bluuebunny/crossref_metadata_2025 huggingface dataset:
Los viajes de las personas residentes en Canarias: turismo y movilidades
Authors:   Universidad de La Laguna (España), Santana Turégano Manuel Ángel
Published: February 2022

This paper analyzes domestic tourism in the Canary Islands, using secondary data and following an approach based in the mobilities paradigm, using the night stays as the main indicator. The analysis shows that the two most populated islands differ in their propensity to consume lodging services, and these differences are considered to be due to both cultural and commercialization reasons. Due to the scarce utilization of statistics already available, and the difficulties that stem from trying to analyze phenomena such as night individuals spent in tourism facilities in the same island where they live, it seems that adopting an approach based in the mobilities paradigm would be beneficial for better understanding phenomena traditionally associated in the Canary Islands with tourism
Hacia una explotación de la encuesta de estructura salarial en hostelería desde el análisis exploratorio de datos con aplicación a la brecha salarial de género
Authors:   UNED (España), Acevedo Blanco Antonio Jesús , Martínez Quintana Violante ,   UNED (España)
Published: February 2022

This paper reviews the possibilities presented by the Quadrennial Salary Structure Survey (EES), especially for economic and sociological studies in the spanish tourist sector, using exploratory data analysis (EDA) of the salary structure in Hospitality. The article is divided into two different parts, first, the instructions and the structure of the data for the correct exploitation of the survey are reviewed. Second, an EDA exercise is carried out in the hospitality economic sector where salary and geographical differences are tested, by economic sector, age and sex cohorts. Finally, after the analysis carried out, it is concluded by highlighting the great analytical possibilities that the survey database can develop, and some confirmatory lines of analysis are offered that help to complement this exploratory introduction to the wage gap.
Turismo religioso en Uruguay. El caso de la festividad de San Cono
Authors:   Universidad de Las Palmas de Gran Canaria(España), Moreira Gregori Pedro Ernesto , Coraza de los Santos Enrique ,   Colegio de la Frontera Sur (México), Rosa Pablo de la ,   Universidad de la Repúblic (Uaruguay), Giannattasio Bruno ,   Universidad de la Repúblic (Uaruguay)
Published: February 2022

Although Uruguay is from its origins a secular state, most Uruguayans profess some kind of religion or participate in some religious demonstration. Within the framework of this phenomenon is that we present a study focused on popular catholic celebrations. Through qualitative methodologies, we analyze a phenomenon where mobility and religious tourism are combined, with feelings, faith and devotion. This case study is located in the city of Florida, Uruguay where the Chapel of San Cone is located. It is an Italian Saint born in the city of Teggiano: image introduced by Italian migrants in the late nineteenth century, it wakes up great devotion, being associated with luck and money, attributing miracles and healings to it.
Mucopolysaccharidosis type IVA in children: Clinical cases
Authors: Burlutskaya A. V. , Savel′eva N. V. , Naumenko G. V.
Published: February 2022

Background. Mucopolysaccharidosis type IVA (Morquio syndrome) is a rare genetic lysosomal storage disease. Due to rarity, the syndrome is typically diagnosed at a later stage of gross affections of musculoskeletal and central nervous systems, leading to disability and a markedly reduced quality of life. A replacement therapy is nowadays available with recombinant human N-acetylgalactosamine-6-sulfatase (elosulfase alfa) enzyme.

Clinical cases description. Two siblings, 10-yo male and 8-yo female, were admitted with complaints of growth retardation, deformity of the spine, thorax and joints, impaired hearing and visual acuity, poor tolerance to exercise. In the boy’s medical history, ﬁrst manifestations appeared in the ﬁrst year of life and progressed gradually; the patient was being observed as spondylodysplastic. Mental development was unaffected. The diagnosis was conﬁrmed only by age of 7 at the National Medical Research Center for Children's Health Federal State Autonomous Institution of the Ministry of Health of the Russian Federation. Genotyping revealed two SNP mutations in gene GALNS (g.88909227C>A and g.88884454G>A in heterozygous state), and enzymatic assays — a severely reduced N-acetylgalactosamin-6-sulfatase activity. A routine elosulfase alfa replacement therapy has been received since 8-year age.

The younger sister had neonatal cardiomegaly; congenital carditis and cardiomyopathy not excluded. Musculoskeletal affections developed by age of 3–4 years. By age of 5 and simultaneously with brother, the same GALNS mutations and severely impaired N-acetylgalactosamine-6-sulfatase activity were detected. A replacement therapy has been routinely received since 6-year age. The therapy triggered positive dynamics of restoring activity and muscle strength in both children, as well as signiﬁcantly abating the musculoskeletal affection progress.

Conclusion. The clinical cases of Morquio syndrome presented demonstrate its long-term and complex diagnosis. A replacement therapy is nowadays available, which warrants an earliest disease detection to halt progression and improve the patient’s life quality and expectancy.
Gender and age-speciﬁc eating behaviour in urban populations at risk of chronic noncommunicable diseases: an observational cohort study
Authors: Gorban V. V. , Menshikh V. S. , Gorban E. V.
Published: February 2022

Background. Nonrational nutrition is among main risk factors of chronic noncommunicable diseases (CNCDs) that can be contained via adopting and motivating population to a healthy diet by clarifying territorial, gender and age-speciﬁc eating behaviours.

Objectives. A study of urban gender and age-speciﬁc eating behaviours associated with universal and metabolic CNCD risk factors.

Methods. An observational cohort study has been conducted with 1,317 urban residents (657 men and 660 women) aged 18–74 years at Kuban State Medical University, Ministry of Health of the Russian Federation, using anonymous surveys on food preferences, tobacco and alcohol consumption, physical activity (PA), as well as physical examination, anthropometry, electrocardiography (ECG) and biochemical blood tests.

Results. Urban residents revealed low-physical activity (PA) (88%), overweight and obesity (33%), high tobacco (65%) and alcohol use (39%) in men, high metabolic risk by waist/hip circumference (33.7%) and signiﬁcant polymorbidity in hypertension (3 to 8 diseases per person). The urban dietary pattern can be described “intermediate” by the co-presence of rational and nonrational ingredients in diet. Rational ingredients prevailed in women and elderly people. Elevated total cholesterol, low-density lipoproteins, triglycerides, gamma-glutamyl transpeptidase, alanine aminotransferase, aspartate aminotransferase, bilirubin, uric acid, urea, creatinine and ultra-sensitive C-reactive protein in blood were associated with foods intake like chips and breadsticks, potatoes, sweet drinks, confectionery and borscht.

Conclusion. Adopting measures to rational nutrition in population may comprise a clear declaration of dysmetabolic nature of speciﬁc foods, eating behaviour monitoring and motivated self-control of body mass index and waist girth beyond medical check-ups.
Integration of Borehole Image Analysis, Open Hole Logs and Core Data to Unlock Stratigraphic & Sedimentological Analysis of a South Eastern Algeria Field.
Authors: Rezig Drifa , Hadjar Imane Ouarda , Nettari Ferhat , Hertz Matthew , Boukhenifra Messaoud , Bouacida Djamil , Hadjarab Hedi , Salmi Omar
Published: February 2022

The identification of the depositional environment is critical to the delineation and prediction of the best quality reservoir. It often starts with a detailed core description, which is based on number of features. Borehole image augments the core data by providing important information for orienting features, filling in gaps between cores, recognizing sedimentary structures and sorting for facies analysis and identifying depositional environments.

Oil-Based Micro-resistivity Images and Ultra-sonic Borehole Images were successfully applied to reservoir modeling by introducing a statistically correct model that obeys facies association rules and facies contacts and improves the dynamic history match.

The principal hydrocarbon reservoir of field-A in the South Eastern Algeria basin, TAGI, is part of a fluvial system. The microfacies were analyzed with a pseudo-grain size profile and textural analysis of the borehole resistivity and ultrasonic images. The TAGI sits unconformably on the Paleozoic rocks, it consists of variable sequences which have been sub-divided into four fluviatile cycles. The depositional environments are mainly amalgamated channels passing laterally to flood plain, crevasse splay and sheet flood deposits. The correlation of the cycles combined with empirical functions enabled estimating the geometry of the sandstone bodied for each deposition element. This is required input for object modeling.

This study showed the value of borehole image logs in sedimentology analysis, which progressed from single-well borehole image interpretation to conceptual model construction and channel dimension estimation.
Discrete Fracture Network Modeling of Saline Lacustrine Tight Oil Reservoirs Severely Deformed by Tibetan Plateau Neotectonism
Authors: Chen Xiaodong , Tong Yanming , Zhang Qinghui , Wu Chuan , Tan Chenqing , Gao Farun , Guo Ning , Tan Wulin , Zhang Gaige
Published: February 2022

The study area is located in Qaidam Basin on Tibetan Plateau, which has very complex geological background sedimentologically and structurally. Building the discrete fracture network (DFN) of study zone is a critical work step for subsequent dual porosity / dual permeability (Dp/Dp) dynamic simulation which further enables the optimized design of field development plan (FDP).

The saline lacustrine tight oil reservoirs in study zone were severely deformed by the Neotectonism coupling with the far-field collision between the Indian and Eurasian plates. There were relatively abundant fracture interpretations from image well logs with core calibration and relatively amplitude preserving seismic cube. First, statistics of well fractures were performed in terms of their types and attitudes, which enabled proposing their genetic mechanism together with information of other references and dividing fracture sets more reasonably for subsequent DFN modeling. Then, many efforts were made on fracture driver attribute screening with the aid of neural network (NN) and principal component analysis (PCA) functions. Dozens of possible fracture driver attributes were examined, which were mainly geometric attributes, edge-detection attributes, amplitude/frequency related attributes and lithology/porosity related attributes. Meanwhile, microseismic monitoring (MSM) data were also used to help screen the pertinent fracture drivers. Three fracture driver attributes were finally screened out and they were combined together to produce a fracture intensity trend which was finally used as co-kriging secondary variable together with well fracture intensity logs for modeling the 3D-grids of fracture intensity based on gaussian random function simulation algorithm.

Besides the existed fault model (as large-scale fractures) generated when building matrix geomodels, a medium-scale fracture set was built deterministically with "Fault Patch" extraction method based on the seismic edge-detection Ant Cube. Then for small-scale fractures, totally four fracture sets were built stochastically based on split 3D fracture intensity and their own parameters of geometry, orientation, aperture. These five fracture sets were then upscaled to produce the fracture properties with porosity and permeability constraints from wells, i.e., usually less than 0.1% and hundreds of millidarcies respectively. Then these fracture properties were further calibrated and used in Dp/Dp dynamic simulation process.

Genuine discrete fracture network and Dp/Dp dynamic simulation were built up and performed for the first time for the complex oil reservoirs on Tibetan Plateau, based on organic multi-disciplinary integration. But more importantly, many useful conclusions and decisions were reached based on the Dp/Dp dynamic simulation which made the field development plan more robust and cost-effective.
Machine Learning for Sonic Logs Prediction: A Case Study from the Niger Delta Basin in the Gulf of Guinea
Authors: Tahiru Ibrahim Tinni , Olagundoye Olatunbosun , Alabere Abdulquadri Olalekan
Published: February 2022

Sonic logs are very essential for rock type identification, hydrocarbon typing, rock physics modelling, and reservoir characterization. However, they are seldom available due to high costs of acquisition or measurement errors. Empirical formulas and petroelastic models that are often used to predict missing sonic log data may not produce accurate velocity profiles and are limited to specific geologic settings. Using sonic logs from wells in the CONDA field located in the deep offshore Niger delta basin of the Gulf of Guinea, we demonstrate that machine learning algorithms can be used to predict sonic log data if suitable quantitative relationships exists between it and available well logs. Preprocessing such as outlier removal, missing data filling and normalization was applied to the well logs before using them as training datasets for the model prior to applying several machine learning algorithms to build a predictor model for missing DTP and DTS sonic logs. The results of the training using several machine learning algorithms showed that the Gradient Boost Regressor (GBRT) Algorithm was more robust based on higher accuracy and lower root-mean-squared errors (RMSE). Validation of the prediction model at blind wells was quite good, with coefficient of determination or goodness-of-fit (R2) scores of 0.88 to 0.99 and generally low root mean square errors (RMSE). QC of the predictive model performed using qualitative well correlation analysis between a well with actual DTP and DTS sonic logs and another with predicted DTP and DTS sonic logs gave very satisfactory results based on similarities in log character and trend. The results of our study show that in comparison to sonic log prediction using empirical formulas and/or petroelastic models which is fraught with limitations, machine learning can be used as a robust alternative.
Conditioning Distributed Acoustic Sensing Measurements for Borehole Seismic Imaging
Authors: Sayed Ali Yawar , Khaitan Manish Lal , Martinez Alejandro
Published: February 2022

Distributed acoustic sensing (DAS) differs from particle velocity measurements in both amplitude and frequency response. DAS data are affected by the apparent velocity of the incident energy relative to the fiber as well as the gauge-length at which the fiber is interrogated. We develop a generalized relationship for dependence of the DAS amplitude response on the incident elastic waves in terms of the angle of wave vector, angle of polarization, and phase velocity. Velocity and DAS sensor synthetic data are generated to comprehensively evaluate the conditioning methods needed to prepare DAS data for imaging. DAS sensor images, using data-driven and model-driven approaches, are shown to be consistent with the reference velocity sensor image.
A Nonparametric Estimation Method for Acoustic Dispersion and Attenuation Analysis
Authors: Wang Bing , Li Wei
Published: February 2022

Dispersion and attenuation analysis can be used to determine formation anisotropy induced by fractures, or stresses. In this paper, we propose a nonparametric spectrum estimation method to get phase dispersion characteristics and attenuation coefficient. By designing an appropriate vector filter, phase velocity, attenuation coefficient and amplitude can be inverted from the waveform recorded by the receiver array. Performance analysis of this algorithm is compared with EPM and FBMP, based on the analysis, the proposed method is capable of achieving high resolution and precision as the parametric spectrum estimation methods in the meantime, it also keeps high stability as the other nonparametric spectrum estimation methods. At last, applications to synthetic waveforms modeled using finite difference method show its efficiency.
The Numerical Simulation Study on the Dynamic Variation of Residual Oil with Water Drive Velocity in Water Flooding Reservoir
Authors: Sun Zhaobo , Ma Kuiqian , Cai Hui , Gao Yue , Wang Lian , Jiang Ruizhong
Published: February 2022

The results of core displacement experiments show that increasing the water drive velocity when it is bigger than a limit value can effectively reduce the residual oil saturation and improve the oil displacement efficiency under the same PV. Meanwhile oilfield development experience have proved that raising liquid production rate can effectively enhanced oil recovery. However the existing commercial simulators (Eclipse, CMG, et al) cannot simulate the continuous variation of reservoir properties during the simulation process and the oil-displacement mechanism in high-velocity development is not characterized effectively.

In this paper, a new reservoir numerical simulator is established which captures the dynamic variation of residual oil with water drive velocity in water flooding reservoir, based on the black oil model. Capillary number(Ca), defined as dimensionless ratio of viscous force to capillary force, is used to characterize the relationship between water drive velocity and residual oil. Increasing the water drive velocity can improve viscous force to further increase the Ca. When the Ca is larger than a certain critical value (first critical value), larger Ca lead to lower residual oil saturation. until after the number of Ca continues to increase to a certain critical value (second critical value), the residual oil saturation no longer changes with increasing of Ca. A new BG equation is proposed to match the nonlinear relationship between Ca and residual oil. The BG equation is a continuous function, which is very beneficial to the stability of numerical calculation. Higher curve matching is obtained based on the BG equation than the existing models.

The validation results of the new simulator against the commercial simulators showed that good consistence was obtained between the new self-developed numerical simulator and Eclipse. With consideration of the influence of residual oil change in the new self-developed numerical simulator based on BG equation, the results indicate lower remaining oil saturation in the mainstream area and the higher recovery degree of production wells. The history matching of the SZ oilfield in Bohai Bay is achieved with the new simulator, and the history matching accuracy is obviously higher than that of Eclipse. The research results have good theory and application value for water flooding offshore oilfields.
Inclusion of Microporosity in Numerical Simulation of Relative Permeability Curves
Authors: Lanetc Zakhar , Zhuravljov Aleksandr , Shapoval Artur , Armstrong Ryan T. , Mostaghimi Peyman
Published: February 2022

Advances in high-resolution micro computed tomography (micro-CT) allow obtaining high-quality rock images with a resolution of up to a few micrometres. Novel direct numerical simulation methods provide the opportunity to precisely predict the flow properties in the resolved pore space. However, a large fraction of porosity lies below the resolution of modern micro-CT scanners. These, so called, micro-pores may significantly affect the physics of flow in geologically complex dual-porosity heterogeneous formations (carbonates, shales, and coals) and are currently not accounted for in traditional micro-CT based simulations. In this work, we have employed a multiphase multi-scale Darcy-Brinkman approach to simulate immiscible two-phase flow in a hybrid system containing both macro-porous solid-free regions and a micro-porous permeable matrix. This approach solves the Navier-Stokes based volume of fluid equations system in macro-pores and accounts for multiphase Darcy equations in micro-porous regions. By combining available information on micro-porosity with relative permeability curves estimated from the synthetically generated image with both macro- and micro-porous regions fully resolved, we solve the inverse problem to account for micro-porous contribution in our Darcy-Brinkman simulation. This approach allows us to estimate relative-permeability curves in the micro-porous region and correct the multi-scale simulation so it coincides with the data from the fully-resolved image. As a result, we were able to account for the impact of micro-porosity on the residual saturation and correct the shape of relative permeability curves and their end-points in the micro-porous domain. The proposed approach provides a workflow which can be used to history-match the Darcy-Brinkman pore-scale simulation with core-scale petrophysical data with respect to the relative permeability. Thus, it is possible to account for heterogeneity in complex rock formations by incorporating the whole range of porosity. The inclusion of micro-porosity in pore-scale image-based simulations for predicting relative permeability curves may help in a more reliable modelling and estimation of filed-scale subsurface flows, production profiles, recoverable reserves and carbon capture and storage mechanisms.
Enabling Well and Reservoir Surveillance in Dual Water Injectors Through Distributed Fiber-Optic Sensing
Authors: in 't Panhuis Peter , Harthi Zena , Al Hashemi Mohamed , Hemink Gijs , Spivakovska Daria
Published: February 2022

Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) are novel surveillance technologies which are increasingly being used for in-well surveillance, including in injector wells. Some of the applications include injection profiling, acid-stimulation optimization, well-integrity monitoring, and fracturing diagnostics. Our goal here is to demonstrate how this can be applied to a particular type of water injectors that use dual injection lines to improve conformance by decomingling the injection between shallow and deeper reservoir targets. Specifically, annulus injection will be used to target the shallow reservoirs and tubing injection to target the deeper reservoirs.

Several case studies will be presented from water injectors located in the South of the Sultanate of Oman. This required integrated evaluation combining data from surface meters, downhole gauges, open-hole formation evaluation, as well as DAS and DTS. It will be shown how the data was analyzed to assess the efficiency of the waterflood, to optimize the water injection operating envelope, to identify fracture initiation or plugging, but also to analyze the effectiveness of the well and completion design.

Through its high sample rate and narrow spatial resolution, DAS is particularly effective at monitoring changes to injectivity in real-time. This is especially helpful for identifying the onset of fracture initiation and monitoring the growth in fracture height, which is difficult to achieve with other technologies. DTS warm-back analysis was also performed to complement the DAS interpretation and monitor changes in conformance with a deeper depth of investigation than DAS.

The unique value of the fiber-optic sensing technology is that it enables the implementation of the dual annulus-tubing injection completion design without limiting surveillance capabilities, as conventional surveillance in this well design is either impractical, not feasible, or too costly.
Foam Cementing Technology: Addressing a Lost-Circulation Issue in the East Siberia Region
Authors: Tcibulskii Mikhail , Trofimenko Ivan , Yagudin Marat , Lodin Alexey , Khohryakov Vladimir
Published: February 2022

Lost circulation (LC) is commonly encountered in drilling and cementing operations and can significantly contribute to non-productive time (NPT). An operator in the Kyumbinskoe Field faced this challenge in a fractured production section of the formation, and conventional LC solutions had been ineffective at achieving strict regulatory top of cement (TOC) requirements and satisfactory cement bonding. This paper describes the experience of utilizing foam cementing technology as a primary solution to solve a lost circulation issue on the project. For this project a foam cementing solution was designed to meet operational parameters for cementing a production casing in one stage (multi-stage tool was eliminated).

Use of foam cementing technology helped to minimize losses experienced in all cementing operations previously on this project. CBL results were also improved. All Customer requirements were met: Planned Top Of Cement (TOC)Minimum losses during cementing operationsRig time savingImproving CBL results.
First Smart Liner Mono-Bore Completion in Giant Carbonate Reservoir in Abu Dhabi Provides New Opportunities for Tight Reservoirs Development
Authors: Barragan Ernesto , Jimenez Alvaro , Ramarao Subba , Mogensen Kristian , Albarracin Ruben , Quintero Fernando
Published: February 2022

One of the main challenges in the oil industry is to develop tighter carbonate reservoirs. There are several methods to improve oil recovery (IOR), the adoption of Novel Lower Completions in horizontal wells can be one of the best tools to maximize reservoir contact and to control unwanted fluids (i.e., water or gas). It is especially important in carbonate reservoirs where most of the time it is imperative to provide additional stimulation in order to produce the reservoirs economically. The main objective of the presented work is to provide an alternative type of completion that creates better flow and stimulation distribution along the horizontal hole, In addition, the movable parts were eliminated to reduce the integrity risk while injecting the acid job stimulation.

In carbonate reservoirs, the standard method to improve reservoir permeability is through acid stimulation utilizing coiled tubing to place acid into the reservoir rock and improve the flow within the near wellbore area. This technique is costly and time consuming. Furthermore, in the specific case presented, there is the constraint of limited number of coiled tubing units where many times acid stimulation is delayed due to this fact. Installing the mono-bore segmented completion provides the capability to perform the job without the rig, only bull heading the acid utilizing high pressure pumps.

There are several options to install lower completions in the oil industry for horizontal wells. Smart liner is presented as an alternative to the conventional pre-perforated liner. The smart liner design was done with a proprietary software and consists of a specific number of small holes distributed along the horizontal section to distribute the flow and provide better stimulation placement at higher rates improving "worm holes" creation and extension. The lower completion is equipped with swellable packers to provide segmentation in the horizontal hole for future requirements if needed such as zones shut off. After the successful installation in a water injection well, water injection was initiated to define a baseline to compare with the results after the acid stimulation. The paper presents the lower completion design, the acid stimulation program and the results before and after the stimulation is done without rig on site. Initial results after the stimulation are showing more than twice the injection rate after the job. This job was done without the use of coiled tubing providing a better acid stimulation along the horizontal hole.

The 4-½ inches mono-bore completion is the first time that was done in the Asset, creating new opportunities to improve the stimulation and injectivity / productivity of the wells and consequent future Field development.
Uplifting the Productivity of a Mature Gas Giant in Pakistan Using Integrated Asset Modelling
Authors: Ahmed Sheraz , Muhammad Waqas Ghulam , Sheikh Hafiz Mustafa Ud Din
Published: February 2022

It's almost certain that the oil & gas industry has passed its plateau for large field discoveries. This places an extra burden on the efficient handling of our mature assets, as reasonable amount of hydrocarbons still exist in such reservoirs. However, the ever-increasing cost of new projects and low production gains hardly justify the economics. This study presents a novel approach applied on a mature gas giant, in order to revitalize old wells, optimize surface network and exploit the scattered sweet spots still prevailing in the field, with integrated surface and subsurface engineering strategies.

The field under study has been producing since 1955, with around 112 wells, completed in four independent formations. The primary reservoir (Reservoir-A) is categorized as a depletion-drive gas reservoir and has been on production since the field's inception; the reservoir pressure has depleted from 1,900psi to 300psi. The other formations: Reservoir B, C & D started producing from 1968, 2000 and 2015 respectively. At its peak, the field produced ∼1,000 MMScfd gas; but lately, the production decline rose to around 7-8% annually, mainly due to natural depletion. However, deterioration in well performances and limitations of surface facilities (feederlines, Gas-Gathering-Mains (GGMs) and compressors) have also exacerbated this decline, due to the additional pressure drops amassed in their dynamics with reservoir pressure depletion & water production.

To counter the field's rapid decline, a comprehensive workflow and an Integrated Asset model was developed, with an absolute focus on the NPV for each development. At the subsurface level, Reservoir-B (still under-developed) was the first targeted, as most of its wells were producing at uneconomical rates. An ant-tracking algorithm was run on the newly acquired 3D-seismic; and natural fractures - near the 02 high producers in Reservoir-B, were analyzed. A workflow was then developed to target similar fractures and the integrated impact, on surface facilities, was evaluated. Finally, three successful pilots were drilled in Reservoir-B and Reservoir-C, to evaluate the post-drill dynamics. Based on the real-time performances of these pilots with existing producers and surface facilities, the integrated field model was updated, coupling the wells, surface facilities and all four reservoirs (with independent reservoir models). As a result of this integrated model, 12 more wells, 09 workovers, 02 GGM optimizations and 03 compressor modification jobs were finalized; giving an overall increase in EUR by 800 BCF, while the NPV of the field increased by 131 $MM.

This study offers an innovative approach that has been followed to utilize each data-set systematically, in order to re-vitalize a field even after 84% depletion. The paper also describes the evaluation process for all the optimization opportunities and their impact on the NPVs, to reap the maximum reward from such an old field.
A First Attempt to Predict Reservoir Porosity from Advanced Mud Gas Data
Authors: Anifowose Fatai , Mezghani Mokhles , Badawood Saleh , Ismail Javed
Published: February 2022

Porosity, a critical property of petroleum reservoirs, is a key controlling factor of the reservoir storage capacity. It has been conventionally measured from core plugs. Empirical correlations, statistical, and machine learning methods have been employed for indirect estimation of porosity. The results obtained from these approaches are available only after acquiring drilling and wireline logs. Obtaining porosity estimates in real time, ahead of wireline logging, can help in making critical decisions and enabling early assessment of reservoir quality. We present the results of a machine learning approach to predicting porosity from advanced mud gas data. Datasets integrating advanced mud gas data with porosity were gathered from seven wells to prove this concept. The mud gas data includes light and heavy flare gas components. Optimized artificial neural network (ANN) models were applied to the datasets and multivariate linear regression (MLR) models were used as benchmarks. Each well dataset was split into training and validation subsets using a randomized sampling approach with the ratio of 70:30. A 100 ppm cut-off was applied to the total normalized gas. To evaluate the performance of the models, we use correlation coefficients (R) and mean squared error (MSE). The ANN models consistently outperformed the MLR models in all the datasets. The ANN models have training and validation correlation coefficients of up to 0.89 and 0.88, respectively, compared to an average of 0.79 and 0.77 for the MLR models. The training and validation MSEs for the ANN models are as low as 0.0135 and 0.021, respectively, compared to those of the MLR models in the range of 0.0007 and 0.03, respectively. These results indicate the nonlinearity of the relationship between porosity and the gas components. Furthermore, it can be deduced that the approach is feasible and better results are achievable. The randomized sampling ensures that each data point has an equal chance to be used for either training or validation without bias. The cut-off applied to the normalized total gas is a standard practice to eliminate the background gas effect in the mud gas data. This study provides an opportunity to utilize mud gas data beyond the traditional fluid typing and petrophysical correlation purposes. The presented approach has the capability to complement existing reservoir characterization approaches in providing reservoir quality assessments at the early stage of exploration. We plan to apply state-of-the-art machine learning models and perform sensitivity analysis on the gas components in the future to increase the accuracy.
Operations Sustainability During COVID-19 Pandemic
Authors: Maitieg Mohamed , Rachmat Febry , Baseef Hussam , Al Hadyani Fahad
Published: February 2022

The paper aims to describe the experiences, challenges faced, and lessons learnt on how The Company Well Services division is managing the risks associate to COVID-19.

The global COVID-19 pandemic spread in late 2019 and turned into one of the biggest challenges facing all industries including the oil and gas industry– in decades. The most challenging aspect of COVID-19 is its contagion rate, and its ability to spread from both non-symptomatic and symptomatic people to others who are in close contact through respiratory droplets, by direct contact with infected persons, or by contact with contaminated objects and surfaces.

Today, we must consider this new reality into business continuity planning and take all necessary precautions to mitigate its spread within the organization. As the health and safety of the employees and contractors is paramount to the Company and based on the Company commitment to provide better service quality to its clients, a thorough emergency response system that includes precaution measures to help fighting against COVID-19 and its potential to negatively impact the business was created. Furthermore, The Company COVID-19 Management Standard set the minimum requirements, to manage the risks associate to the COVID-19.

A Crisis Management Team was formed to oversee the implementation of COVID-19 decisions and raise awareness within the Company. All field personnel were required to comply to COVID-19 precautions during operations. With the implementation of new procedures, we were able to maintain the lowest confirmed case numbers among all the companies in our operational sector. Though it was challenging at the beginning for employees to comply, the new requirements have become the normal standard today.

These new measures focus on the importance of employee health and safety when working through COVID-19 pandemic and has helped to educate people on the importance of following health and safety procedures as way of life for everyone while maintaining the Company Operational Sustainability.
Improving ESP Performance in Low Productivity Gassy Wells: Case Study
Authors: A. Salah Ahmed Salah , A. Sabaa Ahmed Sabaa , A. S. Abd Elhaleem Ahmed Samir Abd Elhaleem , Shehab A. Shehab Ali , S. Ritondale Sergio Ritondale , A. Sorou Amin Sorour , A. Nady Ayman Nady
Published: February 2022

The electrical submersible pump (ESP), has become the most efficient and reliable artificial-lift method worldwide. However, in wells with high gas volume, they are highly affected by gas bubbles flowing through each component, affecting: the motor temperature, lifting efficiency, the integrity of the motor and cable. Thus, High gas interference might cause multiple intermittent shutdowns due to Gas Lock and eventually shorten the run life of the ESP.

ESP at Well #A was first installed and directly had a problem with High Gas Interference resulting in a pre-mature ESP failure and costly well intervention to restore well-deferred production. A detailed discussion with a technology provider and reference case studies come up with an integrated solution to have the proper gas handling system in addition to adjusting VSD logic to operate on PID current mode. The present study shows a successful ESP optimization through VSD settings in combination with a comprehensive gas handling system that allowed management of low productivity high GOR ESP applications.

This gas handling system for ESP was complemented by installing [PMM motor, tandem gas separators with vortex technology, compression mixed-flow stages in a wide range centrifugal pump, advanced gas handler (AGH), and Monel armor leaded power cable]. The system was able to manage GOR>2,000 SCF/STB with WC of <10%, Productivity index of 0.2 BPD/psi, and 75% of free gas flowing into the pump intake. This paper contains all the descriptions of each component in the ESP gas handling system. The system was able to increase well production by reducing gas-locking production shutdowns, stabilizing motor current fluctuations, increasing ESP lifetime, and increasing reservoir life by increasing drawdown and allowing effective pump operation at lower intake pressure.

The same directions were successfully applied in another two wells with low productivity gassy behavior; Well #B and Well #C. And, by monitoring ESP performance for a longer period, the pump showed a stable operation, by successfully mitigating the high gas interference trips. Thus, the implementation of this integrated solution in wells with high GOR has been demonstrated to be an effective solution. Also, it provides opportunities to expand the use of ESP in gassy wells, previously thought to be unsuitable.
Automatic Generation of Drilling Schedules Using Machine Learning: A Paradigm Shift in Planning and Resource Allocation
Authors: Nooruddin Hasan , Zahrani Aidah , Shahri Mohammed , Alsousy Abdulhamid
Published: February 2022

In this paper, a new method is presented in which the generation of drilling schedules for planning well cycle requirements are fully automated using advanced machine learning algorithms. The generated schedules incorporate key logics and rules to mimic key operational constrains. The new approach brings about a paradigm shift in planning and resource allocation where time and efforts are reduced by orders of magnitude allowing planners to explore an unlimited number of scenarios and assess plan uncertainty.

Advanced machine learning algorithms are used to automate drilling schedules. A key element is the learning part, in which all rig movements over the past years are carefully tracked and analyzed. Rig capabilities are then inferred and used in prediction, which is achieved by building a Markov Chain (MC) model that tracks the movement of every rig in history and analyses the type of wells drilled in the process. From this, the algorithm computes transition probabilities between different well classes – called MC states – controlling the assignment of rigs to future wells. The MC states can be defined by the user and contain typical well information, such as field and reservoir, fluid type, location, drilling operation type, and well completion type.

The algorithm is tested using a synthetic well requirements dataset, including drilling cost and time estimations over the planning cycle. A detailed drilling schedule was produced from which yearly budget, rig year and well count figures were determined. Since the time to generate a schedule was extremely short, multiple scenarios were performed to address the impact of the total number of rigs to be added every year. This feature permits for resource planning, sensitivity analysis and scenario planning. Quantification of uncertainty in drilling cost and time were addressed by fitting a binormal distribution to historical cost and time values. In each drilling schedule case, drilling cost and time were drawn from the distributions and used to determine overall budget. Tens of cases were performed and histograms of yearly budget, rig year and well count were generated indicating the range of possible figures due to uncertainties in well drilling cost and time.

We present a breakthrough innovation that has a far-reaching impact on planning through automated generation of optimized drilling schedules in minutes. The new approach serves as an important resource for planners by providing the capabilities to explore hundreds of scenarios encompassing entire range of uncertainties.
Extended Physics-Informed Neural Networks for Solving Fluid Flow Problems in Highly Heterogeneous Media
Authors: Alhubail Ali , He Xupeng , AlSinan Marwa , Kwak Hyung , Hoteit Hussein
Published: February 2022

Utilization of neural networks to solve physical problems has been receiving wide attention recently. These neural networks are commonly named physics-informed neural network (PINN), in which the physics are employed through the governing partial differential equations (PDEs). Traditional PINNs suffer from unstable performance when dealing with flow problems in highly heterogeneous domains. This work presents the applicability of the extended PINN (XPINN) method in solving heterogeneous problems. XPINN can create a full solution model to the solution of the governing PDEs by training the neural network on the PDEs and its constraints such as boundary and initial conditions, and known solution points. The heterogeneous problem is solved by performing domain decomposition, which divides the original heterogeneous domain into various homogeneous sub-domains. Each sub-domain incorporates its own PINN structure. The different PINNs are connected through interface conditions, allowing for information to communicate across the interfaces. These conditions include pressure and flux continuities. Various heterogeneous scenarios are implemented in this study to investigate the robustness of the proposed method. We demonstrate the accuracy of the XPINN model by comparing it with the ground truth solved from high-fidelity simulations. Results show a good match in terms of pressure and velocity with errors of less than 1%. Different interface conditions were tested, and it was observed that without the inclusion of pressure and flux continuities, the solver does not converge to the solution of interest. Sensitivity analysis was performed to explore the effects of the neural network architecture, the weights given to each loss term, and the number of training iterations. Results show that wide and shallow networks performed well due to avoiding the gradient vanishing issue that comes with deeper networks. In addition, balanced weights produced better accuracy in general. Moreover, more training iterations improved the accuracy of the results but at lower rates in later training stages. This paper presents XPINN to solve fluid flow in heterogeneous media. We demonstrate the robustness and accuracy of the proposed XPINN model by comparing it with the ground truth solutions in multiple heterogeneous cases. The model shows good potential and can be readily implemented in reservoir characterization workflow.
History Matching and Uncertainty Quantification of Reservoir Performance with Generative Deep Learning and Graph Convolutions
Authors: Shishaev Gleb , Demyanov Vasily , Arnold Daniel , Vygon Roman
Published: February 2022

Generative deep learning is becoming a widely used approach in geological modelling, especially in problems that involve optimization processes under uncertainty like history matching. The basic idea of reservoir modelling and history matching by generative deep learning is to train some kind of reservoir generator and provide optimization under different constraints. There are different approaches that have been developed to utilize various modifications of Generative Adversarial Networks or Variational Autoencoders to implement reservoir generators, but most of them have "conventional" convolution neural networks, hence all data have to be regular (rectangular) in structure. This should be recognized as a limitation.

In this work, we introduce a novel approach of reservoir modelling with Variational Autoencoders based on graph convolutions as opposed to "conventional" convolutions. In our approach reservoir model is considered as a graph, i.e., not a structured data type. Graph convolutions can deal with these data types, and connection with Variational Autoencoders provides us with the capability to generate initially unstructured reservoirs. Variational Autoencoders (VAE) demonstrate the ability to implicitly parameterize geological representations into a latent space of reduced dimensionality and provide ways to uncertainty quantification and production profiling among various geological concepts.

In the first part, we introduce the motivation to Graph Variational Autoencoders (GVAE) as opposed to conventional Generative Deep learning models. The rest of the paper is dedicated to experiments on a synthetic dataset with two different geological scenarios. We show that trained GVAE performs the generation of reservoir models with reliable geology. A latent space structure between Encoder and Decoder of GVAE is represented, and interpretation considering geology under investigation is performed. Finally, we show how latent space can help to estimate the uncertainty of production and an optimization workflow under well data constraints.