bluuebunny/crossref_metadata_2025 huggingface dataset:
Smart Completion Improvement in Horizontal Wells Based on Through-Barrier Diagnostics
Authors: Solovyev Timur , Ivanov Alexander , Soltanov Dzheykhun , Meshkov Victor , Kamalov Artur , Nagimov Vener , Trusov Alexander , Aslanyan Irina , Makarov Dmitry
Published: September 2020

The complex interbedded heterogeneous reservoirs of the Severo-Komsomolskoye field are developed by horizontal wells in which, as part of the pilot project's scope, autonomous inflow control devices (AICD) are installed to prevent early coning and gas breakthroughs in long horizontal sections and reduce sand production, which is a problem aggravated by an extremely low mechanical strength of the terrigenous deposits occurring in the Pokur formation of the Cenomanian stage in this area.

The zones produced through AICDs are separated by swell packers. The issue of AICD effectiveness is discussed in the publications by Solovyev (2019), Shestov (2015), Byakov (2019) and some others.

One of the methods used for monitoring horizontal sections with AICDs is production logging (PLT). However, due to the complexity of logging objectives, the use of conventional logging techniques makes the PLT unfeasible, considering the costs of preparing and carrying out the downhole operations.

This paper provides some case studies of the Through-Barrier Diagnostics application, including passive spectral acoustics (spectral acoustic logging) and thermohydrodynamic modelling for the purpose of effective estimation of reservoir flows behind the liner with AICDs installed and well integrity diagnostics.

As a result of the performed diagnostics, the well completion strategy was updated and optimised according to the log interpretation results, and one well intervention involving a cement squeeze with a straddle-packer assembly was carried out.
Development of an Approach for Well Testing in Real Time
Authors: Pityuk Yulia Airatovna , Akmurzina Guzel Raifovna , Davletbaev Alfred Yadgarovich , Azarova Tatyana Petrovna , Farger Dmitriy Vladimirovich , Krivulyak Aleksandr Sergeyevich , Zyleva Svetlana Afanasevna
Published: September 2020

Effective management of field development requires continuous monitoring of the hydrodynamic parameters of the reservoir and wells. One of the sources of such information is the results of interpretation and analysis of well test. The aim of the work is to increase the quantity and quality of well test, which can be achieved by automating their conduct and the operational processing of field data and events at the wells. The present work describes the concept of well testing in real time, the development and implementation methods for searching candidate wells and algorithms for giving expediency estimation for well testing.

Implementation of automation of the production process for well test can be divided into several successive stages: searching for wells for well testing, giving expediency estimation of the well test with the issuance of recommendations, making decisions about the well testing, monitoring well testing, completion and interpretation of the well test. Each stage requires a methodological and algorithmic study for all types of well test. Candidate wells are searched by identifying the shutdowns and monitoring wells. Expediency estimation algorithm of well test includes calculating the duration of well test and production and injection losses, checking changes in well operating conditions, analyzing the conditioning of field data, and planned and completed well activities, including previously well tests and intersection with the well test program.

Moreover, an analysis of the business processes of well testing showed that all research methods require automation of routine operations of analysis and preparation of field data from sensors of downhole telemetry systems. For this purpose, a service-oriented application for real-time well testing is being developed. The present software allows one to optimize the well test program with the inclusion of unscheduled well test, to increase the number of studies without increasing the losses in production and injection, and to increase the coverage of measurements and the effectiveness of monitoring field development.
Summary of Application Results and Prospects for the Further Implementation of Simultaneous-Separate Operation in Slim Hole Wells and Simultaneous-Separate Operation for Three Development Locations
Authors: Yakovlev Alexandr Alexeevich
Published: September 2020

This paper describes the results of application and prospects for the further implementation of simultaneous-separate operation equipment in slim hole wells and simultaneous-separate operation equipment for three development locations in wells of Territorial production enterprise «TatRITEKneft».
The First for Russian Oil Company State of Art Intelligent Completion System Real Time Cleanup Monitoring and Optimization for 3 ERD wells in Caspian Offshore
Authors: Golenkin Mikhail Yur'evich , Biakov Aleksandr Petrovich , Eliseev Denis Vladimirovich , Zavyalov Aleksandr Aleksandrovich , Zhirkina Anna Andreevna , Pico Yamid Lopez , Shapovalov Alexey Petrovich , Bulygin Igor Aleksandrovich , Yakovlev Ivan Mikhailovich
Published: September 2020

For the first time Russian oil company has applied a new generation intelligent completion system in the Caspian offshore; the system can measure zonal phasic flow rates, fractional composition of the flow, zonal water cuts, pressures and temperatures. This equipment allowed to perform well cleanup with the new strategy, making optimization in the real time. This article describes experiences of running these operations, which are unique both for the Russian and for the world industry.

Since the technical resources in offshore field development are limited, it used to be impossible to run a thorough cleanup and assure the production of ERD wells at optimal level of performance. It states question about the practicability of long wells drilling. Three ERD wells in the Caspian offshore were the first ones where the Russian oil company used a unique all-electrical intelligent completion system. The system includes the flow control valve with infinite number of positions and the set of downhole sensors that provide a unique composition of measurements – pressure, temperature, phasic flow rates, fractional composition of the flow for each zone individually. Novel technology opened an opportunity to optimize the cleanup process by cleaning every zone separately. Meanwhile, it became possible to understand the extent to which every zone is cleaned and in real-time optimize the process by increasing the drawdown in the undercleaned zones.

New methodology to speed up cleanup process, test and commission the well was designed. It is a crucial aspect for the wells that are drilled from an offshore drilling rig. Currently there are 5 wells in Russia and 9 of them in the world where the cleanup operations were performed with the use of described completion technology.

Methodology and technology that are described in the article are innovative for the world industry. Interpretation of the expanded dataset that was obtained from the downhole sensors used to be unavailable for general use during the cleanup and it is described for the first time using an example from several wells.

Practices, experiences and technology described in the article will be useful for the field engineers, reservoir engineers and specialists who make decisions related to planning and organizing the operations both for the offshore and onshore fields.
Oil Rim Water Flooding Improvement Experience
Authors: Ilikbaev Valeriy Vitalievich , Virt Vitaliy Igorevich , Kulikov Iurii Victorovich , Kosolapov Vladimir Olegovych , Kazykhanov Rustam Ilusovich , Varavva Valentina Yuryevna
Published: September 2020

The paper describes an approach to the analysis of the efficiency of water flooding in relation to the development of the oil rim. The ways of optimizing the existing water flooding strategy are shown based on the actual operation of the wells and the forecast based on the hydrodynamic model. The issues of monitoring and control are considered as well as monitoring the operation of injection wells with the aim of subsequent operational decision-making regarding their operating modes.
Experience of the Autonomous Inflow Control Devices Application at the Oil-Gas-Condensate Field to Control Early Gas Breakthroughs
Authors: Meyer Elena , Bormashov Dmitry , Shkred Vitaly , Malofeev Mikhail , Oparin Ivan , Mayorov Andrey
Published: September 2020

Development of oil rims at oil-gas-condensate fields is accompanied by a high risk of gas coning which leads to well productivity losses followed by conversion of such wells to an idle stock. In order to increase the oil recovery factor of oil rims and to eliminate early gas breakthroughs, horizontal wells drilled in thin oil rims are equipped with autonomous inflow control system.

The study describes the experience of AICD application for one of the largest fields in East Siberia with an oil rim and a large gas cap, where an integrated analysis of the AICD well performance was conducted for the first time in Russia. This analysis includes: choosing well-candidates, performing AICD lab testing, sector reservoir simulation, modeling of AICD effect, followed by the actual well performance analysis after running AICDs. All this became possible due to the well-organized cooperative work of the operator and the contractor.

The wells included in this study were successfully put into production at lower GOR values compared to their surrounding wells
Experience of Downhole Microseismic Monitoring of Reservoir Waterflooding in Baitugan Field
Authors: Naumov Yury Anatolyevich , Tupitsyn Andrey Mikhailovich , Enachesku Cristian , Fedoseev Maxim Alekseevich , Lavrenkova Natalia Viktorovna , Mitin Alexey Vladimirovich , Kalvan Lyubov Viktorovna
Published: September 2020

The paper presents the experience of using downhole microseismic monitoring to determine the direction of working agent losses while maintaining formation pressure in Baitugan oil field in Orenburg region. Studies were aimed at history matching and elaboration of the further strategy for formation pressure maintenance (FPM) and field development as a whole.

The seismic monitoring method was used in the downhole version, when observations are done from the observation well located nearby the injection one. For reliable recording of weak microseismic events, high sensitivity receivers were used with 4 sensors per component. A special injection program was developed. Its first stage consisted in optimization of modes that ensure the maximum injection into formation of a fluid supplied at the wellhead.

During acquisition parameter testing, passive recording of the seismic signal was done at all stages of injection during two weeks in various modes, including a short-term stop of injection with subsequent increase in injection to a maximum injection rate with exceeding the fracture gradient. Fracture growth zones were identified below the target formation. Starting from the second week of recording, fracture propagations to the overlying formations were observed under increased injection pressure. Based on data obtained, a three-dimensional map of microseismic events recorded in the formation was created. It was used for interpretation and allowed to specify geometry of self-induced hydraulic fractures depending on injection modes.

Microseismic mapping of self-induced fractures in injection wells was done in Russia for the first time. The results of observations allow to optimize the formation pressure maintenance system through optimizing injection modes depending on local fracture gradients and injection shutting off in the zones with uncontrolled self-induced fractures that develop under minimum wellhead pressures. The obtained data also make it possible to significantly improve quality of history matching for carbonate deposits with reduced formation pressure through determining the injection efficiency. This result directly improves both the quality of forecast analysis and economic impact of formation pressure maintenance.
Modeling of Wax and Salt Deposition in the Near-Well Zone of a Low-Temperature Reservoir Under Various Operating Conditions of Wells
Authors: Gaidukov Leonid Andreevich
Published: September 2020

A number of saline low-temperature reservoirs in Eastern Siberia are characterized by the development of damage effects of the reservoir pore space with filtered solid particles, which are formed as a result of two specific reservoir processes: the phase transition of wax contained in oil; salt precipitation when the highly mineralized front of the injected water breaks through toward production well. These effects are most intense in the near-well zones of producing wells and are initiated by technogenic changes in thermobaric conditions and the release of dissolved gas from oil. As a result, the productivity of wells is dynamically reduced. To design effective measures to restore well productivity, it is necessary to understand the structure of the borehole zone and the dynamic mechanisms of technogenic changes in its filtration properties. For these purposes, the article developed a specialized mathematical model of wax deposition in the pore space of a low-temperature oil reservoir and a model of solid salt precipitation during filtration of extremely saturated NaCl salt solution, which allow calculating the technogenic change in the permeability of the near-well zone of the producing well during its operation.
Complex Approach to Optimization of Brownfields Business Cases Based on the Pilot Project Renovation in PJSC Gazprom Neft
Authors: Asmandiyarov Rustam Nailevich , Monzherin Mikhail Aleksandrovich , Zenov Anton Aleksandrovich , Drofa Pavel Mikhailovich , Ribakov Roman Aleksandrovich , Malcev Nikita Valerievich , Lozina Polina Sergeevna , Veselov Valeriy Mikhailovich
Published: September 2020

The paper considers a new approach to working with mature fields, assessing the effectiveness of current development, searching and forming case studies on optimization of development and associated costs.

The approach is based on several consecutive steps:

– the most accurate understanding of the composition of current costs, their correct accounting for calculating the profitability of the well stock, – the definition of low-profit wells and clusters, – search and evaluation of measures to optimize the development system and work with the wells, – search and assessment of possible measures to reduce existing costs based on best practices.

In the framework of the described methodology, each individual producing well is considered as an independent business unit, the combination of which ultimately forms the overall economy of the field. The field, in turn, is considered as a closed system of the well - wellbore - BPS / CPS, each of whose elements is interconnected.

The results of applying this approach are presented on the example of the Renovation pilot project, within the framework of which a number of optimization cases were formed and successfully implemented at one of the fields of Gazprom Neft PJSC.

Also, during the implementation of the project, a tool program was created that allows economic evaluation of all possible activities in the logic described above, to assess the impact of each element of the system on each other as a result of the implementation of the events.

Thus, a new algorithm has been formed and partially automated for continuous monitoring of current production at the field, identifying "problem" areas and the formation of optimization measures for such areas.
Decision Support System for Tight Oil Fields Development Achimov Deposits and Their Analogues Using Machine Learning Algorithms
Authors: Fedorov Aleksei Eduardovich , Povalyaev Andrey Aleksandrovich , Suleymanov Bulat Ildarovich , Dilmuhametov Ilshat Rashitovich , Sergeychev Andrey Valerievich
Published: September 2020

The aim of this work is to develop an approach to multivariate optimization of development systems for tight oil reservoirs of the Achimov formation, where large volumes of drilling of RN-Yuganskneftegaz LLC are currently concentrated on. The approach described in the paper is an integral part of the corporate module "Decision Support System for drilling out new sections of tight oil reservoirs", which allows making quick design decisions for new drilling sites of target objects.

This work discusses the main parts of the integrated solution of this system that will be embedded into corporate software.

Also, the description of the global approach and obtained results are presented. The main idea of this project is based on automatic assignment of the prospective development zone to an existing cluster-analog, based on well logs response in exploration wells. Following this interpretation, the potential performance of various development systems is evaluated and the optimal one is selected.

Within the framework of these projects the following tasks were solved:

Wells clustering in Achimov deposits and their analogs. The geological heterogeneity and reservoir connectivity were characterized and a special algorithm for wells assignments to an existing cluster was developed, that is done by: Wells clustering depending on their petrophysical properties derived from well logs interpretation via k-means algorithm. Wells classification with a use of neural network. Multivariate 3D dynamic modeling and creation of surrogate models to provide predictions of reservoir simulation results. Development of the software package with all mentioned functionality being implemented.
Deep Neural Network for Real-Time Location and Moment Tensor Inversion of Borehole Microseismic Events Induced by Hydraulic Fracturing
Authors: Wamriew Daniel Stephen , Charara Marwan , Maltsev Evgenii
Published: September 2020

Locations and source mechanisms of microseismic events are very crucial for understanding the fracturing behavior and evolution of stress fields within the reservoir and hence facilitates the detection of hydraulic fracture growth and estimation of the stimulated reservoir volume (SRV). In the classic workflow, there are two main methods for locating microseismic events with a calibrated fixed velocity model: grid search and linear inversion. The grid search is very stable; can find a global minimum and does not need initial event locations. However, it is computationally intensive and its resolution depends on the grid size, hence, it is not suitable for real-time monitoring. On the other hand, although the linear inversion method is quite fast, the inversion may be pushed into a local minimum by thin shale layers and large velocity contrasts leading to false locations. The source mechanisms of the located events, which provide information about the magnitudes, modes and orientations of the fractures, are obtained through moment tensor inversion of the recorded waveforms. In this paper, we propose a deep neural network approach to solve the above challenges, in real-time, and increase the efficiency and accuracy of location and moment tensor inversion of microseismic events, induced during hydraulic fracturing. Location of microseismic events was considered as a multi-dimensional and non-linear regression problem and a multi-layer two-dimensional (2D) convolutional neural network (CNN) was designed to perform the inversion. The source mechanisms of the microseismic events were inverted using a multi-head one-dimensional (1D) CNN. The neural networks were trained using synthetic microseismic events with low signal to noise ratio (SNR) to imitate field data. The overall results indicate that both the 2D CNN and 1D CNN models are capable of learning the relationship between the events locations and source mechanisms and the waveform data to a high degree of precision compared to classical methods. Both the event location and source mechanism errors are less than few percent. Deep learning offers a number of benefits for automated and real-time microseismic event location and moment tensor inversion, including least preprocessing, continuous improvement in performance as more training data is obtained, as well as low computational cost.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Optimization of Stress & Strain Tensors Computational Algorithm While Constructing 3D Geomechanical Model of the Field
Authors: Mylnikov Danila , Melnichuk Dmitrii , Korelskiy Evgeny , Petrakov Yuriy , Sobolev Alexey
Published: September 2020

This work is dedicated to studying the numerical description of oil & gas field mechanical behavior. The algorithm is proposed for the stress and strain tensor distribution computation within the assumption of infinitesimal elastic strains. The proposed algorithm is verified and demonstrated to be potentially more effective than the algorithms currently used in industry for the same problem solution. The proposed algorithm is based on finite volume method with two-grid solution scheme. After algorithm verification versus COMSOL software, it is applied to geomechanical model construction for a particular oilfield located in Russia. The model is successfully constructed and the model quality check is passed.
Elemental Study of Single Fractures Conductivity Using 3D Prototyping and Nuclear Magnetic Resonance
Authors: AlSinan Marwah Mufid , Gao Jun , Kwak Hyung Tae , Satrawi Mustafa
Published: September 2020

Fluid flow in fractures is of great interest in the oil and gas industry due to its numerous applications in naturally and hydraulically fracture reservoirs. In order to resolve a fracture's conductivity, its structural and hydraulic properties must be analyzed extensively. Advancements in Real-time Nuclear Magnetic Resonance (NMR) tests and 3D-printing technology impelled them to become valuable analytical tools. This paper takes a multi-disciplinary approach to improve empirical studies of flow in fractures.

A resin core-plug of parallel-plate fracture model was created. The fracture design has a well-known theoretical solution which was used to validate experimental results. To increase fluid susceptibility to NMR measurements, fractures were replicated 6 times in each plug. X-ray micro-computed tomography images were conducted to evaluate the manufacturing quality and geometrical parameters.

NMR T2 relaxation time and saturation profiles were conducted to monitor the flood front and asses the fracture volume. The NMR pore volume can be substituted in the cubic law equation to obtain the fracture permeability.

3D printing technology provides a non-destructive and efficient method of controlling geometrical parameters in the designed models. In addition, 3D printing is flexible regarding the manufacturing material, which allows control of surface wettability. All of these factors would improve the quality of model studies, including the current study of the impact for different geometrical factors on fracture conductivity
Metrology and Standardization in Geomechanical Modeling
Authors: Ravilov Nikolay , Tatur Olga , Chettykbayeva Kamilla , Sobolev Alexey , Petrakov Yuriy , Ocheretyanyy Anton
Published: September 2020

The paper presents an algorithm for estimating the error of the 1D geomechanical model taking into account the measurement errors of the instruments and the correlation dependencies used. The sensitivity analysis of the resulting error to the quality of the source information was also performed. A confidence parameter has been introduced to assess the contribution of a measurement to the calculations. An approach is proposed for the transition from the convergence of correlation dependencies to the error of the correlation used.

The application of this methodology allows at the initial stage to draw a conclusion about the quality of simulation results and take measures to increase the reliability of calculations.
Plastic Instability of Vikulovskaya Suite
Authors: Toropetsky Konstantin Victorovich , Borisov Gleb Alexandrovich , Samoilov Mikhail Ivanovich , Zharkov Artem Vladimirovich
Published: September 2020

This paper considers methodology aspects for constructing one-dimensional (1D) geomechanical models and stability calculations for the case of viscoplastic behavior of rock under stress with a case study of the Vikulovskaya sandstone from the Em-Egovskoe Field in West Siberia.