bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization of the Reservoir Pressure Maintenance System in a Low-Permeability Carbonate Field
Authors: Ismagilov Ruslan Faritovich , Chernykh Irina Aleksandrovna , Chukhlov Andrey Sergeevich , Nikulin Sergey Evgenievich , Gulyaev Danila Nikolaevich , Zinurov Linar Airatovich 
Published: October 2021

The investigated field is located in the Solikamsk drawdown in the northeast of the Perm Territory. The oil content level of this field is composed of Tournaisian-Famennian, Radaevsky, Radaevian, and Tula formations. This article will analysis carbonate deposits from the Tula formation using the multiwell retrospective testing (MRT) technology.
Currently, the development system has been already formed, and there is ongoing compaction drilling and targeted drawdown increase that is carried out at certain wells. A pressure support system has been formed. Before the surveys have been conducted, there was a trend in production decline, for reasons that are currently unknown. To identify the causes of production decline at the carbonate reservoir in the field, special technology was used to analysis production history data and bottom hole pressure - this technology is called multiwell retrospective testing (MRT).
Four sections were selected for further analysis, MRT was able to reconstruct the reservoir pressure variations and production coefficient at the tested well, the influence of the offset wells on the tested wells has been evaluated, along with transmissibility at the cross-well interval and well-bore skin of the tested wells.
Modified CR-Type Material Balance Model for Well Production Forecasts in Case of Well Treatments
Authors: Gubanova Anna E. , Khabibullin Bulat A. , Orlov Denis M. , Koroteev Dmitry A. 
Published: October 2021

Abstract
To reduce inefficient costs and environmental risks, oil companies strive to optimize the process of hydrocarbon production at all stages of field development, including geological and technical works at wells. In particular, it is important to predict fluid production with high accuracy. 3D hydrodynamic modeling is a generally accepted technique for solving this problem. It provides reliable results but requires many input data, computational resources, and time for calculations. Since the decision-making process has to be reactive, it is necessary to develop a simultaneously precise and prompt predictive instrument for quick forecasts of liquid production. The most promising tools for these purposes are proxy models based on solving the material balance equation. They adapt to the existing historical data even without PVT properties and reservoir data. Some of the most popular approaches are proxy models such as Capacitance Resistance Models (CRM).
CR-type model is a material balance-based flow model, which provides preferable transmissibility trends, the presence of sealing or leaking faults with compressibility effects in consideration, and dissipation between injector-producer pairs. It is a data-driven model with adjustable time constants and interwell connectivity parameters. Before the model tuning, all parameters must be initialized with analytical or random approximations, and then they can be found by an appropriate optimization procedure. Historical-based Capacitance Models can be applied to poorly studied fields. Besides, they give an opportunity to rapidly optimize field development strategy by making calculations with different well exploitation parameters. They only require historical data of hydrocarbon production volumes, injection profiles, and bottom-hole pressure dynamics as input data. One of the main is that properties in the interwell space are estimated approximately and considered to be constant throughout the entire development history. However, this is a weak assumption in the case of including well interventions and stimulations.
Thus, the main goal of this work is to adjust coefficients online to changes in well operation modes, introducing new wells or shut-in the existing ones. Since the governing equation includes the considered CRM improvement, users can perform optimization over different timespans, including "special" intervals. As a result, weighting connectivity parameters of the model can be depicted on a map of well interactions versus time.
Production Performance Analysis of Volga-Ural Mature Waterflood with PRIME Diagnostic Metrics
Authors: Aslanyan Artur Mihailovich , Ganiev Bulat Galievich , Lutfullin Azat Abuzarovich , Farkhutdinov Ildar Zufarovich , Garnyshev Marat Yurievich , Farakhova Rushana Rinatovna , Mustafina Alfiya Nurimanovna 
Published: October 2021

Abstract
The paper presents a practical case of production performance analysis at one of the mature waterflood oil fields located at the Volga-Ural oil basin with a large number of wells. It is a big challenge to analyse such a large production history and requires a systematic approach.
The main production complication is quite common for mature waterflood projects and includes non-uniform sweep, complicated by thief injection and thief water production. The main challenge is to locate the misperforming wells and address their complications.
With the particular asset, the conventional single production analysis techniques (oil production trend, watercut trend, reservoir and bottom-hole pressure trend, productivity trend, conventional pressure build-up surveys and production logging) in the vast majority of cases were not capable of qualifying the well performance and assessing of remaining reserves status. The performance analysis of such an asset should be enhanced with new diagnostic tools and modern methods of data integration.
The current study has made a choice in favor of using a PRIME analysis which is multi-parametric analytical workflow based on a set of conventional and non-conventional diagnostic metrics. The most effective diagnostics in this study have happened to be those are based on 3D dynamic micro-models, which are auto-generated from the reservoir data logs.
PRIME also provided useful insights on well performance, formation properties and the current conditions of drained reserves which helped to select the candidates for infill drilling, pressure maintenance, workovers, production target adjustments and additional surveillance.
The paper illustrates the entire PRIME workflow, starting from the top-level field data analysis, all the way to generating a summary table containing well diagnostics, justifications and recommendations.
Short-Term Forecasting of Well Production Based on a Hybrid Probabilistic Approach
Authors: Evseenkov Anton Sergeevich , Kuchkildin Denis Kamilevich , Krechetov Konstantin Igorevich , Ospishchev Semyon Alexandrovich , Kotezhekov Victor Sergeevich , Yudin Evgeny Viktorovich 
Published: October 2021

Abstract
The presented article is dedicated to creation and testing of probabilistic ensemble computational tool for operational forecasting of well production in short term (STF). The ensemble consisted of models based on such physical and mathematical tools as: the equation of non-stationary filtration, material balance, Darcy's law and machine learning models. After calculations by each model, their forecasts are combined into a single ensemble forecast. The hybrid approach is based on the Monte Carlo method on Markov chains as a separate probabilistic model using Bayes’ formula. In this case, statistical weights of each model (the degree of confidence in each model) is determined in the form of a probability distribution based on the reliability of previously performed forecasts. The test results presented in this article were obtained on the real field data. The obtained forecasts of individual models and the ensemble were compared to real data. Real data tool usage analysis showed that the proposed approach gives a small error in comparison with actual measurements. Efficiency of calculations allows to automatically adapt the model to the entire well production history (several hundred wells) within a few hours.
Intelligent Production Monitoring with Continuous Deep Learning Models
Authors: Gryzlov Anton , Safonov Sergey , Arsalan Muhammad 
Published: October 2021

Abstract
Monitoring of production rates is essential for reservoir management, history matching, and production optimization. Traditionally, such information is provided by multiphase flow meters or test separators. The growth of the availability of data, combined with the rapid development of computational resources, enabled the inception of digital techniques, which estimate oil, gas, and water rates indirectly. This paper discusses the application of continuous deep learning models, capable of reproducing multiphase flow dynamics for production monitoring purposes. This technique combines time evolution properties of a dynamical system and the ability of neural networks to quantitively describe poorly understood multiphase phenomena and can be considered as a hybrid solution between data-driven and mechanistic approaches. The continuous latent ordinary differential equation (Latent ODE) approach is compared to other known machine learning methods, such as linear regression, ensemble-based model, and recurrent neural network.
In this work, the application of Latent ordinary differential equations for the problem of multiphase flow rate estimation is introduced. The considered example refers to a scenario, where the topside oil, gas, and water flow rates are estimated using the data from several downhole pressure sensors. The predictive capabilities of different types of machine learning and deep learning instruments are explored using simulated production data from a multiphase flow simulator.
The results demonstrate the satisfactory performance of the continuous deep learning models in comparison to other machine learning methods in terms of accuracy, where the normalized root mean squared error (RMSE) and mean absolute error (MAE) of prediction below 5% were achieved. While LODE demonstrates the significant time required to train the model, it outperforms other methods for irregularly sampled time-series, which makes it especially attractive to forecast values of multiphase rates.
Selecting and Modifying Multiphase Correlations for Gas-Lift Wells Using Machine Learning Algorithms
Authors: Gorbachev Roman , Gubaev Andrey , Lubnin Alexander , Chorny Alexey , Kurbanov Vasif 
Published: October 2021

Abstract
In the conditions of the development of the oil fields of the Cuu Long Basin in the continental shelf of the Republic of Vietnam, in the absence of downhole gauge systems, the urgent task is improving the accuracy of the calculation of bottomhole pressure in the producing wells based on the operation modes and construction.
The aim of the paper is to create tools for selecting and modifying the correlation of multiphase flow most suitable for the development of a particular group of fields, as well as to develop a tool to implement effective management of the modes of operation of gas-lift wells by choosing the optimal gaslift injection rate.
Based on data from 814 instrumental measurements in wells with different construction, liquid flow rate, watercut, GOR and gaslift injection, the calculation of bottom hole pressures was made.
The calculated and actual bottomhole pressures were compared with five correlations of multiphase flow, the most suitable correlations were determined and modified, including using machine learning methods, which helped to significantly improve the convergence of calculated and actual bottomhole pressures.
On the basis of the newly modified correlation, a calculation of bottom hole pressure (BHP) in each production well was made, the calculation of the change in bottomhole pressure when changing the operating modes of wells has been implemented.
For the field group of the Cuu Long Basin, it was revealed that with the increase in watercut in the producing wells significantly reduces the efficiency of the gas-lift method of operation. This effect is not reflected in the widespread correlations of multiphase flow, which does not allow to use the results of calculations without making additional edits.
A way to adapt the calculation values to instrumental measurements has been implemented, one of the known correlations has been modified and used in the forecast of changes in bottomhole pressure after changes in operating modes of wells throughout the well stock.
Integrated Field Development Modeling of Block in Giant Oil Reservoir
Authors: Bogachev Kirill , Zagainov Aleksandr , Piskovskiy Evgeny , Moshina Iuliia , Grishin Aleksei , Muryzhnikov Anton , Gatin Aydar , Korostelev Nikita 
Published: October 2021

Abstract
The creation and matching of an integrated field model including a model for part of a giant field, well models and surface network model is considered here.
The integrated model was created using an innovative method of solving a unified system of equations that cover all the physical processes in the reservoir-well-surface network system; no integrator software was involved.
The project involves a history-matched dynamic model covering part of a giant field, a surface network layout and well constructions with the subsurface equipment parameters. These data were fed to a single software product to create a digital twin which would allow simultaneous work with both the reservoir and the network.
The approach enabled quick creation and matching of an integrated model with a lot of wells which can create forecasts for various operation modes and estimate the base case production for the infrastructure in place, as well as offers an option to connect new project wells to the current surface network.
Hierarchy of Integrated Models of Varying Detail to Solve Problems at Different Stages of Gas Condensate Projects Development
Authors: Varavva Artem Igorevich , Apasov Renat Timergaleevich , Badgutdinov Ruslan Rustamovich , Yamaletdinov Ayrat Flyurovich , Koryakin Fedor Andreevich , Sandalova Ekaterina Evgenevna , Samolovov Dmitriy Alekseevich , Bikbulatov Salavat Miniahmetovich , Nekhaev Sergey Sergey 
Published: October 2021

Abstract
This study makes an attempt to generalize the integrated modeling tools used by the authors in practice on the basis of the following criteria: the volume of the initial data, the integration mechanism, constraints and assumptions. Various types of integrated models are compared with each other, the need for their application is analyzed, they are connected at various stages of the project development and by the tasks solved at this stage. It also describes the challenges that the authors encountered when working with integrated models of various levels of detail, approaches to their solution, and the lessons learned.
Generation of a Probabilistic Facial Model on the Basis of Lithology Logs, Well Logs, Seismic Data and Existing Methods of Machine Learning and Classification
Authors: Zaitsau Henadz , Shumilyak Valeri , Konyushenko Alexander 
Published: October 2021

Abstract
The main topic of an article is machine learning and classification (neural net) use for prognostic lithological model creation. Moreover, research preceding stages such as attribute analysis, seismic inversion, seismogeological modeling and briefly the results of lithological and petrophysical investigations are described in this art
Seismic Data Analysis for Well Performance and Production Data Forecast: Massive Low-Relief Gas Reservoir Case Study
Authors: Gorlanov Aleksei Anatolyevich , Vorontsov Dmitrii Yurevich , Schetinin Aleksei Sergeevich , Aksenov Aleksandr Ivanovich , Ovchinnikova Diana Gennadyevna 
Published: October 2021

Abstract
In the process of developing massive gas reservoirs, gas-water contact (GWC) rise is inevitable, which leads to water-breakthrough in wells and declining daily gas production. Drilling horizontal sidetracks and new horizontal wells helps to maintain target production levels.
The direction of drilling a horizontal well section largely determines its efficiency. In complex geological conditions, a detailed analysis of seismic data in the drilling area helps to reduce drilling risks and achieve planned starting parameters.
The integration of seismic data in geological models is often limited by poor correlation between reservoir properties from wells and seismic attributes. Flow simulation models use seismic data based on the assumptions made by the geological engineers.
The study uses a cyclic approach to geological modeling: realizations include in-depth analysis of seismic data and well performance profiles. Modern software modules were used to automatically check the compliance of the geological realization with the development history, as well as to assess the uncertainties. This made it possible to obtain good correlation between well water cut and seismic attributes and to develop a method for determining the presence of shale barriers and "merging windows" of a massive gas reservoir with water-saturated volumes.
Comprehensive Analysis of the Geological and Geophysical Data in the Study of the Upper Miocene Turbidite Systems of the Nam Con Son Basin, Vietnam
Authors: Kurianova Mariia , Birkle Ekaterina , Egorkina Tatiana , Koltsov Sergey 
Published: October 2021

Abstract
The article considers the approaches to the G&G data interpretation used in the Branch Office of Gazprom International in Saint Petersburg (hereinafter referred to as "GPEPI") when studying the geology aspects of turbidite deposits. This approach is showcased on one of the Upper Miocene deposits of the Nam Con Son Basin in Vietnam, and a conclusion is drawn about the possibility of using this complex technique in the study of sand bodies of any genesis.
Efficiency of Using a Proxy Model for Modeling of Reservoir Pressure
Authors: Yudin Evgeniy Viktorovich , Markov Nikolay Sergeevich , Kotezhekov Viktor Sergeevich , Kraeva Svetlana Olegovna , Makhnov Andrei Vasilyevich , Trubnikov Nikita Pavlovich , Gorbushin Leonid Arkadevich 
Published: October 2021

Abstract
The presented paper is devoted to the development and testing of a computational tool for assessment of the reservoir pressure and prompt generation of the pressure maps of collectors. The tool is based on a proxy model that allows to solve the two-dimensional diffusion equation for unsteady liquid filtration using the boundary element method. To expand the applicability of the proxy model, an algorithm for automated parameter adaptation was developed. This algorithm allows to exclude knowingly unreliable data or low-quality data from modeling. This is achieved due to analyzing the correlation between the injection, production and bottom-hole pressures for the entire well stock over the history of the reservoir development. In addition, this paper describes an approach to modeling two-phase oil and gas filtration based on the use of pseudofunctions. This approach considers the influence of gas on the oil filtration process. The use of pseudofunctions allows us to linearize the diffusion equation for two-phase filtration and to solve it using the boundary element method in the same manner as for the case of oil filtration without gas. To demonstrate the results of the proxy model validation, examples of its use for generating the pore pressure maps for two real collectors are given. The average values of the reservoir pressure at the wells calculated using the proxy model are compared with the results of the corresponding well tests and with the traditional isobar maps. The analysis showed that the average deviation of the proxy model from the real reservoir pressures is less than 10%.
Solving the Inverse Problem of Geomechanical Monitoring to Improve the Efficiency of Field Development
Authors: Krasnikov Artem Alexandrovich , Melikov Ruslan Fuadovich , Korelskiy Evgeniy Pavlovich , Petrakov Yuriy Anatolyevich , Sobolev Aleksey Evgenievich 
Published: October 2021

Abstract
The purpose of geomechanical monitoring at mineral deposits, including oil and gas, is to control the state of the rock mass, forecast possible risks and complications at all stages of the field's life: reservoir and caprock integrity failure, violation of the integrity of drilling and system of well completion, infrastructure facilities.
The paper suggests approaches to the organization of a geomechanical monitoring system based on observations of displacements and deformations of the ground surface (or seafloor) during the development of reservoir and target interval.
There are analytical and numerical approaches have been tested on simple models with pore pressure changes, considering the heterogeneity of elastic-strength properties.
Artificial Neural Network as a Method for Pore Pressure Prediction throughout the Field
Authors: Mylnikov Danila , Nazdrachev Viktor , Korelskiy Evgeniy , Petrakov Yuriy , Sobolev Alexey 
Published: October 2021

Abstract
Geomechanical model construction is an essential part of field development processes planning. Building a correct pore pressure model is one of the key tasks within the process of geomechanical model construction. The traditional approach to pore pressure modeling in oil and gas industry is based on the empirical analytical models usage. This approach has a number of disadvantages, which often lead to the constructed pore pressure model to be incorrect. The authors highlight two most significant disadvantages of the traditional approach: 1) a priori discrepancy between the empirical model and fundamental physical laws; 2) the impossibility of selecting such a combination of parameters of the standard analytical model, for which the resulting pressure corresponds to the entire set of actual field data (pore pressure measurements).
This paper proposes a methodology for assessing the pore pressure distribution across the field, based on the usage of neural network technology. This approach potentially eliminates both of the above disadvantages from the pore pressure model building.
On the Use of Indentation for Studying Rock Properties
Authors: Stsiapankin Ihar , Khaletski Andrei , Hutman Raman , Tkachou Viktar 
Published: October 2021

Abstract
It was developed the method for determining the strength of material without breaking the test sample. The essence of the method is to determine the strength characteristics of the rock by pressing into the variable cross-section indenter. The practical significance lies in the possibility of performing an express analysis of the strength of rocks on small-sized samples - 3 mm or more, for example, using the particles of landslide cuttings from unstable intervals when drilling wells; there is no need for the preparation of reference material; the simplicity and availability of the equipment used and high accuracy of the results obtained is also important. The developed method has no analogues and is patented in the patent offices of the Republic of Belarus and the Russian Federation.