bluuebunny/crossref_metadata_2025_split huggingface dataset:
PROGNOSTIC FACTORS OF RELAPSE IN THE SURGICAL TREATMENT OF DESMOID FIBROIDS OF THE TRUNK AND EXTREMITIES
Authors: Yadrina A. V. , Karpenko V. Yu. , Novikova O. V. , Bychkova N. M. , Derzhavin V. A. , Bucharov A. V. 
Published: April 2019

Purpose of the study. Evaluation of the results of surgical treatment of desmoid fibroids (DF) using reconstructive plastic surgery and determination of possible prognostic factors of relapses.Patients and methods. The results of surgical treatment of 40 patients with desmoid fibroids of extra abdominal local­ization were analyzed. In 45% of patients, the tumor was located in soft tissues of the shoulder girdle and upper limb, in 35% — on the trunk, in 20% — in soft tissues of the lower limb and in 7% of patients on the neck. The tumor size varied from 2 to 20 cm, the median was 8 ± 4.38 cm. Surgical treatment for newly diagnosed DF was performed in 22 (55%) patients, and for recurrent tumors — in 18 (45%) patients.Results. According to the planned histological study, negative resection boundaries were established in 24/40 patients (63%). In the remaining 16 cases (37%) positive resection margins were obtained: R1 in 14 patients and R2 in 2 patients. Adjuvant treatment is recommended for 14 patients with resection R1/R2. The duration of follow — up after treatment ranged from 6 to 107 months, median‑16.5 months. During follow-up, relapses were diagnosed in 14 patients (35%). No deaths have been reported.Conclusion. Surgical treatment of DF of extraabdominal localization is accompanied by a sufficiently high frequency of relapses. According to the results of the study, an unfavorable factor in the development of relapses is the location of the tumor in soft tissues of the shoulder girdle and upper limb.
COMPARATIVE ANALYSIS OF CONVENTIONAL AND LIQUID-BASED CYTOLOGICAL METHODS FOR CERVICAL SMEARS
Authors: Volchenko N. N. , Sushinskaya T. V. , Borisova O. V. , Melnikova V. Yu. , Petrov A. N. 
Published: April 2019

Purpose. Comparative analysis of the diagnostic value of traditional cytology (TC) and liquid cytology (LC) in the identi­fication of cervical intraepithelial neoplasia (CIN) and cervical cancer (CC).Patients and methots. The study included 87 women aged 18 to 80 years. All women were referred with suspicion of CIN, or underwent a control cytological examination after treatment for CIN and cervical cancer. Smears were taken sep­arately from ecto-, endocervix and mixed. Various tools were used (trowel, brushes type D, F and Cervex-Brush Combi). Each patient was sampled at the same time by different types of instruments. All surveyed women were performed at the same time traditional and fluid cytology. Liquid preparations were prepared using SurePath™ technology (BD and Company, Netherlands, USA) and E‑Prep (Biodyne, South Korea). Traditional cytological preparations were stained ac­cording to the Pappenheim method, liquid preparations — by Papanicolaou. A comparative analysis of cytological and histological findings was carried out.Results. According to our data, the number of non-informative material practically coincides in the shopping center and in the LC, from the instruments used the best results were obtained when using a cytobrush of type D and a wood­en spatula. The frequency of detection of CIN squamous epithelium in the LC is somewhat less than in the shopping center (60% vs. 62%). This is due to the lack of experience in evaluating the life cycle drugs. Despite the fact that the overall frequency of CIN detection is somewhat lower in the LC than in the TC, the frequency of detection of high grade squamous intraepithelial lesion (HSIL) is higher in the LC (51% versus 46%). This is due to the fact that cellular elements are concentrated in a limited area and single small HSIL complexes that are missed in the shopping center are better detected in the LC. The sensitivity of the TC smear from the cervix was 96.2%, life cycle — 92.4%. The accuracy of the shopping center is 92%, the life cycle is 89.6%.Conclusion. LC is an alternative to traditional cytological examination in order to detect pretumor diseases and cervical cancer. For an objective assessment of liquid preparations, additional training of cytologists is necessary.
The Role of Myofibroblasts and Mast Cells in Oral Mucosa Repair After Fractional Laser Treatment
Authors: Sergeeva E. S. , Gusel'nikova V. V. , Ermolaeva L. A. , Belikov A. V. , Fedotov D. Yu. , Sufieva D. A. , Semyashkina Yu. V. , Antropova M. M. , Korzhevskii D. E. 
Published: April 2019

The aimof this study was to clarify the features of the reparative process of the rat's oral mucosa in the later periods after fractional laser treatment with an analysis of the possible involvement of mast cells and myofibroblasts in this process.Material and methods.The samples of the oral mucosa of male Wistar rats (n=9) were used as a material for this study. Fractional laser treatment was carried out using stLase (DentalPhotonics, USA) with power P=7–10 W and pulse duration tp=100–200 ms (wavelength 980 nm). Histological sections of the oral mucosa from the control zones (unaffected) and zones treated with laser radiation were stained with hematoxylin and eosin and with Masson’s aniline blue. To identify mast cells, toluidine blue was used. For the detection of blood vessels and myofibroblasts, immunohistochemical reaction to smooth-muscle α-actin was performed.Results.On the 28th day after fractional laser treatment in rat oral mucosa the signs of incomplete repair were present. At this period in laser treated areas within the lamina propria rows of densely adjacent myofibroblasts were found. In the rows of myofibroblasts the mast cells are not visualized, while in the similar areas of the intact mucosa mast cells are present in large numbers.Conclusion.Fractional laser treatment stimulates the prolongation of regeneration process of rat oral mucosa while formation of myofibroblasts occurs in the laser treated zone. During this process mast cells may be involved. The ability to degranulate under laser radiation was shown.
Estimation of Drained Rock Volume DRV and Pressure Depletion using Discrete Fracture Model and Complex Analysis Methods
Authors: Khanal Aadi , Weijermars Ruud 
Published: April 2019

Abstract
The objective of this study is to visualize the drained rock volume (DRV) and pressure depletion in hydraulically and naturally fractured reservoirs, using a high-resolution simulator to plot streamlines and time-of-flight contours that outline the DRV, based on computationally efficient complex potentials. A recently developed expression based on fast, grid-less Complex Analysis Methods (CAM) is applied to model the flow through discrete natural fractures with variable hydraulic conductivity. The impact of natural fractures on the local development of DRV contours and streamline patterns is analyzed. A sensitivity analysis of various permeability contrasts between natural fractures and the matrix is included. The results show that the DRV near hydraulic fractures is significantly affected by the presence of nearby natural fractures. The DRV location shifts according to the orientations, permeability and the density of the natural fractures. Reservoirs with numerous natural fractures result in highly distorted DRV shapes as compared to reservoirs without any discernable natural fractures. Additionally, the DRV shift due to natural fractures may contribute to enhanced well-interference by flow channeling via the natural fractures, as well as the creation of undrained rock volumes between the natural fractures. Complementary pressure depletion plots for each case show how the local pressure field changes, in a heterogeneous reservoir, due to the presence of natural fractures. The results from this study offer insights on how natural fractures affect the DRV and pressure contour plots. This study uses a fast grid-less and meshless high-resolution flow simulation tool based on CAM to simulate the flow in heterogeneous naturally fractured porous media. The CAM tool provides a practical/efficient simulation platform, complementary to grid-based reservoir simulators.
Analytical Solution of Chemical Flooding in Heterogeneous Non-Communicating Layers With a Focus on Low Salinity Water Flooding
Authors: Al-Ibadi Hasan , Stephen Karl , Mackay Eric 
Published: April 2019

Abstract
As an enhanced oil recovery method (EOR), chemical flooding has been implemented intensively for some years. Low Salinity WaterFlooding (LSWF) is a method that has become increasingly attractive. The prediction of reservoir behaviour can be made through numerical simulations and greatly helps with field management decisions. Simulations can be costly to run however and also incur numerical errors. Historically, analytical solutions were developed for the flow equations for waterflooding conditions, particularly for non-communicating strata. These have not yet been extended to chemical flooding which we do here, particularly for LSWF. Dispersion effects within layers also affect these solutions and we include these in this work.
Using fractional flow theory, we derive a mathematical solution to the flow equations for a set of layers to predict fluid flow and solute transport. Analytical solutions tell us the location of the lead (formation) waterfront in each layer. Previously, we developed a correction to this to include the effects of numerical and physical dispersion, based on one dimensional models. We used a similar correction to predict the location of the second waterfront in each layer which is induced by the chemical's effect on mobility. In this work we show that in multiple non-communicating layers, material balance can be used to deduce the inter-layer relationships of the various fronts that form. This is based on similar analysis developed for waterflooding although the calculations are more complex because of the development of multiple fronts.
The result is a predictive tool that we compare to numerical simulations and the precision is very good. Layers with contrasting petrophysical properties and wettability are considered. We also investigate the relationship between the fractional flow, effective salinity range, salinity dispersion and salinity retardation.
This work allows us to predict fluids and solute behaviour in reservoirs with non-communicating strata without running a simulator. The recovery factor and vertical sweeping efficiency are also very predictable. This helps us to upscale LSWF by deriving pseudo relative permeability based on our extension of fractional flow and solute transport into such 2D systems.
Slug Flow Monitoring in Pipes Using a Novel Non-Intrusive Optical Infrared Sensing Technology
Authors: Sarkodie Kwame , Fergusson-Rees Andrew , Makwashi Nura , Diaz Pedro 
Published: April 2019

Abstract
The application of real - time monitoring technologies presents a means to harnessing proactive or reactive controls in minimizing severity effects of slugging in the production system. This paper presents the development of a non-intrusive optical infrared sensing (NIOIRS) setup, for slug monitoring in pipes. The flow characteristics monitored were the development of slug flows and average phase fractions of gas and liquid in a vertical test section (0.018m by 1m) for superficial velocities of 0-0.131 m/s for water and 0 – 0.216 m/s for air. The measurement principle was based on the disparities in refractive indices of each phase in the sensing area. The sensing component of the sensor consisted of two pairs of IR emitters and photodiodes operated at wavelengths of 880 nm specifications. A circuit, for signal conditioning, amplification and data acquisition was set up to convert infrared light detected into voltage signals. Development of slug flow regimes was monitored from signal distributions binned under reference voltages. The transitions from bubble to slug flow, were observed at 10 percent count of the signal distributions around typical sensor response for air. Validation from photos showed good agreements with the sensor response. A single peaked distribution around the response for water indicated bubble flow regimes, with the development of two peaks indicated increasing gas slugs for increasing superficial gas velocities compared to liquid slug in the pipe. Phase fraction results were interpreted from a derived calibration models, which were based on the average observed response and reference responses of water and air over time. This model was compared with swell level changes, photographs and homogenous and drift flux correlation with agreement within maximum error bands +/− 0.5 % based on the swell level method and +/− 0.3% based on photographs. The Real-time application was carried out via the execution of an algorithm which incorporated the calibration information from the NIOIRS. The derived signals were processed and analyzed onto a display to identify slug flow development and phase fractions in real-time. A cheap and accurate sensing setup has been developed with the potential of real time monitoring of flow regimes and phase fraction determination.
A Machine Learning Approach to Predict Gas Oil Ratio Based on Advanced Mud Gas Data
Authors: Yang Tao , Arief Ibnu Hafidz , Niemann Martin , Houbiers Marianne , Meisingset Knut Kristian , Martins Andre , Froelich Laura 
Published: April 2019

Abstract
Mud gas data from drilling operations provide the very first indication of the presence of hydrocarbons in the reservoir. It has been a dream for decades in the oil industry to predict reservoir gas and oil properties from mud gas data, because it would provide knowledge of the reservoir fluid properties in an early stage, continuously for all reservoir zones, and at low costs. Previous efforts reported in the literature did not lead to a reliable method for quantitative prediction of the reservoir fluid properties from mud gas data. In this paper, we propose a novel approach based on machine learning which enables us to predict gas oil ratio (GOR) from advanced mud gas (AMG) data.
The current work is based on a previous successful pilot in unconventional (shale) reservoirs. Our aim is to extend the results of the pilot study to conventional reservoirs. In general, prediction of reservoir fluid properties is more challenging for conventional reservoirs than for unconventional reservoirs, due to the complexity of petroleum systems in conventional reservoirs. Instead of building a model directly from AMG data, we trained a machine learning model using a well-established reservoir fluid database with more than 2000 PVT samples. After thorough investigation of compositional similarity between PVT samples and AMG data, we applied the model developed from PVT samples to AMG data.
The predicted GORs from AMG data were compared with GOR measurements from corresponding PVT samples to assess the accuracy of the GOR predictions. The results from 22 wells with both AMG data and corresponding PVT samples show large agreement between prediction vs. measurement. The accuracy of the predictive model is much higher than previous results reported in the literature. In addition, a Quality Check (QC) metric was developed to efficiently flag low-quality AMG data. The QC metric is vital to give confidence level for GOR prediction based on AMG data when PVT samples are not available.
The study confirms that AMG data can be used as a new data source to quantitatively predict continuous reservoir fluid properties in the drilling phase. The method can be used to optimize wireline operations and for some cases, it provides a unique opportunity to acquire reservoir fluid data when conventional fluid sampling or use of wireline tools is not possible. After high-quality PVT data becomes available in the wireline logging phase, the continuous GOR prediction can be further improved and used to determine reservoir fluid gradient and reservoir compartmentalization.
A 4D Small Data Solution in a Deepwater Gulf of Mexico Seismic-Driven History Matching Workflow
Authors: Thibodeaux Brandon , Gao Chao , Ramsay Travis , Segovia Felix , Hernandez Luisalic , Vora Harsh 
Published: April 2019

Abstract
Time-lapse seismic monitoring is a powerful technique for reservoir management and the optimization of hydrocarbon recovery. In time-lapse seismic datasets, the difference in seismic properties across different vintages enables the detection of spatio-temporal changes in saturated properties and structure induced by production. The main objectives are (1) to identify bypass pay zones in time-lapse seismic data for the deepwater Amberjack field, located in the Gulf of Mexico, (2) confirm the identified bypass pay zones in the results of reservoir simulation, and (3) recommend well planning strategies to exploit these bypassed resources.
A high-fidelity seismic-to-simulation 4D workflow that incorporates seismic, petrophysics, petrophysical property modeling, and reservoir simulation was employed, which leveraged cross-discipline interaction, interpretation, and integration to extend asset management capabilities. The workflow addresses geology (well log interpretation and framework development), geophysics (seismic interpretation, velocity modeling, and seismic inversion), and petrophysical property modeling (earth models and co-located co-simulation of petrophysical properties with P-impedance from seismic inversion). An embedded petro-elastic model (PEM) in the reservoir simulator is then used to affiliate spatial dry rock properties with saturation properties to compute dynamic elastic properties, which can be related to multi-vintage P-impedance from time-lapse seismic inversion. In the absence of the requisite dry rock properties for the PEM, a small data engine is used to determine these absent properties using metaheuristic optimization techniques. Specifically, two particle swarm optimization (PSO) applications, including an exterior penalty function (EPF), are modified resulting in the development of nested and average methods, respectively. These methods simultaneously calculate the missing rock parameters (dry rock bulk modulus, shear modulus, and density) necessary for dynamic, embedded P-impedance calculation in the history-constrained reservoir simulation results. Afterward, a graphic-enabled method was devised to appropriately classify the threshold to discriminate non-reservoir (including bypassed pay) and reservoir from the P-impedance difference. Its results are compared to unsupervised learning (k-means clustering and hierarchical clustering). From seismic data, one can identify bypassed pay locations, which are confirmed from reservoir simulation after conducting a seismic-driven history match. Finally, infill wells are planned, and then modeled in the reservoir simulator.
Small Data Driven Metaheuristic Petro-Elastic Model Development Using Seismic Inversion
Authors: Ramsay Travis 
Published: April 2019

Abstract
The petro-elastic model (PEM) represents an integral component in the closed-loop calibration of integrated four-dimensional (4D) solutions incorporating time-lapse seismic, elastic and petrophysical rock property modeling, and reservoir simulation. Calibration of the reservoir simulation model is needed so that it is not only consistent with production history but also with the contemporaneous subsurface description as characterized by time-lapse seismic. The PEM requires dry rock properties in its description, which are typically derived from mechanical rock tests. In the absence of those mechanical tests, a small data challenge is posed, whereby all necessary data is not available but the value of reconciling seismic attributes to simulated production remains. A seismic inversion-constrained n-dimensional metaheuristic optimization technique is employed directly on three-dimensional (3D) geocellular arrays to determine elastic and density properties for the PEM embedded in the commercial reservoir simulator.
Ill-posed dry elastic and density property models are considered in a field case where the seismic inversion and petrophysical property model constrained by seismic inversion exist. An n-dimensional design optimization technique is implemented to determine the optimal solution of a multidimensional pseudo-objective function comprised of multidimensional design variables. This study investigates the execution of a modified particle swarm optimization (PSO) method combined with an exterior penalty function (EPF) with varied constraints. The proposed technique involves using n-dimensional design optimization to solve the pseudo-objective function comprised of the PSO and EPF given limited availability of constraints. In this work, an examination of heavily and reduced-order penalized metaheuristic optimization processes, where the design variables and optimal solution are derived from 3D arrays, is conducted so that constraint applicability is quantified. While the process is examined specifically for PEM, it can be applied to other data-limited modeling techniques.
4D Seismic History Matching Incorporating Unsupervised Learning
Authors: Etienam Clement 
Published: April 2019

Abstract
The work discussed and presented in this paper focuses on the history matching of reservoirs by integrating 4D seismic data into the inversion process using machine learning techniques. A new integrated scheme for the reconstruction of petrophysical properties with a modified Ensemble Smoother with Multiple Data Assimilation (ES-MDA) in a synthetic reservoir is proposed. The permeability field inside the reservoir is parametrised with an unsupervised learning approach, namely K-means with Singular Value Decomposition (K-SVD). This is combined with the Orthogonal Matching Pursuit (OMP) technique which is very typical for sparsity promoting regularisation schemes. Moreover, seismic attributes, in particular, acoustic impedance, are parametrised with the Discrete Cosine Transform (DCT). This novel combination of techniques from machine learning, sparsity regularisation, seismic imaging and history matching aims to address the ill-posedness of the inversion of historical production data efficiently using ES-MDA. In the numerical experiments provided, I demonstrate that these sparse representations of the petrophysical properties and the seismic attributes enables to obtain better production data matches to the true production data and to quantify the propagating waterfront better compared to more traditional methods that do not use comparable parametrisation techniques.