bluuebunny/crossref_metadata_2025 huggingface dataset:
Formación ciudadana en el programa de Psicología de la Universidad Nacional Abierta y a Distancia, CEAD Ibagué
Authors: Arbeláez Gallego Natalia , Ñañez Rodríguez José Julián
Published: February 2022

El presente artículo presenta la valoración y el aporte del programa de Psicología de la UNAD, CEAD Ibagué, en lo relacionado con la formación ciudadana de sus estudiantes, a través del análisis del Proyecto Académico Pedagógico Solidario (PAPS) y del Plan de Estudio del programa de Psicología de la UNAD. Los objetivos que movilizan esta apuesta pedagógica están orientados de manera general a valorar el aporte del programa de Psicología de la UNAD, CEAD Ibagué, en lo relacionado con la formación ciudadana de sus estudiantes. De manera específica se buscó analizar en el proyecto académico pedagógico solidario (PAPS) y en el Plan de Estudio del programa de Psicología de la UNAD, los aspectos relacionados con la formación ciudadana a los estudiantes; así mismo, describir los aportes a la formación ciudadana desde las actividades extracurriculares y/o complementarias dirigidas a los/las estudiantes de Psicología, CEAD Ibagué y, finalmente, analizar las concepciones que poseen los /las estudiantes, docentes y egresados sobre la formación ciudadana que imparte el programa de Psicología de la UNAD - CEAD Ibagué. Metodológicamente es de enfoque mixto, con un alcance descriptivo desde el estudio de caso. Se establecieron también audiencias foco conformadas por la comunidad estudiantil del programa de Psicología del CEAD Ibagué, compuestas por una muestra de 18 estudiantes que se encuentren realizando sus prácticas profesionales. Así mismo, un grupo de cinco (5) docentes del programa de Psicología del CEAD Ibagué, quienes participaron en la formación de los estudiantes de prácticas profesionales; y otro grupo de cinco (5) egresados del programa de Psicología del CEAD Ibagué para el grupo focal. Por otro lado, los instrumentos aplicados fueron: la revisión documental, la encuesta y el grupo focal. Los datos se analizaron a través de la triangulación de información, con lo cual se estableció que la valoración significativa se expresó al evidenciarse un acoplamiento en el discurso de los docentes, estudiantes y egresados con las directrices de la UNAD, expresadas en diversos documentos como el PAPS y los lineamientos del programa de Psicología, pues en cada uno de estos componentes de análisis se tuvo en cuenta la formación ciudadana como manera de abordar y dimensionar las problemáticas sociales.
Comprender las prácticas evaluativas del profesor de matemáticas para mejorar el aprendizaje de la función
Authors: Gómez Jhon Sebastian , Hurtado Martínez Elizabeth
Published: February 2022

El documento muestra los resultados del proyecto de investigación titulado “Comprender las prácticas evaluativas del profesor de matemáticas para mejorar el aprendizaje de la función lineal”, definiendo como propósitos caracterizar las prácticas evaluativas que realiza el profesor de matemáticas asociadas al contenido matemático función lineal, identificar desde el modelo curricular Análisis Didáctico buenas prácticas evaluativas y proponer una estrategia metodológica para promover buenas prácticas de evaluación que aporten al mejoramiento en la comprensión de la función lineal en el aula de matemáticas.

Para caracterizar las prácticas evaluativas del profesor de matemáticas se identificó la tipología sugerida por Zambrano (2014), y para el diseño metodológico el Análisis Didáctico desde la perspectiva de Gómez (2015), que promoviera buenas prácticas evaluativas. Este referente aportó información relevante desde la didáctica de las matemáticas, las matemáticas mismas y el contexto para el diseño de las tareas matemáticas. Como producto final de esta investigación se tiene como resultado cinco tareas que buscan mejorar las prácticas evaluativas, utilizando el análisis didáctico. La propuesta de evaluación presentada motiva los intereses de los estudiantes, y busca generar escenarios de reflexión acerca de lo que están aprendiendo los estudiantes, cómo lo está aprendiendo y cuál es la responsabilidad del profesor en el diseño e implementación de estrategias de aula que permitan que los estudiantes se acerquen a los aprendizajes y capacidades.
Stakeholder theory and the sustaninable recovery of the tourism sector
Authors:   Universidad de La Laguna (España), Silva-Santisteban Mondoñedo María Elena
Published: February 2022

Stakeholder theory has been applied in the traditional disciplines of business, as well as, in tourism research. This article presents a review of stakeholder theory, putting emphasis on instrumental stakeholder theory, and it presents the application of stakeholder theory to tourism research. It continues with a description of the current situation of the tourism industry and outlines the value of stakeholder theory in the recovery of the tourism sector. It is affirmed that the principles of instrumental stakeholder theory are capable of creating competitive advantage and social welfare. In order to attain both, stakeholder work needs to be performed and stakeholder engagement needs to be undertaken. It is argued that instrumental stakeholder theory might be very helpful for the sustainable recovery of the tourism sector after the pandemic.
El museo fuera del museo. Un nuevo concepto territorial y relacional para el Museo del Mar de Santa Pola y su marca destino
Authors:   Universidad Miguel Hernández (España), Cerdá Bertoméu María José , Soler Beresaluze Susana H. , Pérez Sansano Guillermo
Published: February 2022

Santa Pola (Costa Blanca, Alicante, Spain) is a classic, mature destination, and a popular choice for residential sun and beach holidays. Faced with the need to maintain its value promise and add to and diversify its product, this article explores opportunities and experiences based on heritage tourism. To achieve this, we propose a new way of managing local heritage (cultural and natural) based on a territorial-museum philosophy to be developed by the Museum of the Sea, as an official museum that forms part of the Valencia region’s museum network. In this framework, it is suggested that museums should be authentic tourism agents, focused on conserving heritage, creating heritage-tourism experiences, enhancing the destination’s image and territorial development.
Vacuum-assisted laparostomy with staged peritoneal lavage in management of secondary postoperative diffuse peritonitis: a prospective comparative non-randomised clinical trial
Authors: Uvarov I. B. , Sichinava D. D. , Manuilov A. M.
Published: February 2022

Background. Secondary postoperative diffuse peritonitis (SPDP) associates with a high incidence of abdominal sepsis and 35–92% mortality rate. An optimal surgical doctrine in this complication in lacking to date.

Objectives. An efﬁcacy assessment of vacuum-assisted laparostomy (VAL) with staged lavage relative to relaparotomy on demand (RD) in SPDP patients.

Methods. Patient enrolment and analyses were conducted within period 01.11.2017-31.12.2020, totalling for 141 SPDP patients, 77 (54.6%) males and 64 (45.4%) females aged 64.5 (5972.7) years. Cohort I patients (n = 52) had post-abdominal-lavage VAL using Suprasorb® SNP (SNP-1 and SNP-2) equipment and consumables (Lohmann & Rauscher GmbH, Austria). Staged lavage was performed 48-72 h apart. Cohort II (n = 78) had a standard RD  technique. Cohort III (n = 11) treatment included RD-to-VAL transition. The endpoint was the inpatient treatment outcome, a favourable completion or death. The additional estimated criteria were complications rate and severity (in ACCORDION-modiﬁed Clavien-Dindo classiﬁcation), sepsis rate, C-reactive protein level, abdominal index dynamics, patient’s intensive-care and total-hospital lengths of stay.

Results. Cohort I included 157 staged-lavage VALs, cohort II — 107 RDs, cohort III — 49 operations. The mortality rate was 3/52 (5.8%), 24/78 (30.8%) and 7/11 (63.6%) in cohorts I, II and III (respectively, p < 0.001). No difference was observed in the length of hospital stay, with a shorter intensive care stay after ﬁnal abdominal closure in cohort I. Clavien — Dindo grade 3a complications were observed for 25.0% of cohort I, 60.3 and 45.5% — of cohorts II and III (respectively, p < 0.01); grade 3b complications were 0 (0%), 24.4 and 100% in cohorts I, II and III (respectively, p < 0.001; all 11 patients were reoperated). Multiple organ failure (grade 4b) was reported in 5.8, 30.8 and 63.6% of cohorts I, II and III (respectively, p < 0.001). By end of treatment, sepsis had resolved in 9/11 (81.8%) patients in cohort I, 5/24 (20.8%) and 1/6 (16.7%) — in cohorts II and III (respectively, p = 0.002).

Conclusion. Programmed staged-lavage VAL is an optimal surgical treatment tactics in SPDP. Relative to RD, VAL provides a more effective management of local and systemic abdominal sepsis, lower mortality, fewer and less sever complications, shorter intensive care stays after abdominal closure.
Study of novel 1,4-dihydropyridine derivatives as prospective anti-inﬂammatory remedies: a randomised controlled trial
Authors: Bibik E. Yu. , Krivokolysko D. S. , Batishcheva G. A. , Samokish A. A. , Venidiktova Yu. S. , Myazina A. V. , Pankov A. A. , Frolov K. A. , Dotsenko V. V. , Krivokolysko S. G.
Published: February 2022

Background. Over the past decades, scientiﬁc community is motivated on ﬁnding new anti-inﬂammatory agents with a safe and high-effective proﬁle to manage pathology.

Objectives. A study of the anti-inﬂammatory action of novel compounds, 1,4-dihydropyridine derivatives, in a classical formalin-induced paw oedema test in white rats.

Methods. Originally synthesised 1,4-dihydropyridine derivatives were preliminarily subjected to virtual screening using the SwissTargetPrediction toolkit. White laboratory rats (130 animals) were divided into a control (formalin oedema) and intact group, 4 comparison (meloxicam, sodium metamizole, sodium diclofenac and indomethacin) and 7 experiment groups by the number of 1,4-dihydropyridine derivatives studied. The samples anti-inﬂammatory efﬁcacy was evaluated in acute formalin-induced paw oedema model simulated by right hind limb subplantar injection of 0.1 mL 2% formalin. The studied substances were administered intragastrically at 5 mg/kg 1.5 h prior to oedema induction. Oncometry was assessed quantitatively by limb circumference. Animals were managed in compliance with GOST 33044–2014 “Principles of Good Laboratory Practice” at all experiment steps. Experimental data were analysed statistically to describe quantitative variability with variance σ2, mean limb girth a and standard deviation σ. Data homogeneity and reliability were estimated by variation coefﬁcient V and the Wilcoxon T(W) criterion.

Results. As the most anti-inﬂammatory effective, partially hydrogenated mar-040 pyridines (ethyl 4-({[5-cyano-6-{[2-(diphenylamino)-2-oxoethyl]thio}-4-(2-furyl)-2-methyl-1,4-dihydropyridin-3-yl]carbonyl}amino) benzoate) were shown 33-fold superior to indomethacin, 26-fold — to sodium diclofenac, 25-fold — to meloxicam and 30-fold — to sodium metamizole; mar-037 pyridines (ethyl 4-[({[3-cyano-5-({[4-(ethoxycarbonyl)phenyl]amino}carbonyl)-4-(2-furyl)-6-methyl-1,4-dihydropyridin-2-yl]thio}acetyl)amino] benzoate) — 17–23-fold superior vs. reference drugs. We also show that mаr-014 (ethyl 4-({[5-cyano-6-({2-[(3,5-dichlorophenyl) amino]-2-oxoethyl}thio)-4-(2-furyl)-2-methyl-1,4-dihydropyridin-3-yl]carbonyl}amino)benzoate) and mar033 (ethyl 2-[({[3-cyano-5-({[4-(ethoxycarbonyl)phenyl]amino}carbonyl)-4-(2-furyl)-6-methyl-1,4-dihydropyridin-2-yl]thio}acetyl)amino]-4,5,6,7-tetrahydro-1-benzothiophene-3-carboxylate) compounds are 2.7-fold more effective vs. reference drugs.

Conclusion. The synthesised 1,4-dihydropyridine compounds reveal high efﬁcacy in experimental assays. Selected novel 1,4-dihydropyridine derivatives exhibit a marked anti-inﬂammatory activity and offer value in future preclinical trials.
Drilling Offshore Wells with HP WBM in Extreme HP HT Conditions
Authors: Affede Luca , Lorefice Roberto , Pinto Vieira Larissa , Giubertoni Matteo , Buzzi Lorenzo , Carpineta Gabriele
Published: February 2022

During drilling of three exploration wells challenging conditions encountered, such as temperatures up to 180°C, interbedded highly reactive shales/silts, formation pressures which required mud weights up to 2.35 sg and narrow margin between pore and fracture gradients, posed a host of technical, logistical and cost challenges to Eni activities. These conditions required an accurate drilling fluids design to maximize operational efficiency and to minimize the risks related to such an extreme environment.

Technical demands were particularly critical since the reactive shale formations had historically proved to be difficult to inhibit when drilled with Water Based Mud and might have caused swelling, tight hole, sticky wireline runs, bit-balling and accretion that could have resulted, among other issues, in low penetration rates (ROP). The formation nature coupled with ECD (Equivalent Circulation Density) constraints due to the high mud weight required to cope with high pore pressure, which also caused high mud rheology readings, were therefore the main limits to be overcome to achieve the well objectives.

A tailored drilling fluid program was thus proposed which consisted of an inhibitive HPWBM (High Performance Water Based Mud) that could be converted to an HT-HPWBM, (High Temperature-High Performances Water Based Mud) while drilling, to cross the deeper and hotter sections of the well. This fluid was specifically engineered and optimized after each well in order to contain high concentration of a combination of monovalent salts to guarantee inhibition and reduce solids loading, dedicated polyamine shale inhibitor and fluid loss additives to minimize API/HPHT filtrate and filter cake thickness with the aim to reduce shale water invasion throughout the drilling campaign, graphite to minimizes fluid invasion and fracture propagation and ROP (Rate Of Penetration) enhancer continuously injected using dedicated pump to act as anti-balling and anti-accretion additive.

The achieved results were drilling targets delivered safely, on time and with good overall fluid performances which either reduced or eliminated many of the challenges seen in offset wells, including: no barite sag, rheology stability, and stable long-term mud properties and wellbore conditions even during extended formation logs acquisitions.

This paper covers the design, execution and accomplishments of the water-based drilling fluids employed on three HP/HT wells drilled, together with all of the lessons learned captured, highlighting the evolution of these systems to reach a step-change in terms of performances in such a harsh environment.
Autonomous Viscosity/Density Sensing System for Drilling Edge-Computing System
Authors: Gonzalez Miguel , Adams Robert , Thiel Tim , Gooneratne Chinthaka , Magana-Mora Arturo , Safran Ali , Ghamdi Faisal , Powell Chris , Hulse Ed , Ramasamy Jothibasu , Deffenbaugh Max
Published: February 2022

Current mud monitoring practices are limited due to their reliance on manual measurements such as funnel viscometers, weight balances, or basic field rheometers. These manual practices impose restraints on the quantity and quality of the available data that are essential to ensure optimal and safe drilling operations. In this study, we introduce a new autonomous mud viscosity/density system based on an electromechanical tuning fork resonator. The system was integrated into an edge-computing system for improved data collection and deployment of machine learning models. The system was tested during a live drilling campaign. The viscosity/density sensor is based on an electromechanical tuning fork resonator. The sensor was integrated into a submergible housing for in-tank measurements. Two systems were developed for simultaneous measurements at inflow (possum belly) and outflow (suction pit). The data from the two systems were broadcast wirelessly to the central computer room at the rig for real-time display and data aggregation by the edge-computing system for the development of time-series analysis models using machine learning. During initial field testing, data from a single sensor were collected for various hours at a rate less than a sample per second. The test allowed for continuous monitoring of the mud consistency not accessible by current measurement practices. The data demonstrated the potential to perform real-time calculation and display of drilling parameters and to detect anomalies in the fluid that might be indicative of developing operational problems, which would enable the instrument to be used as an early-warning system and real-time calculation of drilling parameters. The system detailed here provides an essential building block to enable drilling automation. The robustness and compactness of the instrument allow it to be installed at various points in the mud circulation system for the generation of large data sets that can be processed using modern analytics algorithms in an edge-computing framework.
Numerical Simulation of Effects of Shock Waves on the Integrity of Cement Sheath
Authors: Zhan Guodong David , Xu Jianhui , Aljohar Abdulwahab , Guo Huijuan
Published: February 2022

With the application of electrical explosion technology in the oil industry, such as plugging removal, wax removal and micro-cracks generation near the wellbore zone, the impact of shock wave generated by the electrical explosion on cement sheath cannot be ignored. To study the effect of electrical explosion downhole application on the integrity of cement sheath during the operation, a numerical simulation was used. A composite model of casing-cement sheath-rock is established in AUTODYN. The propagation process of shock waves in the composite and the damage to cement sheath are studied. The results show that the shock wave propagates through the perforation hole in the liquid and keeps a high pressure in the perforated area. In the nonperforated area, the shock wave decays greatly after passing through the casing pipe. When the cement sheath and rock are subjected to the shock waves, the damage area extends outward from the perforation hole. The cement sheath and rock are mainly subjected to the tensile and shear damage by the shock waves. What is more, the casing deformation caused by shock waves may also contribute to the damage of cement sheath behind the casing.
Dynamic Optimization of Underground Gas Storage Reservoir Rate Schedules
Authors: Ilamah Osho , Altwaijri Mohammad Hamad
Published: February 2022

Depleted or partially depleted natural gas reservoirs are attractive candidates for large scale underground gas storage. The effectiveness of underground gas storage reservoirs as a means of addressing seasonal gas demand and supply imbalances depends on optimal storage and withdrawal rate scheduling. In this paper, we develop an approach to maximize seasonal underground gas storage reservoir performance by dynamic optimization of storage and withdrawal rate schedules. Our method involves coupling a predictive gas production and storage system model to a non-invasive population-based optimizer. Optimization is performed progressively on a receding time horizon over which updated optimization requirements and constraints may be admitted. An example application is included to illustrate the approach. The results show that dynamic rate schedule optimization can significantly improve underground gas storage reservoir performance.
Achieving Reservoir Performance Excellence By Implementing Automated Sector Performance: Onshore Field Case Study
Authors: Martinez Santiago Maryvi , Al Shehhi Shamma , Anbalagan Sukilesh , Shbair Alaa , Noordin Firdaus , Trisnadi Dicky , Adli Mustapha , Surya Krisna
Published: February 2022

ADNOC has development and implemented a robust automated sector performance review (SPR) process using state-of-the-art analytics and business process management tool (Khan et al., 2019). In this paper, we will present the achieved results and the defined opportunities by implementing SPR across some targeted reservoirs during the previous last 2 years.

With the necessity of having analysis at sector level, the main objective of this work is to conduct an integrated reservoir dynamic synthesis, identify all challenges and opportunities to come up with robust and practical action plan aiming for best reservoir management and ultimately obtain best oil recovery by sector.

Applying Integrated Reservoir Management (IRM) Workflow on Giant Onshore Field, It was decided to start the project on one major Reservoir A (divided by 3 sectors) as a project pilot.

First data were collected from operations database, data management in spreadsheets, simulation output format, maps and images. All data were organized into the automated SPR workflow through a web based Business Process Management (BPM) that provided mechanism for the user to load, validate and approve technical data.

Setting the workflow to focusing on analyzing the reservoir performance at sector level, the data is illustrated in an integrated visualization environment including panels for the reservoir KPI, production plan compliance status and reservoir pressure maintenance, diagnostic plots, production and injection summary, etc., opening the possibility for the user to identify new opportunities and areas that needs further investigations.

Few key enhancements are listed and were suggested to the solution as a next phase.

Following a methodical SPR automated workflow these conclusion are drawn:

Technical data can be approved with appropriate notification for task execution. Data processing cycles, visualization and performance analysis dashboard time frame was reduced. It was identify the underperforming areas into the sectors. The Opportunity Management proactive system was used to identify reservoir profitable opportunities through a centralized platform. Action plans include well and surface intervention. 20% of the activities were successfully implemented and provided significant added values.

Implementation of the automated SPR workflows as part of Digital technologies is renovating the traditional work process into very effective and advanced analytics and has achieved excellence in reservoir management and reserves recovery.
The Utilization of Machine Learning Method to Predict Hydrocarbon Flow Rate for a Better Reservoir Potential Evaluation
Authors: Kasim Fadzlin Hasani , Idris Nurul Nadhira , Majidaie Saeed , Kantaatmadja Budi Priyatna , Ahmed Siddiqui Numair , Sidek Akhmal , Yahaya Nur Aqilah Nabila
Published: February 2022

The numbers of machine learning technologies used in subsurface characterization work is increasing with more company rely on data driven to assist in performing any evaluation. In this study, a supervised random forest machine learning approach was utilized in two stages; first stage was to predict static reservoir using well logs and core as inputs. The output is then used as the basis in the second stage to predict initial oil rate (Qi) and subsequently to determine estimated ultimate recovery (EUR) at targeted interval as proposed in the first stage.

Static reservoir machine learning prediction outputs were benchmark with available routine core analysis with the result showed R2 of 88% respectively. For initial oil rate (Qi) prediction, a total of 9000 observation points from 20 wells were extracted for training and blind testing process by using variables such as permeability, net thickness, well choke size, well flowing pressure, average pressure, water cut, irreducible water saturation (Swi), and historical production rate. The estimated ultimate recovery (EUR) is then predicted utilizing the thickness of that unit and the decline rate that is obtained from the neighboring wells that has produced from the said reservoir as the analogue. The Qi and EUR results from machine learning is compared with the estimated Qi and EUR using conventional methods for verification purpose.

The results from machine learning dynamic properties prediction showed 97% R2 for training while the testing score mean is 87% against the historical data.

High R2 from static and dynamic machine learning prediction indicated that the method was reliable and able to assist petroleum engineer in reservoir potential evaluation process.
Rigorous Multi-Variate Characterization and Modeling of Paleo Zone for Simulation Model History Matching under Reservoir Uncertainty
Authors: BuKhamseen Nader Y. , Maucec Marko , Awan Anwar R. , Saffar Ali H. , Nieto Jorge E.
Published: February 2022

Presence of paleo zone, which frequently exists below Free-Water-Level surface, can impact dynamic reconciliation of reservoir simulation models. The process is even more challenging with embedded complex representations of reservoir connectivity (conductive fractures) and inherent uncertainty associated with geological and flow modeling. We present a rigorous approach that integrates characterization of paleo zone, parameterization of paleo zone conductivity and application of flow profiles as a guide in accelerated history matching study of large-scale Dual Porosity-Dual Permeability model. The presence of immobile oil within paleo zone can cause permeability reduction and inherently limit aquifer support to oil zone. Accordingly, such occurrence can be represented as a low permeability streak or region in the simulation model and leveraged for more accurate calibration of model injection wells located inside the paleo zone. We performed probabilistic sensitivity analysis and parameterization of paleo zone conductivity using Design of Experiments on a synthetic simulation model with optimized aquifer size and strength as the basecase. The outcome of the synthetic sensitivity scenarios using dynamic model strongly indicates that paleo zone is partially sealing. Multiple scoping runs were performed to identify appropriate permeability values required to calibrate the model. The use of multipliers in porositypermeability transform reproduces blocking or baffling effect of the paleo zone, considering this fluid will behave as part of the rock framework. Porosity and permeability were recomputed inside the paleo zone based on Bulk Volume of Water (BVW) data assessment. The higher the BVW the higher the chance to have effective communication between the oil leg and aquifer. These multipliers represent the probability of the sealing character of the paleo zone and reflect on the non-uniform distribution of accumulated hydrocarbons. Above methodology was used to define the initial set of paleo zone petrophysical property modifiers, rendering multiple model realizations within optimistic-pessimistic range. Flow profiles can be used to guide segmentation of paleo zone with preferential well injectivity to further improve the efficiency of history matching process. Our paper demonstrates a successful application of multi-variate characterization and modeling of paleo zone geometry and properties for a history match of a conceptual, complex reservoir simulation model under reservoir uncertainty. An innovative approach to probabilistic parameterization of paleo zone conductivity has contributed to a model with exceptionally high quality and rendered a reservoir simulation model with reliable predictive capability in accelerated time.
Expediting Well Delivery Through Integrated Drilling Systems Optimization: A Case Study of a Challenging Deep Field in the Middle East
Authors: Ovwigho Efe , Corona Mauricio
Published: February 2022

While drilling vertical deep gas wells, it is required to drill 1300 ft of 5-7/8" section across highly interbedded, abrasive and very hard sandstone formations with compressive strength of 25 – 40 kpsi. This operation is plagued with numerous BHA and bit failures and an average of 3-4 BHA runs are required per well. The current push for expedited well delivery, has made it necessary to optimize the Drillstring in order to enhance performance.

Performed BHA FEA which revealed that the current drillstring was not optimized for drilling long highly interbedded sections. The analysis revealed that the BHA would generate high levels of shocks and vibration that would lead to BHA and bit failue. The model recommended the replacement of all 4" drillpipes by 5-1/2" drillpipes to improve the BHA stability. Optimum drilling parameters were recommended by the model.

Dynamic modelling of the conventional bit also showed that it was not suited for very hard interbedded formations. Introduced high impact rolling diamond cutting element to the conventional bit in order to maintain cutter sharpness for improved drillbit durability. This new cutters were strategically placed across the bit in order to enhance the bit's resistance to impact and abrasion. The conventional motors were also replaced with greater torque motors.

The drilling parameters were also reviewed, revamped and standardized. Operations monitoring team was set up to flag deviations during execution. Wells were assigned for the implementation.

The optimized BHA solution proved to be very stable. While drilling the section, did not observe string stalling, vibrations, shocks or erratic torque. Doubled the on-bottom ROP while drilling this section. The wellbore was in great shape and while POOH to the shoe, no tight spots were recorded and this resulted in the elimination of the planned wiper trip. This solution led to average savings of approximately 140 hours on this well. This consist of 80 hours due to improved ROP, 8 hours due to elimination of wiper trip and 52 hours due to the elimination of two round trips to pick up new BHAs. In addition, the well was delivered six days earlier due to this integrated solution. This solution has been adopted on the Project.

This paper will show how simple changes in drill string design can lead to huge savings in this current climate where there is a constant push for drastic reduction in well costs and expedited well delivery. It will explain the step-by-step process that was followed prior to implementing this integrated solution.
A New Local-Global Upscaling Method for Flow Simulation in Naturally Fractured Reservoirs
Authors: He Xupeng , AlSinan Marwa , Kwak Hyung , Hoteit Hussein
Published: February 2022

Explicit modeling of discrete fractures at the field scale is computationally intensive, and therefore, upscaling the fractures in the context of an equivalent continuum model is indispensable for field-scale simulations. This work introduces a new upscaling technique based on the local-global multiple-boundary (LG-MB) upscaling method for fluid flow in naturally fractured reservoirs.

We extend the commonly-used local-global (LG) upscaling method by introducing a new approach to compute the fluid fluxes within the fractures crossing the grid-block boundaries. This method is based on the multiple-boundary (MB) approach applied within a local-global upscaling procedure. The global coarse-scale simulations are implemented to determine the boundary conditions for the calculations of local upscaled permeability. The procedure allows repeating the process to assure consistency between the global and local calculations. We implement the multi-point flux approximation (MPFA) finite volume method coupled with embedded discrete-fracture model within the MRST framework. We then verify the proposed LG-MB upscaling technique by comparing it with the reference fine-scale solutions and other existing local and local-global upscaling techniques. Results show that the proposed approach provides pronounced accuracy compared to local-based upscaled models with minor computational overhead. Compared to traditional local-global upscaling techniques, it provides more computational accuracy yet with the same efficiency. The superiority of the proposed LG-MB upscaling technique is attributed to two factors related to 1) the multiple-boundary technique to capture the flow behavior with high anisotropy from fractures non-alignment with the grid; 2) the use of local-global approach to accurately obtain the real flow boundary conditions. This work introduces a new upscaling method for flow simulation in naturally fractured reservoirs. We demonstrate its applicability in field applications and its superiority to existing upscaling methods. This method is accurate and straightforward and can be implemented within existing upscaling workflows.
Residual Oil Saturation Estimation from Carbonate Rock Images Based on Direct Simulation and Machine Learning
Authors: Rizk Ahmed Samir , Tembely Moussa , AlAmeri Waleed , Al-Shalabi Emad W.
Published: February 2022

Increasing global oil demand, combined with limited new discoveries, compels oil companies to maximize the value of existing resources by employing enhanced oil recovery (EOR) techniques aimed at the remaining oil. Estimating residual oil saturation (Sor) in the reservoir after conventional recovery techniques, such as waterflooding is critical in screening the suitable EOR technique and in further field development and production prediction. The objective of this work is to provide an artificial intelligence (AI) workflow to assess Sor of carbonate rocks, which will aid in the development of a long-term strategy for efficient production in this fourth industrial age. In the present work, two-phase lattice Boltzmann method (LBM) simulation was used with the benefit of high parallelization schemes. After applying the CPU-based solver using LBM on thousands of carbonate rock digital images, an AI-based workflow was developed to estimate Sor. Different advanced tree-based regression models were tested. Relevant input features were extracted from complex carbonate micro-CT images including porosity, absolute permeability, pore size and pore-throat size distributions, as well as rock surface roughness distribution. These features were fed into the learning models as inputs; while the output used to train and test the models is based on the direct simulation results of Sor from the image dataset.

The results showed that extracting the engineered features from images aided in building a physics-informed machine learning model (ML) capable of accurately predicting Sor of carbonate rocks from their dry images. Three ML models were trained and tested on more than 1000 data points, namely gradient boosting, random forest, and xgradient boosting. Even with such small number of data points, the three models yielded promising results. Gradient boosting algorithm showed the highest predictive capability among the three techniques, with an R2 of 0.71. Increasing the number of data points is expected to help the models capture wider ranges of rock properties, and consequently, result in an increase in the prediction capability of the models. To the best of our knowledge, this is the first study that leverages machine learning to estimating residual oil saturation in complex carbonate. This work will contribute to the development of a novel framework for estimating accurately and reliably residual oil saturation of heterogeneous rocks. As a result, this research will aid in providing decision-makers with a simple tool for screening the most suitable EOR technique for optimal asset use.
An Experimental Study on Shale-Fluid Interactions to Help Drill Stable Shale Formations
Authors: Al-Arfaj Mohammed K.
Published: February 2022

Abstrac

Drilling stable boreholes in shale formations is a challenging task due to the potential interactions of clay-rich shales with aqueous phase of drilling fluids. The objective of this paper is to look into some testing techniques utilized to characterize shale rock samples and assess the reactivity, recommend a strategy and best practices to overcome the wellbore instability problems when drilling shale formations.

Wellbore instability associated with drilling shale formations is a major source of non-productive time in drilling operations. Inhibiting shale-fluid interactions and minimizing the scope of time-dependent shale problems are essential to mitigate this challenge. The solution is of two-fold: mechanical and chemical. This paper addresses the chemical aspects of the problem and recommends a novel type and combination of shale inhibitors to use in drilling.

Different testing techniques were employed to assess the shale reactivity with different types of drilling fluids. After characterizing the shale samples in terms of mineralogy, petrography and inhibition testing has been carried out to reveal the reactivity extent of the shales. Dispersion test predict the shale-fluid interactions to help in optimizing the drilling fluid formulations to drill those shale formations. Two shale inhibitor combinations were tested against shale samples with different characteristics and clay mineralogy to develop a semi-universal type of inhibitive drilling fluid formulation.
A New Approach to Quantify the Wellhead Performance for Gas Condensate Reservoirs Using Artificial Intelligent Techniques
Authors: Eliebid Mohammed , Hassan Amjed , Mahmoud Mohamed , Abdulraheem Abdulazeez
Published: February 2022

Selecting the optimum choke size that can deliver the optimum gas flow rate is extremely complex in gas condensate wells because of the high unpredictability of two-phase flow behavior and the changes in gas-liquid ratio with pressure and temperature. There are many analytical and empirical correlations in the literature, that describe the two-phase flow through wellhead chokes in critical conditions. However, the nature of the flow in most gas condensate wells is sub-critical, and using these correlations causes severe errors in the estimation of flow parameters. Also, the models available for sub-critical flow are not providing satisfactory accuracy for prediction and are often difficult to use. The objective of this work is to develop reliable models for predicting choke performance. Artificial intelligent (AI) based models were developed to accurately design the optimum wellhead choke size, based on the required flow rate, pressure difference, and gas to liquid ratio. The newly developed models are accurate and easy to be used, comparing to the existing predictive models. In this work, artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were applied to estimate the gas flow rate from gas condensate wells. Among the developed models, ANFIS with a subtractive clustering approach performed the best with a percentage error of 2.4% and coefficient of determination (R2) of 0.981. The newly developed models can improve production management by proposing the optimum flow parameters that help in regulating the flow rate of two phases from gas condensate wells. Also, they can help in stabilizing the flowing pressure downstream the choke and prevent downhole reservoir formation damage, by providing the required back pressure to avoid excessive drawdown. Overall, the presented models provide an easy
Expanding the Degradable Fluid Loss Additive Application Spectrum Through Advanced Slurry Transport Modeling, Theoretical and Experimental Integration
Authors: Khan Abdul Muqtadir , Isaev Vadim , Plyashkevich Vladimir
Published: February 2022

Chemistry and the materials portfolio play a central role in fracturing success. Challenges in a achieving successful and productive proppant pack are resolved using varying products and fit-for-purpose chemistry utilization. The conventional understanding of using fluid loss additive (FLA) only for reducing fluid leakoff in high-permeability formations can be extended with advanced digital tools for optimum realization of a broader application spectrum for well-specific challenges.

Polylactic acid-based powdered degradable FLA (DFLA) was developed with engineered particle size distribution to plug rock pore throats. Core flow tests were conducted with and without DFLA with borate crosslinked base fluid to measure the performance metrics. The application spectrum was extended beyond the fluid loss control in high-permeability rock to aid in screening out multiple fractures and natural fractures, reducing poroelastic tendency of tight, tectonically influenced formations. An advanced numerical modeling simulation approach was used to evaluate the distribution of the DFLA particles along the fracture cross-section and their dynamics to yield optimum fracture geometry with lesser pad volume.

Coreflood tests demonstrated a reduction of up to 40% in fluid loss coefficient and spurt loss components with 30 lbm/1,000 gal DFLA loading. A regained permeability reduction of 12% from the baseline was observed when 25% particulate DFLA mass loss occurred, which can be minimized with higher shut-in times and complete degradation. The spectrum was expanded conceptually for up to eight applications based on literature references. Digitally advanced hydrodynamics and an in situ kinetics simulator were used to accurately model the slurry flow with and without DFLA. The model was extended with a sensitivity study with 32 synthetic cases to extend FLA utilization to medium- and low-efficiency formations. The modeling results showed that more than 50% of crosslinked pad volume could be saved while retaining the same fracture geometry evolution.

Industry use of FLA chemistry has been minimal. In the digital age, this is the first and a unique demonstration of how digital tools can aid extending the material portfolio spectrum investigated from laboratory, simulation, and field case perspectives. Multiple applications of FLA can enhance project economics and reduce polymer and fracturing fluid formation damage by lowering the difference between differential pressure at the fracture face and drawdown during cleanup and the production phase.
Novel Stuck Pipe Troubles Prediction Model Using Reinforcement Learning
Authors: Alzahrani Majed , Alotaibi Bader , Aman Beshir
Published: February 2022

Predicting stuck pipe problems during oil and gas drilling operation is one of the most complex problems in the drilling business. The complexity of the problem is driven not only by the complexity of the natural factors, but it extends to the nature of the drilling operation itself. The drilling operation is continuously influenced by a dynamic smart system. The dynamic part of the system is impacted by natural forces like formation related characteristics, and also is impacted by human activities during the operation such as drilling, tripping and hole cleaning. The smartness of this system is driven by the fact that the operation is controlled by a number of experts, i.e. drilling engineers, trying to run the best sequence of operations using best operation parameters to achieve operation objective. At the top of that, the engineers can change their operation plan whenever they find it necessary to address any operational condition, including a potential stuck pipe problem.

In this paper we prove the stuck pipe prediction problem is not a binary classification problem. Instead, we define the stuck pipe prediction problem as a multi-class problem which takes into consideration the dynamic nature of the drilling operation. A reinforcement learning based algorithm is proposed to solve the redefined problem, and its performance and evaluation results is shared in details. The accuracy of the developed algorithm in terms of detecting true stuck pipe events is shown. The results will compare the performance of different machine learning algorithms, which is then used to justify the selection of the best performing method. In addition, we show the accuracy performance improvement through time by employing the feedback channel to retrain the model. The presented method is using a reinforcement logic, in which the solution is connected to the operation reporting to label the solution prediction for false and true predictions. This information is then used to return the neural networks to learn new operational patterns to enhance accuracy.
Application of a Sustainable Cognitive Twin Solution for Drilling Hazard Avoidance and Optimization
Authors: James Chris , Sankaran Sathish , Hinshaw Chris , Fallah AmirHossein , Borjas Ricardo , Adams Craig
Published: February 2022

Model-based methods such as torque and drag, hydraulics, hole cleaning, and downhole dynamics have improved over the past years for pre-job design and post-analysis of drilling operations. However, there is a still a large gap in extending these traditionally manual processes and scaling them up for model-based, real-time decision support systems. Significant opportunities exist in applying these towards drilling hazard avoidance, performance diagnostics, and optimization. It is desirable to have an automated solution that orchestrates data ingestion and model calculations based on physics-based, data driven, or hybrid methods, to analyze real-time field data and enable valuable insights into operations. In this context, cognitive twins refer to the ability to deliver actionable insights for real-time optimization and hazard avoidance through systematic automation of data and model pipelines.

In this work, a sustainable real-time solution is proposed comprising of data aggregation, cleansing, analytics, and cognitive twins to enable the automated orchestration, analysis, and insight delivery from drilling models. This process drives the scalability of the system – ensuring evergreen models and analysis are always available across all rigs and wells in a drilling fleet. Methods of modeling and analysis combine data driven methods with physics-based constraints to compute certain key quantities (e.g., friction factors, pressure deviations, and cuttings bed height). These quantities contain results and intermediate features which can be used for machine learning methods to drive fleet performance optimization above and beyond well-level analysis.

We applied these methods to both onshore and offshore wells in real-time to identify and automatically detect several early warning signs of common drilling problems such as stuck pipe, washouts, and poor hole cleaning. Issues related to friction, hydraulics, cuttings transport, general drilling efficiency, and combinations of the previous categories are translated into actionable insights, which can be immediately implemented in the field to enhance safety, reduce non-productive time, and increase performance. In addition, the insights were also analyzed at a fleet-wide scale to identify patterns and drive operational best practices. Common challenges and solutions relating to implementing such a system at scale are addressed.

This methodology addresses and overcomes many of the challenges involved in applying digital twins in a sustainable, real-time mode. It extends functionality into cognitive twins that can analyze modeled behavior to real-time results and enables automated insights to be generated for single models such as torque and drag in isolation and combinations of models such as the insights derived from interpreting torque and drag, hydraulics, and hole cleaning results simultaneously.
An Integrated Directional Drilling Simulator with Steering Advisor and Self-Learning Algorithm
Authors: Liu Yang , Marck Julien , Tian Kaixiao , Demirer Nazli , Pho Vy
Published: February 2022

The industry has recently put a significant emphasis on optimizing the directional drilling process to reduce non-productive time (NPT) and improve well-bore quality. Mud motors are widely used due to their low operational cost and high reliability. In this paper, a drilling advisory system for mud motors has been developed to provide steering recommendations in real time to maintain smooth wellbore trajectories respective to a given well plan while satisfying operational constraints. The drilling advisory system, which was tested both offline and online on 20 of runs across North America and globally, provides reliable steering decisions relative to the surveys and real-time data, when available. This capability revolutionizes the drilling industry by providing a tool that guides directional drillers toward improving efficiency, consistency, and quality. Moreover, the advisory system serves as the core to the development and implementation of a fully autonomous drilling system.