production process by selecting the operating regime and submersible equipment to increase the hydrocarbon recovery. In addition, the models can be helpful to perform well-testing avoiding costly shut-in operations.
Application of Integrated Asset Modeling Approaches for Reservoir Management of a Large Oil Field in Western Siberia
Authors: Koshkin Timur Aleksandrovich , Sayfutdinov Azat Firdavisovich , Kukushkin Viktor Aleksandrovich , Markov Pavel Vladimirovich 
Published: October 2021

Summary
The article reviews the development and implementation of a digital twin for one of the large fields of LUKOIL-West Siberia LLC. The project team has developed an integrated asset model (IAM) of an oil field at a late stage of its development, which is used both for making managerial decisions and in the operational work of the engineering and technical service. The IAM includes simplified models of reservoirs, models of wells and gathering systems, as well as simplified models of plants. The resulting model can produce short-term assumptions regarding production levels (up to 1 year) and is highly sustainable, which is confirmed by the examples given in this article as to the application of IAM for various production tasks. The developed automated tools allow making prompt decisions to optimize well stock operation, as well as to reveal deviations in the process parameters of downhole pumping equipment and metering facilities. The use of IAM tools enable production functions to perform many application tasks related to forecasting well operation modes and evaluating the existing production capacities of the field. The cases presented in this paper serve as a good practice for application of the IM by assets in their activities and can be implemented for similar brownfields.
Creating the Integrated Model for Conceptual Engineering of Reservoir Management and Field Facilities Construction – Experience of Tazovskoe Oil and Gas-Condensate Field.
Authors: Varavva Artem Igorevich , Apasov Renat Timergaleevich , Samolovov Dmitry Alexeyevich , Elesin Artem Viktorovich , Apasov Gaidar Timergaleevich , Voyevoda Evgeniya Vladimirovna , Reshetnikov Dmitry Anatolevich , Senachin Maxim Arkadievich 
Published: October 2021

Abstract
The paper describes the experience of building a full-field integrated model (PK1 reservoir) of the Tazovskoye field, including a model of the reservoir, wells, and a gathering network, taking into account the external transportation system. In order to integrate the features of the field, such as the simultaneous development of a thin oil rim and a gas cap, high growth rates of the gas-oil ratio, oil wells - both ESP-operated and flowing, algorithms and tools have been developed, which are discussed in the paper. The results of the integrated model runs are given, main features of the solutions are highlighted.
Experience in Optimizing the Location and Parameters of Multistage Hydraulic Fractures for a Multilateral Well Based on Reservoir Simulation
Authors: Syrtlanov Vil , Golovatskiy Yury , Chistikov Konstantin , Bormashov Dmitriy 
Published: October 2021

Abstract
This work presents the approaches used for the optimal placement and determination of parameters of hydraulic fractures in horizontal and multilateral wells in a low-permeability reservoir using various methods, including 3D modeling. The results of the production rate of a multilateral dualwellbore well are analyzed after the actual hydraulic fracturing performed on the basis of calculations. The advantages and disadvantages of modeling methods are evaluated, recommendations are given to improve the reliability of calculations for models with hydraulic fracturing (HF)/ multistage hydraulic fracturing (MHF).
Methodology for Constructing Simplified Reservoir Models for Integrated Asset Models
Authors: Markov Pavel Vladimirovich , Botalov Andrey Yuryevich , Gaidamak Inna Vladimirovna , Smetkina Margarita Andreevna , Rychkov Andrey Fyodorovich , Koshkin Timur Aleksandrovich 
Published: October 2021

Abstract
The paper presents the developed methodology for building simplified reservoir models for integrated asset models (IAM) of oil and gas fields: allocation and substantiation of areas, substantiation of model parameters, substantiation of actual weighted average reservoir pressure for areas, history matching and validation, evaluation of effective injection factors, integration in an IAM, prediction calculations, model updating. The novelty of the methodology is the developed approaches and methods of considering different features of fields with a high extent of automation for areas and fields as a whole. Models based on the material balance method and two-dimensional proxy models of one-phase flow in porous media are used as simplified reservoir models in the paper.
The developed methodology has been successfully tested for four oil and gas fields of Russia, which have different geological and production features: a large field with a long development history and a large number of active wells, a field with low permeability in all pay zones and high scopes of new wells commissioning, a field with a gas cap and high gas/oil ratios (GOR) for individual wells, a field with a complex system of reservoirs and tectonic faults and a large number of multi-pay production wells.
For three out of four fields, at the moment, the IAMs have been transferred to commercial operation based on the pilot projects performed and are used by field specialists to solve the following problems: quality analysis of reservoir pressure measurements; assessments of actual reservoir pressure trends by areas; assessments of ineffective injection for areas; prediction of reservoir pressure, water cut and GOR profiles for wells (up to one year) for various prediction scenarios, including optimization scenarios (taking into account the limitations of the material balance method).
Approach to Hydrodynamic Modeling of In-Situ Combustion in Carbonate Reservoir Based on the Results of Laboratory Studies and Preliminary Works for Pilot Test
Authors: Maksakov Kirill Igorevich , Lesina Natalia Valerievna , Schekoldin Konstantin Aleksandrovich 
Published: October 2021

Summary
For the purpose of this work, the authors used an integrated approach to the modeling of in-situ combustion (ISC) including the results of laboratory studies and preliminary works, which significantly affect the choice of the method for implementing ISC and the results obtained in the process of modeling.
The laboratory studies provided the data on the temperature range of the beginning of high-temperature oil oxidation, which is to be achieved during the modelling of the bottomhole zone heating. Based on the resulting injectivity profile, the reservoir distribution within the injection well zone in the geological model was updated. A high-permeability channel between the injection well and one of the production wells revealed during cold water injection explains the main oil production increment resulting from ISC and demonstrated by the reservoir simulation model. Based on the results of model runs for a more uniform distribution of the effect between producing wells, the best start-up time for the most reactive well was determined. Using dynamic modeling of in-situ combustion in a carbonate reservoir, the parameters of this technology implementation were found, and incremental oil production was estimated.
For the first time, the ISC technology is planned for implementation in a carbonate reservoir with high-viscosity oil in Samara region. The developed integrated approach to the dynamic modeling of in-situ combustion, which considers both the laboratory studies and preparatory work data, enables the most accurately determination of the best ISC technological parameters and this technology contribution.
Practical Application of Integrated Simulation Technologies for Asset Development and Surface Facilities Design of Gas Fields
Authors: Zhuravlev Mikhail , Novikova Anastasiia , Cherkasova Aleksandra , Shakhov Dmitry , Kharkovsky Alexander , Belousov Roman , Sadykov Azamat , Anuchin Konstantin , Avanesyan Grisha , Khafizov Vadim 
Published: October 2021

Abstract
The main goal of this paper is to describe the automation process for asset design solutions assessment in accordance with the expected production levels in dynamics. The integrated model contains embedded sub-models (various assessment elements, such as pipeline networks, compression facilities, gas treatment units, reservoir simulation models for production profiles simulation and an economic model to obtain an instant investment estimate).
A continuous data flow between all the component models provides a quick assessment of different variables influence on the final efficiency of the integrated asset development option; this approach makes possible the rapid expansion of options range as well as the increase in analysis depth.
We describe this approach on the example of the gas assets group development project, which includes the integration of following part of surface facilities: pipeline networks (gathering system) for well pads with the corresponding booster compressor stations and transport network to deliver well product to gas process unit. The work shows the recommendations about how to set up the optimal configuration of an integrated model (type and composition of sub-models, linking algorithms, data exchange directions, etc.) to solve various issues of long-term planning. In addition, we show the example of standardizing the process of managing the sub- models to provide the integrated model fast update when new production data arrives or when the surface facilities concept is changed and to make the approach transfer to other close projects easier.
The novelty of the work lies in the creation of a unique approach to solve the issues of conceptual design by flexible configuration of an integrated model for specific tasks. This approach includes processing of production data different formats, the ability to connect an economic model to obtain the instant investment assessment of surface facilities option within comprehensive analysis. In addition, it includes the ability to connect detailed models of the gas-processing unit and booster compressor station with prospective economic efficiency assessment in accordance with the production profiles updates.
The integrated model example and overall approach that we provide in this paer is unique due to the following factors:
– "flexibility" of the model, which changes its appearance depending on the tasks. – prompt update of the economic indicators of the project. – clear accounting of transport and process facilities (use of detailed models for pipeline and processing systems (including booster compressor stations).