bluuebunny/crossref_metadata_2025_split huggingface dataset:
Virtual Flowmetering for Intelligent Wells
Authors: Danilko Aleksej , Senkov Alexandr , Andreev Alexey 
Published: October 2019

Abstract
Intelligent wells are equipped with tools for production control; however, they often do not answer the question of production zones allocation. Effective monitoring is only possible with an understanding of the inflow profile along the well; but, physical flowmeters are not cheap and are not always profitable. This work considers the use of virtual flowmeters for the purpose of understanding the profile of fluid flow to the well.
An intelligent horizontal well with five production zones was considered. Each zone was equipped with pressure sensors and a flowmeter. On the surface, there is data of wellhead pressure and multiphase flow rates.
As part of this work, a model of fluid flow in the bottomhole zone, annular space, inflow control valves, and tubing were designed. The model was calibrated with real-time measured data: pressure in the annulus, liquid flow of each zone, as well as wellhead pressure and surface flow rates.
In cases where there is no physical flowmeter, but there are sensors in the tubing and annular space, this approach can be used to calculate the production from each zone. Such a tool, when connecting the model to a real-time data system, allows use of the available information as efficiently as possible to automate the process of monitoring and manage production as well as provide opportunities for proactive production monitoring systems.
In addition to virtual flowmetering, the method can be used to clarify reservoir parameters, such as reservoir pressure, permeability, and skin factor.
Field-Scale Automatic Facies Classification Using Machine Learning Algorithms
Authors: Kuvichko Alexander , Spesivtsev Pavel , Zyuzin Vladimir , Istomin Sergey , Kalistratov Alexey , Kuznetsov Maxim , Igitov Semen 
Published: October 2019

Abstract
We propose a novel approach to facies classification based on a supervised machine learning algorithm. This approach allows for the automatic facies classification on a field scale based on an ensemble of Decision Trees algorithm associated with gradient boosting. Major steps of the workflow include data integrity assessment, data scaling, identification and correction of gaps in data, log processing, feature engineering, training, testing, and tuning the hyperparameters on the validated set of data. At the ultimate stage of the workflow, the algorithm accepts a set of well logs as an input and produces a discrete facies type as an output. This method substantially increases the quality of the facies classification, that is key to further geological modelling and dynamic simulation that help reduce drastically the risk of incorrect well planning, fracturing and other operations, thus avoiding a huge negative financial impact. The novelty of approach is related to the selection of machine learning algorithms that are best fitting the dataset, combined with a workflow to enhance the dataset itself.
Reduced Order Reservoir Simulation with Neural-Network Based Hybrid Model
Authors: Temirchev Pavel , Gubanova Anna , Kostoev Ruslan , Gryzlov Anton , Voloskov Dmitry , Koroteev Dmitry , Simonov Maxim , Akhmetov Alexey , Margarit Andrey , Ershov Alexander 
Published: October 2019

Abstract
This paper considers the development of a computationally fast model for simulation of multiphase flow in porous media for a heterogeneous reservoir with the unlimited number of wells characterized by a different type of completion. This fast solution has been obtained by means of replacing the differential equation governing the flow in porous media by approximate governing equations which are parametrized by convolutional neural networks. The matching of the dynamic properties of the original and reduced models is ensured by conservation of spatial invariance property of the equations. The suggested approach is characterized by the minimal number of limitations and shortcomings related to geological-hydrodynamical structure and size of the original model. Also, there is no necessity of additional model training for reservoirs not included in a training dataset. Suggested approach has been evaluated on the synthetic benchmark test model SPE10, where a significant decrease in computational time has been demonstrated comparing to a traditional commercial reservoir simulator. Based on the results of all demonstrated test case scenarios, it could be noted that hybrid hydrodynamic modeling leads to a significant reduction in computational cost (by a factor of few hundreds), maintaining at the same time required accuracy of calculations.
Integrated Asset Modelling and Automated Workflows from Pre-Production to Full Field Development Stage for a Giant Green Field West Qurna-2
Authors: Kunakbayeva Liliya , Gauder Denis 
Published: October 2019

Abstract
The West Qurna-2 field is one of the biggest undeveloped oil fields in Iraq. The development of the field is handled by the Iraqi Basra Oil Company and a consortium between LUKOIL and the National Iraqi North Oil Company. The production of the field started in 2014. This paper describes how the integrated asset model (IAM) and automated workflows have been used to support the field's development from first oil to full field development.
Along the field's development stages, the IAM system has assisted LUKOIL in its operations.
Its availability for first oil allowed refining the assumptions made about the productivity of the wells and reservoir characteristics for issuing realistic production profiles, hence playing a crucial role in achieving the production's targets.
Subsequently, the wells have been gradually converted to artificial lift (electrical submersible pumps) and the IAM system has allowed for extension of the natural flowing mode of the wells, optimization of workover operations, short term optimization of the ESP operations and extension of their lifetime.
Today, the IAM system allows for mitigating the water handling constraints through tailor-made field-wide optimization algorithms. The latter optimizes not only the field operations through integrated asset planning but also the water production and injection system continuously.
Overall, the IAM system has proven to be instrumental for the development and optimisation of West Qurna II. It has allowed to de-risk the initial production, optimize the workovers in the field, enhance the ESP performance and lifetime, optimize the pressure maintenance systems, etc. It has also provided a platform to empower multi-disciplinary teams.
The saving figures presented in this paper alone amount to around 7 million dollars in 2015-2016 and it is not unreasonable to ultimately expect total savings of tens of million dollars. Such savings make the implementation of IAM systems very attractive for similar green field operations.
LUKOIL has made critical choices for ensuring the success of the initiative, such as allowing for customization and flexibility of its IAM system, putting the necessary efforts in developing and maintaining accurate hydraulic models and starting simple and keeping focusing on the continuous and organic development and improvement of its IAM system.
This paper demonstrates how IAM systems can be practically used for optimization activities in a green field, and the associated direct benefits. It also details how to maintain such systems continuously up to date for a field in development phase, where the layout is continuously changing. A series of best practices are identified for ensuring successful embedment of these advanced approaches within the day-to-day operations.
Applying Machine Learning Methods to Search for Missing Net Pay Zones in Mature Oilfields Wells
Authors: Egorov Dmitry Vitalievich , Sabirov Arthur Rustamovich , Osmonalieva Oksana Taalaevna , Belozerov Boris Vladimirovich , Reshytko Alexander Alexandrovich , Klenitskiy Anton Nikolaevich , Shchepetnov Arseniy Andreevich , Semenikhin Artyom Sergeevich 
Published: October 2019

Abstract
The main aim of this work is to develop new approaches for processes of searching previously missed potential net pay intervals within wells from well log data of brownfields with machine learning algorithms. The practical interest for such a solution is an extraction of additional oil potential recovery from field data in order to prolongate development and production life of mature fields through development of system capable to automatically make recommendations on previously misinterpreted intervals.
Besides application of cognitive technologies the developed methodology includes also a workflow of cross-functional interaction of experts from different disciplines (geologist, petrophysicists) between each other and digital system. The system is based on modern deep learning architectures including convolutional and recurrent artificial neural networks. It utilizes well log data and corresponding interpretation of many specialists in order to accumulate or digitize a vast amount of previous experience. It can be used for the following robust re-interpretation of data in those parts of wells which were not previously considered in terms of net pay intervals but have high potential for new oil saturated thicknesses according to geological conditions and previous experience of manual investigation of such intervals.
The field test on a basis of described methodology was conducted on assets of Gazpromneft PJSC in Yamanlo-Nenets Autonomous Region, Western Siberia. The comprehensive volume of geological and geophysical information from oilfield was collected including well log data and corresponding results of expert interpretation. This information is used for model training and then predictions about previously uninterpreted intervals are made, providing business user a new interpretation of target geological objects. New interpretation produced by model was compared with current manual interpretation and new net pay intervals were considered as previously missed and potentially oil saturated. At the next step those intervals were examined by petrophysicists, geologists and reservoir engineers in order to estimate probability of oil saturation. Intervals with highest expert marks were proposed for field work and tested by perforation of the target zone. As a result of described process new net pay intervals were found and well, which was suspended, started a new production life. Obtained results confirm high potential of machine learning models application for search of new potential net pay intervals by helping an expert in daily geological and petrophysical tasks.
Analysis of Flow Characteristics in Porous Media with Heterogeneity at Microscale and Macroscale
Authors: Gerke Kirill M. , Isaeva Anna V. , Karsanina Marina V. , Shaporenko Elena V. , Korost Dmitry V. 
Published: October 2019

Abstract
Hydrodynamic or oilfield scale models are needed to predict long-term the production process, to design and optimise well placement and evaluate the outcomes of different enhanced recovery practises. These models operate using continuum medium approximations and upscaled effective porous media properties such as permeability, relative permeability and capillary curves and are conventionally based on (multiphase generalization of) Darcy and continuity equations. In this work we explored the difference between flow patterns on the Darcy scale depending on how we parameterized the model – using scalar permeability values, or based on pore-scale simulations on 3D pore geometries of real rock samples. In the latter case two permeabilities were utilized – conventional directional permeability (in three directions) and full permeability tensor. Our results strongly support the idea that full tensors are needed to accurately represent fluxes at Darcy scale then upscaling from core-scale measurements or simulations, or even if using geologic/geostatistical model (with spatial heterogeneous distribution of flow properties). More specifically, our upscaled Darcy scale model parameterized using hypothetical scalar or pore-scale simulated permeabilities revealed: even a simple scalar permeability field, if heterogeneous, produces significant off-diagonal terms in tensorial permeability results; using tensoral permeabilities when upscaling from the core-scale results in significant difference in upscaled (tensorial) permeabilities compared to conventional directional simulation framework.
Mathematical Model for Describing the Growth of Mini-Fractures in a Fractured Medium
Authors: Bykov Aleksander , Bychkov Alexey , Nevmerzhitskiy Yan , Zavialov Ivan , Zavialova Natalia , Elmira Salimgareevs , Yemchenko Olga 
Published: October 2019

Abstract
The main feature of fractured porous reservoirs, such as carbonates, is the presence of cracks of all scales, from the micro level to the main cracks that can cross the whole field. In connection with this feature, and beacause of the influence of formation perturbations on the geometric parameters of cracks, the interpretation of minifrac is a difficult task, since leak-off depends in a complex way on pressure. In this regard, the actual task of building a mathematical and numerical model that could be able to take into account the many processes that affect the anomalous dynamics of the bottomhole pressure.
As a resul, a physico-mathematical model, which describes the opening of pre-specified cracks in the three-dimensional case, as well as the generation of new cracks on the basis of the fracture criterion was constructed. Filtration in the matrix and the cracks on a small scale was considered in in obedience to the double porosity model – double permeability. The features of multiphase filtration of a power-law fluid were taken into account. The calculation of geomechanical equilibrium was carried out according to the model associated with the method of calculating frames and trusses.
Reservoir Flow Connectivity Estimation in Geomodels and New Capabilities of Geomodels using this Parameter
Authors: Popov Victor , Podnebesnykh Alexander , Pylnik Sergey 
Published: October 2019

Abstract
The main aim of the work is to find method to estimation flow connectivity without model simulator. The relations between flow connectivity and various reservoir geometry parameters were studied. Average number of reservoir intervals, intermittence, and reservoir boundary density parameter are not directly related with flow connectivity. Cluster proportion and net to gross ratio can be used with limitations for flow connectivity estimation when stochastic heterogeneity is low. The best estimation can be done by using neighbor reservoir parameter which correlation coefficient with flow connectivity is 0.99 and regression parameters are independent of reservoir modelling method and its options (variogramm range, nugget, net to gross ratio, object shape etc.). New method of flow connectivity coefficient estimation allows making local connectivity estimation and maps this parameter in order to evaluate potential flow rates of different regions of reservoir. Also flow connectivity should be used to rank realizations of lithology or net to gross properties in geological models in uncertainty analysis as a parameter which reflects production properties of reservoir.
Application of CRM-Like Models for Express Forecasting and Optimizing Field Development
Authors: Olenchikov Dmitry , Posvyanskii Dmitry 
Published: October 2019

Abstract
Lately, demand has increased for simplified field models (compared to 3D hydrodynamics) allowing to calculate a lot of production variants. One of promising approaches is CRM (capacity resistive model). Currently, several model classes are offered. The work is designed to improve CRM method for operative forecast of production indicators in active aquifer and in case of significant change of operation modes in wells. We have shown that the influence coefficients (of wells) are settable parameters of classic CRM, sensitive to changing the operation modes in wells. It limits CRM efficiency for tasks where well operation modes can change significantly, for instance, in waterflooding optimizing (WFO). The work offers the following CRM method improvements: using productivity coefficients instead of well influence coefficients;methods of direct reporting in equations of CRM-like model: influence of aquifer and crossflows between separate draining volumes.
The offered improvements will allow increasing the forecast performance of the model using productivity coefficients and direct reporting of aquifer and crossflows.
We have provided examples comparing CRM forecasts with the results of calculation of synthetic reference flow models. We have provided an example of application of the offered approach to waterflooding optimizing of a real oil field.
Evaluation of the Saturating Fluid Effect on the Composite Materials Elastic-Strength Properties
Authors: Pavlov Valeriy , Pavlyukov Nikolay , Krasnikov Artem , Lushev Mikhail , Eltsov Timofey 
Published: October 2019

Abstract
Effective assessment of the stress-strain state of the near wellbore zone is one of the key problems in the process of modeling the stability of the wellbore walls. Drilling mud infiltrates permeable rocks during the drilling process. This causes a change in the elastic-strength properties of rocks and, accordingly, the redistribution of tension around the well. At present, there are no computational methods that take into account the effect of saturation fluids on the change in the elastic-strength properties. A unified system approach for the implementation of this type of research when changing infiltration fluids is not developed yet.
In this paper, we study the effect of various types of drilling mud on the elastic-strength properties of core samples, which are equivalents of rocks (composite samples made of different sand and clay cement facies). Measurements of porosity, acoustic properties, ultimate strength for uniaxial compression, and static Young's modulus at different samples saturation are made. Studies of the elastic-strength properties of the samples are performed after 48 and 168 hours soaked in the drilling fluids. According to the study, the relative change in the dynamic Young's modulus with various sample saturation is 13.4-27.7%, the static young modulus (compression) is 19-40%, the dynamic Poisson ratio is 1.4-14.6% and the uniaxial compression strength is 28-35%. The data obtained indicate a significant effect of the saturating fluid on the elastic and strength properties of materials.
A numerical one-dimensional simulation of the stability of the borehole walls is performed, taking into account the type of saturating fluid and the relative change in the elastic-strength properties. The results indicate a change in the stability of the wellbore walls; the indicators of the change in the equivalents of the collapse gradient and hydraulic fracturing are 0.2-0.3 g / cm3. A change in Young's modulus of 30% affects the design parameters of a hydraulic fracturing fracture – by width up to 100%, by half length up to 50%.
Consideration of Elastic Properties and Stresses Anisotropy in Fracturing Planning
Authors: Krasnikov Artem , Melikov Ruslan , Pavlov Valeriy , Pavlyukov Nikolay , Subbotin Mikhail 
Published: October 2019

Abstract
Development of complex-build oil and gas reservoirs is associated with advanced technologies such as horizontal wells drilling and multi-stage hydraulic fracturing. Geomechanical modeling for hydraulic fracturing purposes is a fundamental tool for assessing technological constraints and risks, as well as increasing efficiency of reservoir treatment.
In proposed approach of horizontal stresses modeling and calibration to the actual hydraulic fracturing data additional features considered to compensate low contrast of Poisson's ratio calculated from broadband acoustics: elastic properties TIV-anisotropy, variation of Biot coefficient adjusted to mechanical facies, correlations between static elastic properties and petrophysical parameters based on core measurements.
Lab measurements on oriented core samples revealed elastic properties anisotropy that caused difference of the static young's modulus parallel and perpendicular to the formation bedding up to 80 – 100%, and for the Poisson's ratio is up to 10 – 20%. Considering these results stress calculation leads to a difference between an isotropic and anisotropic profile up to 20%, this has significant impact on the hydraulic fracture geometry.
The rock behavior under load is different and is determined by the properties of the rock grains and the contact between them. Thus, the section separation into mechanical facies plays an important role when estimating elastic parameters, including Biot coefficient (α), which is different for shales, sand and/or carbonate, for example. Correct estimation of α with respect to mechanical facies allows achieving good agreement between stress calculation and actual measurements obtained with a mini-frac job, thereby increasing fracture geometry prediction accuracy.
Another tool to compensate lack of stress contrast calculated based on standard 1D geomechanical workflow is the use of petrophysical parameters such as porosity, clay content, neutron density and its correlation with static elastic properties to estimate minimum horizontal stress. This method may improve geomechanical model matching with field observations, but it has a limited scope of application.
In this paper demonstrated that additional study of the rock properties with special logging and core measurements at initial phase of field development planning may significantly reduce geomechanical modeling uncertainties and improve understanding of hydraulic fracturing and fracture geometry, which is a basis for hydrocarbon production and economic evaluation of the project or the whole asset.
The paper presents adapted geomechanical modeling workflow based on special lab core testing and elastic properties anisotropy and Biot coefficient evaluation to adjust the horizontal stresses profiles in complex-build reservoirs to the field measurements and observations as well as to fracturing data in existing wells.