bluuebunny/crossref_metadata_2025 huggingface dataset:
Socio-political and socio-economic Space of the Republic of Crimea: risks and prospects for development (based on study conducted by the team of the Crimean Branch of FCTAS RAS)
Authors:   Crimean Branch of FCTAS RAS, Simferopol, Russian Federation, Uzunov Vladimir V. , Chigrin Victor A. ,   Crimean Branch of FCTAS RAS, Simferopol, Russian Federation, Gorodetskaya Elena G. ,   Crimean Branch of FCTAS RAS, Simferopol, Russian Federation, Zakharova Vera A. ,   Crimean Branch of FCTAS RAS, Simferopol, Russian Federation, Uzunova Oksana Y. ,   Crimean Branch of FCTAS RAS, Simferopol, Russian Federation
Published: November 2020

The article summarizes the results of a study conducted by the team of the Crimean branch of the Federal Center of theoretical and applied sociology of the Russian Academy of Sciences in 2019 within the framework of the planned topic: “Regions in the social space of Russia: centripetal and centrifugal trends”. The Republic of Crimea is considered as a spatial, multi- ethnic and multi-religious formation, a feature of which is the process of entering the socio-economic, political and legal space of the Russian Federation. The socio-political space of Crimea is considered in the context of the problem of social interactions. It is the interaction of individual and / or collective agents that is the main source of reproduction and changes in the socio- political space. The study monitored the characteristics of the population of the Republic of Crimea entering the political and legal space of Russia, analyzed the main characteristics of the socio-political activity of demographic, ethnic, socio-professional and religious groups of the population of the Republic of Crimea, including their assessment of the political, economic and socio- psychological situation in Crimea. Given the multi-level elections to the State Council in Crimea, as well as to local self-government bodies, the aim of the study was also to study the main parameters of political and electoral behavior — political and electoral activity, certainty, stability and motivation of electoral choice, analysis of the formation of the electoral and legal culture of Crimeans, deformed during the years the peninsula was under the jurisdiction of Ukraine.
“The Middle” as a Methodological Tool for Studying Social Reality
Authors:   Institute of Sociology of FCTAS RAS, Moscow, Russian Federation, Davydov Alexey P.
Published: November 2020

The article considers the possibility of interpreting the meaning of the “middle” as a methodological tool for studying social reality. Various types of “middle” are analyzed. “The Middle” J. P. Sartre/V. S. Bibler has liberal Cartesian roots. Through the principle of “I am responsible for my being-for- Other, but I am not its foundation,” it is aimed at the mastery and control of the Other by the Self. This type of “middle” allows the unity of the Self with the Other while maintaining the independence of the subjects from each other. This unity of subjects involves the regulation of relations between them through a change in the subject of dialogue (the interpolar “middle”) and the transition from dialogue as a one-time act to a process of multi-level dialogue. Examples of the Westphalian peace treaty between Catholics and Protestants (1649) and the Brest- Lithuanian peace treaty between Russia and Germany (1918) are given as examples of a successful change in the subject of dialogue. “Middle” M. M. Bakhtin/B. A. Lectorsky has social-democratic/ Christian-democratic roots. Through the principle of “I am for the Other from the point of view of the Other/Other for me from my point of view”, she aims to form a common foundation of the brotherly culture of I and the Other. This type builds the unity of the Self with the Other as a result of the inner need of both to serve each other. “For each other” and “from each other” can be reduced to identity in the idea of the “middle” of R. S. Grinberg on the basis of public-private partnerships. In this “middle” version, independence (the “from” tool) and ministry (the “for” tool) generate the “for / from” tool as a “middle” methodology. It is also proposed to consider the logic of the evolution of culture through the transition from the idea of a one-shot dialogue to the idea of multi-level dialogue: “thesis-antithesis chaos (the middle is struggling within itself between internal opposites) — thesis- antithesis…”.
“Life Satisfaction” and “Personal Happiness” in the Sociological Studies of Subjective Well-Being
Authors:   Institute of Sociology of FCTAS RAS, Moscow, Russian Federation, Tatarova Galina G. , Kuchenkova Anna V. ,   Institute of Sociology of FCTAS RAS, Moscow, Russian Federation
Published: November 2020

The article is devoted to the actualization of methodological issues of using the indicators “life satisfaction” and “personal happiness”. In sociological studies they play (either individually or as part of indices) the role of a generalized (general, integral) indicator of subjective well-being, in contrast to particular ones. The features of the relationship of these indicators are considered at the country, group (the basis for the selection — lifecycle stages) and individual levels. The article justifies the expediency (for sociological measurements) of the introducing of a two-dimensional space, highlighting segments in it, on which a very definite character of the relationship between life satisfaction and happiness is observed. Compared with other existing indices based on the difference in answers to direct questions about “life satisfaction” and “personal happiness” or the arithmetic mean, the proposed model is a “logical” index. The advantages of this construct in mass surveys are: procedural transparency, which facilitates the detection of atypical situations; the priority of quality over quantity when highlighting qualitatively homogeneous groups (according to a generalized indicator); avoiding the artificiality of the construct; avoiding “summation”. The empirical base is the data of the Russian Longitudinal Monitoring Survey (RLMS-HSE) (2017) and European Social Survey (2016).
The Impact of Knowledge Management Processes on the Quality of Higher Education from the Perspective of University Professors: A Case Study of the Professors of the Faculty of Economic and Commercial and Administrative Sciences, University of Biskra – Al
Authors: خير الدين أ. جمعة , دريدي أ. أحلام , خليل أ. صبرينة
Published: November 2020

This study aimed to identify the impact of knowledge management processes on the quality of higher education from the viewpoint of university professors at the Faculty of Economic,Commercial and Administrative Sciences at the University of Muhammad Khaydar Biskra. To achieve this objective, the descriptive analytical and case study method was used. Thestudy population was 195 professors, 109 of whom were selected as a sample of the study. To collect data from the study sample, a questionnaire was used. 195 questionnaires weredistributed to the study sample, but 72 questionnaires were returned at a rate of return (66.05%). Then the hypotheses were tested based on a set of research methods, using theSPSS software. Major findings of the study indicated a statistically significant effect of knowledge management processes with their dimensions (quality of scientific research,quality of student services and graduates, quality of curricula and study programs) on the quality of higher education among professors of the Faculty of Economic, commercial andadministrative sciences at the University of Biskra. The study concluded with a set of recommendations, including the adopting knowledge management processes approach todevelop and improve the quality of higher education institutions, utilizing and activating professors' roles, knowledge, ideas and expertise.Keywords: knowledge management, knowledge management processes, higher education quality, university professors, University of Biskra.
Eukaryotic and Prokaryotic Microbiota Interactions
Authors: Kodio Aly , Menu Estelle , Ranque Stéphane
Published: November 2020

The nature of the relationship between the communities of microorganisms making up the microbiota in and on a host body has been increasingly explored in recent years. Microorganisms, including bacteria, archaea, viruses, parasites, and fungi, have often long co-evolved with their hosts. In human, the structure and diversity of microbiota vary according to the host’s immunity, diet, environment, age, physiological and metabolic status, medical practices (e.g. antibiotic treatment), climate, season, and host genetics. The recent advent of next generation sequencing (NGS) technologies enhanced observational capacities and allowed for a better understanding of the relationship between distinct microorganisms within microbiota. The interaction between the host and their microbiota has become a field of research into microorganisms with therapeutic and preventive interest for public health applications. This review aims at assessing the current knowledge on interactions between prokaryotic and eukaryotic communities. After a brief description of the metagenomic methods used in the studies analysed, we summarise the findings of available publications describing the interaction between the bacterial communities and protozoa, helminths, and fungi, either in vitro, in experimental models, or in humans. Overall, we observed the existence of a beneficial effect in situations where some microorganisms can improve the health status of the host, while the presence of other microorganisms has been associated with pathologies, resulting in an adverse effect on human health.
Legal consciousness as a factor promoting the achievement of educational objectives and the realization of the right to education by individuals and collectives
Authors: Tereshchenko Elena Anatolyevna , Kovalev Vladimir Viktorovich , Trofimov Maxim Sergeevich , Zasseev Dzambolat Arthurovich
Published: November 2020

The article studies the legal consciousness of participants to educational activities in the process of achieving educational objectives and realizing their right to education. Obstacles that are typical of such a type of research conditioned by an insufficient theoretical basis for studying legal consciousness in general and as a category accompanying the realization of goals, capabilities and rights of participants to educational activities. In the Russian legal science, these obstacles are caused by the prevalence of ideological dogmas over objective scientific approaches (for example, the concept that state and law will wither away as unnecessary institutes alongside the formation of communism), which is emphasized by Russian scholars concerned with these issues. Over time, the above-mentioned difficulties have been overcome and today the issues associated with legal consciousness (in particular, individuals and collectives in the educational sphere) are an integral part of scientific interests in the field of jurisprudence. The article aims at reviewing and analyzing the process of studying legal issues (including participants to educational activities) by Russian and foreign scholars, summarizing the results of their scientific research and drawing the relevant conclusions. Relying on the existing theoretical and practical experience and an extensive set of different research methods (general scientific and specific legal methods), the authors have achieved certain results and reached several conclusions. Their main achievement is new data on various aspects of justice.
Fort McCoy, Wisconsin WWII buildings and landscapes
Authors:   Engineer Research and Development Center (U.S.), Adams Sunny E. , Tooker Megan W. , Smith Adam D.
Published: November 2020

The U.S. Congress codified the National Historic Preservation Act of 1966 (NHPA) mostly through the National Register of Historic Places (NRHP), which requires federal agencies to address their cultural resources. Section 110 of the NHPA requires federal agencies to inventory and evaluate their cultural resources, and Section 106 requires them to determine the effect of federal undertakings on those potentially eligible for the NRHP. This report provides a World War II development history and analysis of 786 buildings, and determinations of eligibility for those buildings, on Fort McCoy, Wisconsin. Evaluation of the WWII buildings and landscape concluded that there are too few buildings with integrity to form a cohesive historic district. While the circulation patterns and roads are still intact, the buildings with integrity are scattered throughout the cantonment affecting the historic character of the landscape. Only Building 100 (post headquarters), Building 656 (dental clinic), and Building 550 (fire station) are ELIGIBLE for listing on the NRHP at the national level under Criterion A for their association with World War II temporary building construction (1942-1946) and under Criterion C for their design, construction, and technological innovation.
Late Middle Miocene Emergence of the Olympic Peninsula Shown by Sedimentary Provenance
Authors: Shekut Samuel , Licht Alexis
Published: November 2020

The Olympic Peninsula is the uplifted portion of the Cascadia accretionary wedge and forms the core of a 200 km scale oroclinal bend on the west coast of Washington State. The accretionary wedge started forming 45 million years ago following the accretion of the Siletzia igneous province along the Cascadia subduction margin. Low-temperature thermochronology studies have shown that the core of the peninsula has been continuously exhumed for the last 14 million years. The earlier onset of oroclinal bending, uplift, and emergence remains poorly documented. Here, we explore the Cenozoic drainage history of the Cascadia forearc and accretionary wedge to reconstruct the deformation history of the Olympic Peninsula. We use detrital zircon provenance and grain petrography data from modern rivers draining the Cascades, the Cascadia forearc and accretionary wedge, as well as from Eocene to late middle Miocene sedimentary units from the same areas. We first show a clear difference of sedimentary provenance between sedimentary units in the accretionary wedge, with older units reflecting mélange and imbricated strata that began as part of Siletzia, and younger units reflecting trench-fill material sourced from the Cascades and accreted to the wedge. We show that the accretionary wedge was directly fed from the Cascade arc until at least 16.5±0.5 Ma, providing a maximum age for the emergence of the Olympic Peninsula. Fluvial deposits in the Cascadia forearc basin dated at 13.3±1.3 Ma display zircon age spectra and sedimentary grain petrography features typical of recycled accretionary wedge material. Although these deposits may also reflect local input, middle Miocene exhumation rates suggest the Olympic Peninsula was an active sediment source. Our results bracket the timing of emergence of the Olympic Peninsula to a narrow window in the late middle Miocene. We suggest that the initial onset of accretionary wedge deformation and oroclinal bending predates this by at least 10 million years, in the upper Oligocene, and is marked by flexural subsidence and high sedimentation rates recorded in strata of the Seattle Basin. Our results support a composite history for the development of the Cascadia accretionary wedge rather than models predicting a gradual and steady build-up.
Tracking Condensate Banking in Tight Gas Reservoirs Through Well Test Analysis: Right Well Test Design Leads to Factual Reservoir Characterization
Authors: Mithani Aijaz Hussain , Motaei Eghbal
Published: November 2020

Understanding gas condensate reservoirs require comprehensive data acquisition efforts to collect representative PVT samples, measure the effective reservoir permeability and characterize reservoir behavior through well test. Right well test design is key in tight gas reservoirs to reveal all flow regimes and characterize reservoir to have valid FDP formulation including well trajectory design, well completion design and well locations and proper facility sizing.

Well test design was done and updated in appraisal to reflect condensate banking in tight reservoir with moderate condensate gas ratio (CGR). The main objective was to measure the well deliverability and reservoir characterization to get condensate banking fingerprint (signature) on pressure derivative in Modified Isochronal Test (MIT) section. This design allows to measure the permeability and radius of impaired region due to condensate banking, while extending test enough allows to measure gas zone permeability as well.

A single well radial model (SWRM) was constructed to reflect the condensate banking in simulation model as well. The log data been up-scaled to well test data in free gas zone to analyze the flow behavior of condensate banking in near-wellbore.

The condensate bank radius was estimated for different test rates in MIT test. The estimated permeability of condensate bank at different flow rates (and draw-down) are different as per expectation. This is due different saturation at different draw-down based on liquid drop-out. The permeability of single-phase gas zone (far from near-wellbore) is constant in all flow periods of MIT consistently. The results from well test is in perfect agreement with up-scaled single well model. The wellbore hydro-dynamic is modeled using transient well models (OLGA application) and showing liquid loading in long term which suggest to couple the model with transient model to consider the impact of liquid hold-up in the wellbore for proper completion design, well trajectory design and production profile forecast.

The liquid drop-out from PVT analysis was used to estimate the saturation near wellbore at each pressure and then from different draw-down periods the effective permeability of each test period correlated to this saturation. This resulted to estimate relative permeability from well test and PVT data (CVD test) which is more reliable due to extra-large scale compare to core analysis tests.
A Data-Based Approach for the Prediction of Stuck-Pipe Events in Oil Drilling Operations
Authors: Brankovic Aida , Matteucci Matteo , Restelli Marcello , Ferrarini Luca , Piroddi Luigi , Spelta Andrea , Zausa Fabrizio
Published: November 2020

Stuck-pipe phenomena can have disastrous effects on drilling performance, with outcomes that may range from time delays to loss of expensive machinery. In this work, we develop three indicators based on the mudlog data, which aim to detect three different physical phenomena associated to the insurgence of a sticking. In particular, two indices target respectively the detection of translational and rotational motion issues, while the third index concerns the wellbore pressure. A statistical model that relates these features with the documented stuck-pipe events is then developed using machine learning. The resulting model takes the form of a depth-based map of the risk of incurring into a stuck-pipe, updated in real time. Preliminary experimental results on the available dataset indicate that the use of the proposed model and indicators can help mitigate the stuck-pipe issue.
Absolute Permeability Prediction Model Based on Fractal Theory of Porous Media
Authors: Li Changyong , Pi Jian , He Juan , Zhang Yu , Wang Long
Published: November 2020

This paper reviewed many conventional capillary pressure (Pc) models, which is the foundation of estimating permeability through Pc curves. The infeasibility of Brooks-Corey Pc model for heterogeneous rocks caused poor prediction accuracy of the existing permeability model. An advanced Pc model was derived through fractal theory in which the cylindrical capillary tube was regarded as the pore unit of the fractal object. In addition, a novel permeability model was developed by substituting the integral item in Nakornthap-Evans model with the advanced Pc model expression. The permeability model established a power law relationship with the rock physical properties and characteristic parameters of Pc curves. The calibration and validation analysis were then conducted with a dataset of 210 rock samples. The fractal based Pc model can fit the Pc points measured in laboratory well, and estimate the characteristic parameters for each Pc curve through fitting work. The prediction permeability closely matches the measured permeability, which was verified by the high regression Coefficient of Determination. With the fractal dimension increasing, the absolute permeability decreases monotonically. The advanced Pc model and novel permeability model should have great application prospects in the field of Pc curve characterization and physical property analysis.
Primary Cementing Utilizing Epoxy Resins as Additive: Experimental and Application
Authors: Alanqari Khawlah Abdulaziz , Wagle Vikrant , Al-Johar Abdulwahab Sami , Al-Yami Abdullah Saleh , Ali Wajid , Abbas Athman
Published: November 2020

This paper discusses the designing and field application of a polymer resin-based cement system that enhances the final cement properties in terms of compressive strength and cement bond to the casing along-with minimizing the occurrence of casing-to-casing annulus (CCA) pressure during the life cycle of the well.

Several experimental studies were applied to this polymer resin-based cement system including rheology, thickening time test, free fluid test, compressive strength test, etc. The results showed that the polymer resin-based cement has improved compressive strength, ductility, and enhanced shear bond strength. Which, as a result, helps to provide a dependable barrier that would help to prevent future sustained casing pressure (SCP). Besides, the cement slurry was designed with a controlled and engineered rheology. This was done by controlling the amount of the resin and cross-linker into the cement slurry in order to achieve the target density as well as to avoid a sudden increase in the final slurry viscosity that could result in mixing difficulties at the field.

Moreover, the field deployment of this system on a 13 3/8-in casing second stage tail slurry has been discussed.
Ensemble-Based Well Location Optimization Under Subsurface Uncertainty Guided By Deep-Learning Approach To 3D Geological Feature Classification
Authors: Schulze-Riegert Ralf , Lang Philipp , Pongtepupathum Wanida , Drew Christopher , Round Andrew , Shaw Kevin , Adeyemi Tobi , Topdemir Sabahattin , Pattie Stephen , Nasiri Hamid , Hegre Thor Martin
Published: November 2020

Ensemble-based reservoir simulation workflows have become popular for estimating prediction uncertainty and optimizing field development objectives under uncertainty. While workflows exist for ensemble generation, ensemble analyses become an increasing challenge for extracting value from ensembles through robust field development guidance. This work presents a self-supervised deep learning modelling approach for classifying 3D-structural features applied to expert-driven field performance optimization under uncertainty.

Meaningful representations of subsurface features are essential to efficient machine learning tasks such as classification of categorical outcomes and regression for predicting dependent target variables. Unlabeled data is abundant and used for unsupervised learning which is complex in contrast to the use of labelled data. However, labelling is generally expensive and, in some cases, ill-defined or impractical due to missing modelling approaches. In this work, a self-supervised deep learning modelling approach is applied to learn semantically meaningful representations of subsurface features, e.g.,size and structure of connected volumes. The deep learning model is designed to learn a representation of 3-dimensional subsurface features. Representations are categorized, clustered and ranked for well location decision support.

For application demonstration, the self-supervised deep learning modelling approach is embedded in a structured workflow design for guiding well location selection to optimize reservoir delivery performance under subsurface uncertainty. Performance verification and application results are presented for the Olympus semi-synthetic case study. Based on probabilistic success criteria we present an optimized well placement design for which 90% of all producers deliver the economic demand at an 80% probability level or higher. In a second study the workflow is applied to a real field to discuss guidelines of use and to share expected gains in workflow efficiency, result quality and decision support.
Success Story: 64 Wells with Hybrid Real Time DTS/DAS Monitoring and Permanent Downhole Gauges Installed in the United Arab Emirates
Authors: Patel Dipen , Wee Keith , Gorgi Sam , Ashraf Hamza
Published: November 2020

The latest well completions developments for extended-reach wells include advanced techniques to improve the effectiveness of production and optimization solutions to achieve sustainable well production. Monitoring systems installed during the completion use real-time distributed temperature sensing (DTS), distributed acoustic sensing (DAS), and downhole gauge data to obtain better reservoir insight throughout the life of the well. This enables informed decision making to achieve optimal well production and gas injection.

Permanent downhole gauges (PDGs) with dual sensors (tubing + tubing) are installed, along with hybrid cable as part of the upper completion string. The hybrid cable has an electrical conductor and two fiber-optic lines for DTS and DAS measurements. The cable is clamped onto the tubing at each coupling and passes through the upper completion subassemblies, which include gas lift mandrels (GLMs) and tubing-retrievable safety valves (TRSVs). An intermediate completion is run before the upper completion, which comprises a two-trip permanent packer and remote actuated barrier device (multicycle).

Completions monitoring with hybrid cables provides an advantage over conventional gauge-only systems, with only a slight increase in completion costs. Using a single cable provides more run-in-hole (RIH) efficiency, fewer lines to manage, and a smaller equipment footprint. PDG data are used by production engineers, field development personnel, and subsurface personnel to determine pressure drawdown and optimize surface production choke size. Distributed fiber-sensing technology determines the effectiveness of gas lift operations to optimize injection rates, which effectively optimizes pumping rates and flow from surface. Fiber-sensing technology also helps identify tubing and annulus leaks, monitor the health of the completion during the life of the well, and minimize wellbore damage.

This type of completion has been installed successfully in 64 wells (60 production and 4 injection), and all systems are operational with data accessible from all wells.

This approach benefits the industry by highlighting best practices, providing advanced technology options for evaluating data and reservoir productivity, and providing completion and drilling effectiveness for extended deep-reach wells.
Geomechanics Recipes for Advanced Reservoir Stimulation — A Case Study for an ADNOC Field
Authors: Adigolo Charles , Ebeid Mahmoud , Eriavbe Francis , Nasreldin Gaisoni , Ni Qinglai , Mohamad-Hussein Assef , Rajaiah Nanthakumar , Subbiah Surej Kumar
Published: November 2020

The recent surge of interest in developing tight resources in the United Arab Emirates (UAE) has been accompanied by a growing appreciation of geomechanics challenges with potentially far-reaching implications on successfully placing and producing from stimulation treatments. The challenges are mostly posed by regional tectonics and localised ‘stress regime’ changes from normal faulting to strike-slip. Therefore, understanding the in-situ geomechanical conditions plays an important role in optimising stimulation designs and recommending safe drilling mud weights.

In this study, a 3D mechanical earth model (MEM) was built by comprehensively integrating data from multiple sources—including interpreted mechanics and lithology information such as stresses, stress direction, and core results. This 3D geomechanical model covered the target reservoir and extended from the surface to deeper formations to fully capture geological structures with high resolution. A set of faults was also incorporated to help examine their impact on well planning. Sensitivity analyses were conducted to account for uncertainties in mechanical properties and local tectonic stress state.

The 3D MEM successfully captured the observed drilling events in the thin weak inter-layers between the target reservoir for multiple wells. Some faults were shown to be critically stressed at present day. Risk of fault reactivation would increase with future production, and horizontal drilling may become increasingly difficult. The horizontal stresses in the target reservoir were significantly affected by porosity. Specifically, the low-porosity zones may have strike-slip stress regime, while high-porosity zones are in a predominantly normal faulting stress regime. The geomechanical modelling approach helped qualitatively highlight the variations in fracture containment (for a range of ‘net pressure’ thresholds), thus demonstrating the risk of breaching in a reservoir seal. It is moreover an interesting side effect that this geomechanical modelling ‘recipe’ provided invaluable insights into delineating potential ‘sweet spots’ for stimulation—essentially characterised by having high-porosity and low breakdown pressures.

The developed 3D MEM helped extend the understanding of subsurface behaviour beyond a few well locations and historic events. Moreover, the model demonstrated its predictive power in relation to identifying the potential location and pressure boundary for a future stimulation plan ultimately impacting the well placement strategies.
A Deep Autoregressive Model for Multiple Petrophysical Evaluations
Authors: Kumar Ashutosh , Priyadarshan Amit
Published: November 2020

Deterministic methods of petrophysical evaluation is made complicated by petrophysical and geological heterogeneity within the formation even though Petrophysical logs provide most of the subsurface data to an exploration geologist. Biases between the calculated values of different petrophysical properties namely, final-porosity, horizontal-permeability, shale-volume and water-saturation, results from the use of multiple empirical formulae, assumptions and correction factors. The existing deterministic petrophysical models evaluate each petrophysical properties separately and then use empirical values to account for dependence between different properties. These models have limited flexibility to account for the existing high-dimensional non-linear dependencies between these parameters.

In this paper, we propose a fully probabilistic and autoregressive deep learning model, which calculates values of final-porosity, horizontal-permeability, shale-volume and water-saturation with high accuracy in one single model instead of multiple empirical formulae. In this model, each petrophysical property is conditioned on the cumulative outcomes of the previous ones, thus evaluating all of the aforementioned petrophysical properties together.

Herein, we have used raw log data from Volve, a field on the Norwegian continental shelf, which has been collected at every 0.1 m across 18 different producing and non-producing wells. The Dataset consists of raw input and corresponding multiple outputs, calculated using existing empirical equations by noted geologists. The model architecture used in this study is similar to WaveNet and consists of stack of dilated causal convolutions along with gated activations, residual connections and skip connections. Validation set, used for hyperparameter tuning and selection of best model out of all possible models, contains the properties of the entire training set. Encoders and decoders used in our model do not share parameters, allowing them to handle accumulated noise while training. When evaluated using symmetric mean absolute percentage error (SMAPE), a single deep learning model performs at par with the expert geologists. Multiple petrophysical values are calculated and were found to be remarkably accurate. Performance of the single model remains consistent across Hugin and Sleipner formation.

The proposed probabilistic model models high-dimensional dependencies. It represents the information available in raw input as well as multiple calculated petrophysical values. It makes no assumption about the complex physical system, making it robust to the existing uncertainties. Proposed deep-learning model can ease the burden of repetitive and quality controlled petrophysical evaluation.
Machine Learning Based Seismic Data Enhancement Towards Overcoming Geophysical Limitations
Authors: Nakayama Shotaro , Blacquière Gerrit
Published: November 2020

Acquisition of complete data, i.e., unblended, well-sampled and broadband data, is technically desirable. Obviously, such a scenario is prohibitively expensive to realize. To deal with economic considerations in a seismic survey without seriously compromising data quality, we propose a machine-learning approach that offers an opportunity to acquire incomplete data, i.e., blended, sparsely-sampled and narrowband data, while still benefitting from being able to process complete data. In this study, we utilize a deep convolutional neural network. The incomplete data are fed into the applied network that simultaneously performs suppression of blending noise, reconstruction of missing traces and extrapolation of low frequencies such that prediction of the complete data is attainable. We validate the performance of the proposed method using both synthetic and field datasets. Acquisition scenarios implemented to generate incomplete datasets impose a significant reduction of data size in the frequency-space domain. Despite the limited information available in the input data, the prediction results obtained from both numerical and field data examples clearly confirm that the proposed machine-learning approach is capable of dealing with deficiencies in the incomplete data and subsequently deriving the complete data of sufficient quality. In addition to suppression of blending noise and reconstruction of missing traces, no discernible difference in prediction errors between preexisting and extrapolated frequencies is observed, which is hardly realizable with existing geophysics-based approaches. As a consequence, the proposed scheme allows for optimal data enhancement even when seismic acquisition is performed in a blended, sparsely-sampled and narrowband fashion.
Building Multifrac Completion Strategies in Tight Gas Reservoirs: A North Sea Case
Authors: Buijs Hernán , Guerra Clairet , Sonwa Roger , Nami Patrick , Vecchia Luciano , Ishmuratov Roman
Published: November 2020

Hydraulic fracture design driven by multi-disciplinary collaboration can maximize the production potential of complex multi-frac horizontal wells. Integration of multiple information sources (i.e.: geological, dynamic and geomechanical data) allows to build representative models and have proven to improve modelling towards a realistic understanding of tight reservoir performance of several multi-fracced wells. 3D properties encompassing the reservoir geological heterogeneity, pore pressure, mechanical elasticity and state of stress were utilized to develop a strategy to fracture stimulate a horizontal wellbore in the North Sea Region. The study was instrumental to build fit-for-purpose hydraulic fracture designs by incorporating state of stress changes related to pore pressure depletion on different faulted compartments supported by a reservoir dynamic simulation. Such models provided meaningful value to optimize the well trajectory used to access the host rock, understand fracture height growth possibilities in different compartments and define the number/size of hydraulic fractures required for optimum production.
Challenges in Drilling & Completion of Extended Reach Drilling Wells with Landing Point Departure More Than 10,000ft in Light/ Slim Casing Design.
Authors: Unnikrishnan Nitheesh Kumar , Al Ameri Saleh , Al Yamani Sultan Mohamed
Published: November 2020

Extended reach drilling (ERD) wells involves numerous drilling challenges that in many cases causes cost overrun and delays for such wells. This paper presents a new approach used to drill a 10,000 ft long departure using a slim design to replace the conventionally well design that uses heavy casings. This approach eliminated the 22" hole section and the associated 18 5/8" casing reducing 20 days form the well duration and saving $1 million.

Achieving such a long departure requires a shallower kick off and requires building inclination as early as possible crossing reactive shales and need high mud weight to guarantee hole stability. Historically an additional casing string used in order to isolate a loss zone prior to the shale formation to drill it with high mud weight. This approach limited the inclination across shales and penetrated it along with the loss zone in same section by controlling the WBM weight to be just high enough for bore stability and minimizing the loss rate but increasing the challenges in the next sections.

Drilling the single horizontal oil producer well started with a 16" hole size, kicked off at 300 ft and built to 22-degree inclination in that section and isolated with 13-3/8" surface casing. A 12-1/4" hole was drilled across the loss zone and reactive shales formation keep the WBM weight as 9.4 ppg (70 PCF) while drilling the loss zone and increased later to 9.9 ppg (74 pcf) prior to drilling the reactive shale where the inclination was limited to 27 degree across it and built later to 32 deg at the end of 12 ¼" hole section and isolated with 9-5/8" production casing.8 ½" hole section was drilled with 12.3 ppg (92 pcf) OBM in order to built 82 deg before penetrating the reactive shale and hold it tangent to cover the maximum departure and built to 90 deg to land in target reservoir with total departure of 10,755 ft and isolated with 8,150 ft long 7" production liner, followed by 4,000 ft 6‘‘ horizontal open hole. This novel light casing design avoided drilling a hole section of 22‘’ and the associated 18-5/8" casing, saving at least 20 days in well duration and nearly $1 million in well cost.

The new slim well design explained in this paper, which is used to drill long departure well with 10,000 ft, is the first of its kind in ADNOC Onshore field. It has proved that such long departure wells can be completed with light casing design and achieve 100% integrity.
A Machine-Learning Approach to Removing Highly Variable Surface-Waves for a Large Shallow-Water Survey Offshore Abu Dhabi
Authors: Badreia Al Hajeri , Guillaume Cambois , Mohamed Mahgoub , Mohamed Al Sarmi , Sonia Domont , Fritz Damianus
Published: November 2020

A large, full-azimuth, shallow-water ocean-bottom node (OBN) survey offshore Abu Dhabi revealed an intriguing azimuth-dependent linear noise pattern. The mud rolls and guided waves appear to curve along surface and near-surface features. Although we have yet to fully understand the mechanism that causes this phenomenon, we need to accurately and effectively eliminate these linear noises to meet the tight processing deadline. In view of the huge amount of data involved, we implemented a data-driven machine learning (ML) technology.

Mud rolls and guided waves are dispersive in nature. The standard way to remove them is to compute the dispersion curve (DC) (phase velocity as a function of frequency) from the data, model the corresponding mode, and subtract the model from the data in an adaptive way. In our case, the linear noise patterns are not only related to the azimuth angle of acquisition, but they also vary spatially, which makes removing them even more challenging. Since we cannot manually select the dispersion curves (DC) given the size of the data, we implemented an ML algorithm to classify the noise pattern and design the best noise removal operator for each category. The remaining outliers can then be resolved using customized solutions. In order to transform seismic data analysis into a pattern recognition problem, we need to generate attribute maps. In our case, one of the attribute map is the dispersion curve (DC), which has been calculated from the data. The maps are used as input to the ML stream which identifies classes by unsupervised K-means clustering analysis. Another attribute used is the root-mean- square (RMS) amplitude calculated over a fixed time window for each receiver gather. The corresponding maps are used as the input to the ML flow, which involves two-steps: first reduce the dimensionality of the input data by performing a principal component analysis (PCA), then identify classes through unsupervised K-mean clustering analysis. The generated class determines the common linear noise behavior and its spatial distribution group, thereby effectively driving the linear noise attenuation parameter setup.
First Piloting of Tight Oil Recovery in a Marginal Greenfield: Technology Selection and Planning Considerations for the First Multistage Acid Fracturing, Offshore AbuDhabi
Authors: Al Mazroui Sultan , Al Menhali Helal , Samantray Ajay , Al Shekaili Fatema , Al Hosani Fahad
Published: November 2020

The paper describes the planning and design of the first pilot well to unlock tight oil from a heterogeneous Jurassic carbonate reservoir, offshore Abu Dhabi. Reservoir permeability ranges from 0.1 to 5mD, with more than 80% of oil in-place locked up in the 0.1-0.3mD rocks. Conventional perforation, acid stimulation and zonal tests were seen to result in unsustainable flow and very low productivity index. Prior to committing capital investment, a decision was made to screen different completion and production technologies to exploit tight reservoirs, and deploy a pilot well to assess the feasibility of commercial recovery.

The pilot study addressed the full value chain of upstream oil development, from static and dynamic modeling, drilling, completion and stimulation strategy, geomechanics and technology options screening including assessment of subsurface uncertainties, material requirements, and economics. Several well construction, stimulation and completion options were screened including stair-step horizontals, multilaterals, fishbone wells, multistage hydraulic fracking, stem tunneling and fishbone stimulation. Multistage acid frac was found to be technically and economically feasible offering improved recovery. Furthermore, this option is Abu Dhabi’s first ever offshore multi stage acid frac pilot, which is expected to lead and potentially unlock the rest of offshore tight reservoirs across the ADNOC Group.
Rock Drillability Intelligent Prediction for a Complex Lithology Using Artificial Neural Network
Authors: Gamal Hany , Elkatatny Salaheldin , Abdulraheem Abdulazeez
Published: November 2020

The fourth industrial revolution and its vision for developing and governing the technologies supported artificial intelligence (AI) applications in the different petroleum industry disciplines. Therefore, the objective of this paper is to use the artificial neural network (ANN) to build a model for the rate of penetration (ROP) that considers the effect of drilling parameters,formation lithology, and drill bit design on the ROP performance. The novelty in this study is addressing the influence of poly diamond crystalline (PDC) bit design as the number of blades and cutter size, bit nozzle total flowing area (TFA),and combined different drilled formations on the penetration rate. The well drilling data covered the 8-3/8" phase with more than 1000 readings for each input.The input data are the weight on bit (WOB),revolution per minute (RPM), torque (T), standpipe pressure (SPP),and mudflow rate (Q), mud weight (MWin), gamma-ray (GR), bit design codes as the number of blades and cutter size, bit nozzle, and total flowing area (TFA).The data training to testing ratio was 70: 30%. Another data set from the same filed was used to validate the model and the results showed high accuracy for the ANN-ROP model. The model provides a high performance and accuracy level with correlation coefficient (R) of 0.99, 0.98, and 0.98 and an average absolute percentage error (AAPE) of 4.36 %, 7.06 %, and 8.14 % for training, testing, and validating respectively.
Application of Stratigraphic Forward Modelling to Carbonate Reservoir Characterization – A New Paradigm from the Albion R&D Project
Authors: Borgomano Jean , Massonnat Gérard , Lanteaume Cyprien , Danquigny Charles , Samson Philippe , Rolando Jean-Paul , Rebelle Michel
Published: November 2020

Improving carbonate reservoir prediction, field development and production forecasts, especially in zones lacking data, requires novel reservoir modelling approaches including process-based methods. Classical geostatistic modelling methods alone cannot match this challenge, in particular if subtle stratigraphic architectures or sedimentary and diagenetic geometries, not directly identified as properties with well data, control the reservoir heterogeneity. Stratigraphic forward modelling approaches can provide pertinent information to carbonate reservoir characterization. Integrating carbonate stratigraphic forward models in reservoir modelling workflows faces two sets of challenges: (1) the difficulty to establish high resolution and multi-scales continuous reservoir analogues from outcrops and (2) the development of forward modelling methods adapted to carbonate heterogeneity and calibrated to well data. The ALBION R&D Project dedicated to the Lower Cretaceous rudist-rich carbonate platform meets these two considerable scientific challenges. This workflow builds on previous modelling approaches at basin scale that integrates seismic inversion and forward modelling. This project delivers deterministic high-resolution static models that support the development of process-based modelling solutions. This multidisciplinary approach focuses on the characterization of primary sedimentary and diagenetic overprint that can impact drastically the flow behavior at inter-well scales in similar reservoir units such as the Kharaib and Shuaiba Formations. The SED-RES modelling package developed in house by Total R&D has been tested and calibrated with these high-resolution stratigraphic outcrop models. It allows valid prediction of carbonate facies associations mimicking the spatial distribution mapped along the Urgonian platform transects.
Deployment of Water Tracers Using Modified Dummy Gas Lift Valves – An Added Value to Field Operations and Reservoir Management
Authors: Gomes Jorge , Narayanan Ram , Alshmakhy Ahmed , Al Rawahi Ali
Published: November 2020

The objective of the paper is to share knowledge on an innovative design of the conventional dummy gas lift valves (GLV) which allows the deployment of water tracers within a polymer matrix (Figure-1). These tracers are released in contact with water and are detectable at surface as soon as water breaks through in the well. This technology does not compete with the established and more expensive conventional tracer deployment downhole, in segmented horizontal wells, for instance, whereby the reservoir zones producing water are detectable. Here, the deployment is in the upper completion of any well equipped with gas lift mandrels, whereby a new design of a dummy valve allows the inclusion of water tracers which are released in contact with water. At surface, in the first stage separator, or manifold, which receives fluids from many wells, a specific well producing water can be detected (Figure-2). Like any dummy GLV, this new tool containing tracers can also be deployed using a normal slick line and does not prevent any cased hole logging operation, like other downhole tracer deployment systems in the market.