bluuebunny/crossref_metadata_2025_split huggingface dataset:
Digital Twin for Downhole Pressure Gauges: Model and Field Case Study
Authors: Vrabie Ivan , Spesivtsev Pavel , Kaipov Yermek , Syresin Denis , Lomukhin Alexander , Romashkin Sergey , Kulyatin Oleg 
Published: September 2020

Abstract
We propose a novel technique to calculate bottomhole pressure (BHP) during well clean up and production using machine learning (ML) approaches solely based on the datasets commonly collected at surface from multiphase flow rates, wellhead pressure, wellhead temperature and fluid properties. We show that the proposed technique enables creating a digital analog of the downhole pressure gauge to provide stable and safer measurements of downhole parameters.
The proposed solution was built by use of classical supervised ML methods such as linear regression, regressions with regularization, and decision trees. These models were trained for given well completion and trajectory using the field datasets consisting of a time series of various surface physical data (such as rates, wellhead pressure, and temperature), acting as input features and target value data measured by downhole pressure gauge. Once the training is complete, these algorithms can predict BHP variations using only surface data.
We demonstrate that the ML models are capable of predicting BHP using wellbore-specific physical parameters and showing decent prediction quality with error less than 5% during transient flow regime. Moreover, we show that these ML models are capable of evaluating the bottomhole pressure almost instantaneously, unlike traditional numerical simulations that may require certain time for calculations. The proposed approach can be an affordable and accurate alternative to traditional solutions including either inverse task problem of BHP calculation or physical measurements with pressure gauges.
In the classical approach, the transient multiphase flows are studied using mathematical models and numerical simulators. In this work, ML models applied to solve the BHP prediction problem demonstrated a low prediction error and a fast prediction time. The results can be used to efficiently control the drawdown during well clean-up and production.
Automated Missed Pay Zones Detection Method Based on BV10 Member Data of Samotlorskoe Field
Authors: Kanaev Ilya Sergeevich 
Published: September 2020

Abstract
Automated pay zone detection on well log data using machine learning
Nowadays in the oil and gas industry more and more tasks are related to the analysis of big volume of data. Successful solutions to this type of problem are based on machine learning algorithms. The aim of this study is to create an automated model for predicting pay zones based on the analysis of well log data. The object of research are sedimentary deposits of the BV10 object of the Samotlorskoe oil and gas condensate field. In this project an approach for the gradual interpretation of lithotypes, prediction of the saturation type for potential reservoirs and also the prediction of porosity and permeability was chosen. The article discusses the application of three machine-learning algorithms: the support vector machine method, the neural network and the random forest algorithm. The existing problems of the studied data are described as well as methods for solving them and the stages of data preparation. As a result of the analysis it was decided to use a neural network to create a model for predicting pay intervals. Additionally, the accuracy of the model predictions was estimated from two wells excluded from the analysis.
A New Data Analytics Based Method to Characterize Waterflood Strategy in Geologically Challenging Mature Oil Field
Authors: Yadav Atul , Davudov Davud , Danisman Yusuf , Malkov Anton , Omara Essam , Venkatraman Ashwin , El-Hawari Ahmed 
Published: September 2020

Abstract
The uncertainties associated with oil and gas field reduces with time. When oil fields mature, there is a potential to better understand the field due to the availability of historic production and injection data. In this research, a novel approach is presented which uses data analytics techniques to optimize waterflooding in a Gulf of Suez field. A combination of qualitative and quantitative techniques has been applied to develop a new workflow for analyzing and optimizing waterflood.
The presented technique involves combining qualitative analysis (random forest) and quantitative analysis (capacitance resistance model, CRM) to obtain a waterflood strategy for the producing field. The Random forest algorithm (machine learning technique) is used to compare two time series signals – production data and injection data from producer/injector wells. The data from each injector and surrounding producers are used for random forest analysis to identify the most effective and ineffective injector-producer pairs. Next, the qualitative analysis using the capacitance resistance model (CRM) is used to determine gain values between each injector-producer pair and to also obtain new injection rates for increasing oil recovery. Results obtained from the random forest model helps reduce the number of unknowns and further validate results in CRM.
The production and injection data reveal the most effective and ineffective injector-producer pairs that are the result of changes occurring in the reservoir during waterflood. Accordingly, the use of data analytics technique of random forest analysis and CRM on production injection data helps improve reservoir characterization. This combined analysis for the presented field uniquely helps identify effective and ineffective injector-producer pairs to determine the efficiency of waterflooding. The results from this novel analytical technique are presented for the Gulf of Suez field. These results compare well with the streamline approach presented for the same Gulf of Suez field.
In summary, a new method for reservoir surveillance using data analytics technique of random forest in combination with the capacitance resistance model is presented. The novel combination of the qualitative and quantitative methods presented also helps adapt the specific characteristic of this field – the presence of water drive (pseudo injector). The modeling of water drive as an additional injector (pseudo injector) improves the gain coefficient obtained from the CRM. The comparison with streamlines helps benchmark the model results especially in cases where such secondary data is not available. The model presented can be adapted to similar mature fields under waterfloods. This new approach can be used to optimize water injection more frequently using operations data being gathered for implementing digitization strategies for oil and gas companies.
Data-Driven Analytics for Early Warnings in Reservoir and Well Performance
Authors: Canchucaja Ramiro Felix 
Published: September 2020

Abstract
This study is aimed to detect deviations by using an algorithm fast enough to process data and to elicit information as raw data is being gathered in order to calculate past and current performance parameters and to calculate data-driven future performance. Data manipulation is normally limited to gathering for history matching purposes, but it is not usually processed with data mining techniques to detect performance pattern changes, to discriminate between stable and non-stable periods, to detect changes of fluid density and to perform production forecasting. The algorithm is developed in R language due to the easiness to execute statistical operations with a great deal of data.
Adaptation of Steady-State Well Flow Model on Field Data for Calculating the Flowing Bottomhole Pressure
Authors: Baryshnikov Egor Sergeevich , Kanin Evgenii Aleskseevich , Osiptsov Andrei Aleksandrovich , Vainshtein Albert L'vovich , Burnaev Evgeny Vladimirovich , Paderin Grigoriy Vladimirovich , Prutsakov Alexander Sergeevich , Ternovenko Stanislav Olegovich 
Published: September 2020

Abstract
In this paper, we demonstrate the ability to run an adaptation on field data for a steady-state multiphase pipe flow model for calculating the bottomhole pressure of an inclined well using the deviation survey data, wellhead parameters (pressure, phases flowrates and properties), and the temperature distribution along the wellbore. In the underlying model published earlier in different modifications (Baryshnikov et al., 2020; Kanin et al., 2019), we divide the well into segments and perform the hydraulic calculation of the pressure distribution along the wellbore via the marching algorithm in which the set of PVT correlations and the segment model are utilized. The distinguishing feature of the utilized approach is a data-driven segment model required for calculating the pressure drop along the chosen part of the pipe. This model is based on Artificial Neural Networks (ANNs), which firstly trained on the synthetic data and lab measurements, and after that, additionally tuned on the real field data collected on two wells during the flowback period conducted via the free-flow production method. During the optimization of the ANNs on the field data, we imply the quasi-steady-state approximation of the multiphase flow, i.e. the transient flow is considered as a sequence of the stationary states. In work, we also carry out the comparison of the prediction capability of the constructed pipe segment model with several most common multiphase flow correlations and mechanistic models (within the frame of the marching algorithm) and demonstrate its more accurate outcome. As a result, we develop a method for calculating the flowing bottomhole pressure value adaptable on the required field data. The model adapted on field data allows estimating the flowing bottomhole pressure at each time moment (assuming the quasi-steady-state flow) which is particularly important in the absence of the bottomhole pressure gage at the well.
New Solution in Integrated Asset Modeling for Multi Reservoirs Coupling
Authors: Bogachev Kirill , Grishin Aleksei , Piskovskiy Evgeny , Erofeev Vladislav 
Published: September 2020

Abstract
A new approach for numerical modeling of a set of compositional dynamic models of oil and gas fields coupled by the same surface network is considered. A unified coupled system of equations is constructed to jointly model reservoir and surface network processes in the method proposed.
A new integrated model "reservoir-well-surface network" is a unified system of equations that includes a description of the physical processes in the reservoir, frictional and static pressure losses in wells and the surface network. The model includes technological constraints induced by well operation modes and surface network equipment. The unified system is solved fully implicitly at each time step.
The fluid model can be described both with tables in Black Oil format or with equations of state. If several fields with compositional fluid models are coupled by one surface network then specially designed algorithm is applied to automatically derive a single equation of state. The latter is then used to calculate the fluids properties of the mixture in the surface network.
Multivariate Spatial Temporal Model of Gas Dynamic in Underground Gas Storage Based on Saturation Parameter from Well Logging Data
Authors: Degterev Anton Yurievich 
Published: September 2020

Abstract
The article considers the first successful example of the UGS operation process modeling based on a saturation parameter derived from well logging data in a multivariant space-time form. Well logging data is a valuable source of information on the actual gas distribution in the UGS reservoirs and on its dynamics during gas injection and production. In traditional hydrodynamic modeling, accounting for the existing dataset of a well logging control is problematic; traditional geometric interpolation algorithms either do not allow obtaining equally probable realizations of the modeled distribution, or require a priori setting of the trend model, which is normally impossible for solving this problem. Previously, no model could fully consider all available well logging control data.
The initial modeling data is a sparse array of heterogeneous space-time structure with a priori unknown value distribution. The only currently known method that is able to obtain an equally probable set of realizations based on data with both arbitrary position and property distribution is the Amazonas method, which is based on a local prediction of properties based on robust statistics in a randomly located search window. Nowadays, the experience of the successful application of this method for multi-variant modeling of the reservoir property allocation has been accumulated. In this article, the possibility of its application to solve the issue of modeling the UGS gas dynamics was studied for the first time.
The result of this research is a multivariant dynamic model of the UGS gas deposit, fully reproducing the entire array of well logging control data for several gas injection/production cycles. Although this type of model does not have the forecast power of a full – fledged simulation model, it helps solving problems going beyond the scope of the traditional hydrodynamic modeling. It can be used to identify areas of maximum uncertainty, which are prospective for setting up work on additional object exploration (including drilling new monitoring wells), to define discrepancies between the gas volumes carried as assets and the actual volumes of gas, to study gas dynamics and to perform multi – variant calculation of gas volumes in the reservoir in cases where, for one reason or another, it is impossible to build a traditional hydrodynamic model.
Explicit Numerical Evaluation of Productivity Impairment in Hydraulically Fractured Wells of Gas Condensate Reservoirs
Authors: Alakbarov Samir , Behr Aron 
Published: September 2020

Abstract
Formation and fracture damage coupled with condensate blockage might significantly reduce the productivity of the hydraulically fractured gas condensate wells. Implicit numerical representation of a hydraulic fracture oversimplifies and disables distinction of individual physically occurring mechanisms causing the well deliverability loss. In this paper, more realistic analysis of production impairment is presented by explicit fracture modelling using an in-house tool. The objective is identification and ranking of the main factors and flow characteristics contributing to productivity drop.
The in-house tool can be characterized as an interface between a fracture design modelling software and a reservoir simulator. An accurate fracture geometry and spatial property mapping is incorporated into a reservoir model via local grid refinement (LGR) generation. An additional functionality of the in-house tool also allowed, based on the fracture report, to determine the initial distribution of the leak-off fluid around the fracture. A mechanically induced damage zone surrounding the fracture was reproduced by permeability/porosity reduction or transmissibility modification on the boundary between the matrix and fracture interface. Permeability modification along with stress-dependent transmissibility option were used to represent the damage of proppant pack on fracture gridblocks.
Explicit and comprehensive numerical representation of the near fracture flow performance was first implemented in a synthetic model. The conducted study revealed that the damage both inside the fracture and in the formation accelerates and enhances the condensate blockage effect. High-velocity capillary number effect within the fracture masked the negative contribution of liquid drop-out on relative permeability curves and can also overwhelm the turbulent non-Darcy flow impact.
To validate the observed phenomena a single well reservoir sector model with explicit hydraulic fracture representation was calibrated against the real production data. Adequate history match of the measured BHP was only possible with dramatic fracture and formation permeability reduction to account for damage processes. Condensate blockage effect caused more than 50% loss of gas flow rate.
Analysis and Prediction of Well Performance in Heterogeneous Reservoirs Based on Field Theory Methods
Authors: Yudin Evgeniy , Poroshin Ilya , Korikov Dmitriy , Morozova Anna , Kolyuk Olesya 
Published: September 2020

Abstract
As a rule, the basis for engineering calculations for monitoring the productivity of wells is the model of stationary and unsteady inflow in homogeneous formations or formations with a given type of zonal heterogeneity. This is due to the presence of analytical models for these cases with a high calculation speed. The approaches proposed in the article on the use of field theory methods, on the one hand, being analytical, have a high calculation speed, and, on the other hand, allow one to take into account an arbitrarily distributed inhomogeneity in filtration-capacitive properties. This advantage can become the basis for the development of new engineering tools in assessing the flow in heterogeneous reservoirs.
The main goal of this work is to develop a methodology for calculating well productivity and rapid assessment of pressure distribution in heterogeneous reservoirs. In contrast to the traditional approach to modeling filtration in inhomogeneous media using numerical finite-difference and finite-element methods, the article investigates the applicability of computational algorithms based on the use of the mathematical apparatus of perturbation methods and composite asymptotics.
Novel Approach to Detailed Flow Modeling in Fractured Reservoirs Using Adaptive PEBI Grids
Authors: Filippov Danil Denisovich , Vasekin Boris Vasilievich , Maksimov Dmitry Yurievich , Mitrushkin Dmitry Alexandrovich , Mezentseva Margarita Aleksandrovna , Shcherbina Vera Viktorovna , Roshchektayev Alexei Petrovich 
Published: September 2020

Abstract
In present paper, we propose efficient computational algorithms for simulation of the flow in deposits with intersecting fractures systems on unstructured PEBI (PErpendicular-BIsectional) grids adapted to geometrical features of well completions: direct flow calculation through the hydraulic fracturing crack on the PEBI grid, the Oda method to calculate effective medium parameters (full permeability tensor) on PEBI grids, EDFM (embedded discrete fracture model) on PEBI grids. Additionally, we propose an algorithm for modeling multilateral wells on the PEBI grid that factors in each wellbore trajectory. The proposed methods are developed for the isothermal three-phase flow problem, including phase transitions, gravity and capillary forces (the Black Oil model).
The developed approach allows for correct simulation of both SRV fractures extensive network distribution features and adding new Fish-Bone wellbores, which enables physically based history matching and accurate and realistic forecast in reservoir modeling.
Creating and Maintenance of Well's Lift Models at Oil Rim Field's Under-Gas-Cap Zones - Experience Novoport Oil and Gas-Condensate Field and Tazovskoe Oil and Gas-Condensate Field
Authors: Apasov Renat Timergaleevich , Apasov Gaidar Timergaleevich , Varavva Artem Igorevich , Vinogradov Dmitriy Vadimovich , Polkovnikov Fedor Igorevich , Platonov Dmitriy Vitalievich , Samolovov Dmitriy Alekseevich 
Published: September 2020

Abstract
The paper describes the experience of lift models creating in case of oil rims of the Tazovskoe and Novoportovskoye fields. There is analysis of the actual well performance with high gas oil ratio, the software capacity to the gas-liquid flow modeling in the well, and review of existing analytical techniques for unknown model parameters prediction. The problems and limitations of existing methods are emphasized and approaches to their solution are proposed.
Application of the AICDs and Particularities of Simulation of Such Devices in Various Mining and Geological Conditions of the Vostochno-Messoyakhskoe Field
Authors: Buzaev Anatolii Sergeevich , Meledin Andrey Sergeevich , Osipenko Aleksey Stanislavovich , Demenev Roman Aleksandrovich , Isakov Kirill Dmitrievich , Glushchenko Nikolay Aleksandrovich , Konopelko Aleksey Yurevich 
Published: September 2020

Abstract
Greater part of new gas-oil fields in Western Siberia are characterized by extensive gas caps, less oil net pay and underlying water saturated intervals.
Determine the major criterion for successful release of application of autonomous inflow control devices (AICD) in objects of the Vostochno-Messoyakhskoye field and methodology of searching for candidate wells. This article presents experience of using AICD on the wells of the PK1-3 and MKH8-9 formations, and results of multiple-path simulation.
The article describes features of simulation modeling of AICD such as initialization of multi-segment wells, creating virtual ICD and estimating of its parameters, as well as methods of history matching of AICD-wells. Efficiency of AICD depending on the geological and technological features: distance to GOC, OWC to well, spreading of permeability along near-wellbore formation, compartmentalization. To calculate influence of these parameters, multivariate calculations performed on synthetic hydrodynamic models.
To determine optimum of AICD working and select candidate wells was calculated various geological and geophysical characteristics: depth of GOC and WOC, properties of fluids, heterogeneity of reservoir properties.
Based on results of this research, optimal geological and geophysics conditions for wells of the "Vostochno-Messoyakhskoye" field were defined. Methods and outputs of this article can be used to solve same issues in similar formations and development schemes.
An Integrated Approach to the Determination of the Tranzision Zone and Water-Oil Contact in an Inhomogeneous Carbonate Reservoir with Various Facies Environments
Authors: Metelkina Natalia Valerievna , Silaeva Elena Anatolievna 
Published: September 2020

Abstract
The effectiveness of exploration and subsequent production drilling and resource base assessment is determined by the reliability of the geological model of the field, the quality of which depends on the most likely distribution of parameters in the inter-well space of the deposit and the most accurate determination of water-oil contact (WOC).
The position of the WOC and the size of the transition zone are key parameters for evaluating the field's reserves, determining the depth of well hole targets, and monitoring the development of the field. The purpose of this work was to determine the free water level (FWL), the size of the transition zone and WOC position on the basis of a integrated approach that takes into account well log interpretation data, capillarometric studies, well tests, results of hydrodynamic logging, production logging test (PLT) and well operation in the carbonate deposits of a complex lithofacial structure of a unique field in the South of Iraq.
Evaluation of Petrophysical Relations by Using Cluster Analysis
Authors: Eltsov Igor Nikolaevich , Samoilov Mikhail Ivanovich , Toropetsky Konstantin Victorovich , Borisov Gleb Alexandrovich 
Published: September 2020

Abstract
The paper describes the methodological aspects of building one-dimensional (1D) models of the physical and mechanical properties of rocks in the near-wellbore space through building statistical correlations of petrophysical and physical and mechanical properties based on the integrated field geological and geophysical information, including laboratory core studies, mud logging, and well logging data.