bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unlocking the Value of Cloud-Enabled Emission Monitoring Systems for Sustainable Operations in the Oil and Gas Industry
Authors: Saboo S. 
Published: October 2023

Abstract
The oil and gas industry plays a pivotal role in meeting global energy demands, but it faces increasing pressure to address environmental sustainability (Dahai Zhang 2017). The importance of sustainability metrics has surged in recent years, prompting senior leadership to optimize financial, operational, and environmental aspects. The global pandemic further accelerated this focus on sustainability, emphasizing the need for accurate emission measurement, transparent modeling, and informed decision-making based on ESG data (Niu 2022). To address these challenges, this paper explores how industry leaders can unlock the value of cloud-enabled Emission Monitoring Systems (EMS) to achieve sustainability goals and generate additional value.
Cloud-enabled EMS, empowered by advanced technologies, provides innovative solutions for monitoring and managing emissions, offering real-time insights and facilitating data-driven decision-making (Uyanik 2019). By leveraging the power of the cloud, energy companies can harness advanced analytics, automation, and scalable infrastructure to enhance their environmental performance and contribute to a more sustainable future.
This paper delves into industrial trends and challenges faced by leaders in emissions monitoring, examines how digitalization can overcome these challenges, and highlights the latest developments in cloud-enabled EMS from a business perspective. It also features a comprehensive case study showcasing an independent oil producer’s journey in the United States, illustrating tangible benefits achieved through the adoption of cloud-enabled EMS technologies.
By analyzing the benefits, challenges, and recommendations associated with EMS and cloud-enabled solutions, this paper aims to contribute valuable insights for the oil and gas industry. It showcases how these technologies can enhance emissions monitoring and reporting, improve data analysis and decision-making, and lead to significant cost savings. Ultimately, the paper demonstrates the value cloud-enabled EMS systems can add to sustainable operations in the oil and gas industry. By addressing challenges and fostering collaboration, energy companies can drive sustainability, achieve their environmental goals, and contribute to a greener and more resilient energy industry.
Seismocracks: Tectonic Origin of Stylolites, Abu Dhabi Carbonates
Authors: Noufal Abdelwahab , Alshehhi Hamda 
Published: October 2023

Abstract
Stylolites have been defined as irregular planes of discontinuity at the bedding boundaries caused by pressure solution, which leads to concentration of relatively insoluble materials of the surrounding/enclosed rocks. Factors controlling stylolite formation, and the relationship between stylolites and the porosity of the enclosing reservoirs were subject of many studies. The published literatures identified the pressure solution as the carbonate diagenesis that influenced by mechanical compaction, chemical changes, water flow and cementation. The pressure extension within the carbonates is affected by the grains size, shape, and type, in addition to the clay content and temperature. Others adding the role of stress, which is responsible for stylolites development and distribution. This paper justifies, the tectonic origin of stylolites.
The stylolites were studied in many carbonate reservoirs of Abu Dhabi fields and comparing the cores with extensive outcrop studies. The collected data are also investigated from the water zone, on the flanks of the structure with cores from the oil zone, on the crest. The main controlling factor of stylolites initiation and propagation is at the tectonic boundaries and tectonic events showing its tectonic origin. The present study addresses a unique approach to solve this feature and hence understanding its impact on hydrocarbon flow, migration and trappment. The development of stylolites creates a reduction in the porosity, permeability and the bed thickness of the reservoir, the latter were not observed in Abu Dhabi as there is no bed thickness reduction were addressed.
Stylolites are principally serrated surfaces formed due to tectonic stress events and their amplitude is dependent on the stress dissipation stages. In addition, it is found some stylolites played as microfaults, which justifies the tectonic origin. This contradicts the previous interpretation, which justifies the needs of compaction, although compaction needs weight of overburden, while the pressure solution process happening in shallow depths. As the fluids migrates, two situations arising, the first is when the maximum stress is vertical, where along the serrated shearing surfaces results in dissolving and accelerating the crests left behind leading to dissolution propagation. The second when the maximum stress is parallel to the bedding, leading to slowing down the crests propagation left behind. In case, the bedding planes are not sheared enough, the connectivity is very less leading to trapped fluid, which facilitate dissolving and penetrating the serrated areas along the minimum stress direction.
The results should be relevant to unravel and predict: the spatial and temporal distribution of Seismocracks and their roles in reservoir performance and production. The Seismocracks are thus of first-order importance for reservoir performance.
Integrating the Dielectric and NMR Techniques to Investigate the Petrophysical Properties of Sandstone and Carbonate Formations
Authors: Oshaish Ali , Elsayed Mahmoud , Hassan Amjed , Al-Ofi Salah , El-Husseiny Ammar , Mahmoud Mohamed 
Published: October 2023

Abstract
The nuclear magnetic resonance (NMR) and the dielectric dispersion are excellent techniques to study the petrophysical properties of hydrocarbon bearing rocks. The superiority of these measurements rises from their sensitivity to the different interactions occurring in the porous medium which include grain – fluid and fluid – fluid interactions. Therefore, an integration between both measurements helps in evaluating different petrophysical properties such as mineral composition, porosity, permeability, wettability and fluids distribution.
In this study, we have explored the dielectric and the NMR responses to pores structure, permeability, and saturation profiles for two sandstone samples (Berea and Fontainebleau) and two Indiana limestone samples with different permeability values which were undergone a centrifuge drainage. Initially, routine core analysis was performed on the samples including porosity, permeability XRD and Scan Electron Microscope (SEM). The rock samples were studied at 3 saturation points, dry condition, 100 % water saturation (Sw=1) and irreducible water saturation (Swirr). To reach the Swirr conditions, the sample was undergone a drainage cycle with model oil using the centrifuge.
The results showed an excellent correlation between NMR, dielectric measurements and the petrophysical properties. The small pores and the lower permeability (21 md) of one of the carbonate rock compared to the sandstone samples (74 md for Fontainebleau and 100 md for the Berea) affected the sweep of the water during the centrifuge process which resulted in a variation in the saturation profile confirmed by the NMR saturation profile and spatial T2 techniques and higher Swirr value (43%) in the carbonate sample while the sandstone samples showed uniform fluid distribution and less Swirr (26% and 9.51% for the Berea and Fontainebleau samples respectively). Consequently, a significant variation between the dielectric measurements of the inlet and the outlet was observed in the carbonate sample with low permeability. Overall, this study provides a new workflow of integrating the dielectric and the NMR techniques to assess the impact of petrophysical properties such porosity, permeability, and pore sizes on the saturation profile across the sample resulted from the centrifuge displacement which consequently affect the dielectric and NMR responses. Ultimately, this work would help in improving the characterization of carbonate and sandstone formations by integrating the dielectric and NMR measurements.
Rapid High-Fidelity Forecasting for Geological Carbon Storage Using Neural Operator and Transfer Learning
Authors: Falola Yusuf , Misra Siddharth , Nunez Andres Calvo 
Published: October 2023

Abstract
Carbon sequestration is a promising technique to minimize the emission of CO2 to the atmosphere. However, the computational time required for CO2 forecasting using commercial numerical simulators can be prohibitive for complex problems. In this work, we propose the use of transfer learning to rapidly forecast the CO2 pressure plume and saturation distribution under uncertain geological and operational conditions, specifically for variations in injector locations and injector rates. We first train a Fourier Neural Operator (FNO)-based machine learning (ML) model on a limited set of simple scenarios. Then, we use transfer learning to fine-tune the FNO model on a larger set of complex scenarios. Most importantly, the CMG forecasting time for one scenario requires approximately 40 to 50 minutes, which was drastically reduced to 12 seconds by using Fourier Neural Operator and then reduced further to 8 seconds by implementing transfer learning on the Fourier neural operator. The mean relative errors of the neural operator predictions of pressure and saturation were 1.42% and 7.9%, respectively. These errors get slightly higher when transfer learning is implemented on neural operator to learn complex task with less amount of data and low training time. Our results show that transfer learning can significantly reduce the computational time required for CO2 forecasting. The data generation and model training times were reduced by 50% and 75%, respectively, by using transfer learning on the Fourier neural operator. Additionally, the total number of trainable parameters was reduced by 99.9%. Our results demonstrate the potential of transfer learning for rapid forecasting of CO2 pressure plume and saturation distribution. This technique can be used to improve the efficiency of CO2 forecasting and to help mitigate the risks associated with CO2 leakage.
The Challenge to Integrate Sedimentology and Well Completion to Predict Production Performance in an Unconventional Reservoir
Authors: de Melo Ricardo C. B. , Mykhaylenko Sergiy , Hosur Laksmikantha Rao 
Published: October 2023

Abstract
This study presents the integration of sedimentology and well completion to explain variable production performances in wells from an unconventional siliciclastic reservoir. The significance of our study is that multidisciplinary approaches are among the most comprehensive solutions that can forecast areas with different performance and, thus, make consistent completion strategies before drilling and optimize the development of unconventional reservoir acreages.
The main producing interval within the area of interest includes cm-thick, carbonate-rich layers that based on a total of 20 vertical wells are inferred to extend several km. The cumulative thickness of these layers and their frequency (number of layers/thickness of producing interval) vary across the area. This variation is compared to hydraulic fracturing and completion data that include breakdown pressure, pressure decline, hammer effect, ISIP gradient, fracture driven interactions, and completion strategies (i.e., proppant intensity, volume of fluids, cluster, stages, well spacing). All these data are then compared to the reported production from horizontal wells (&gt; 150).
Both the correlation of vertical wells and the distribution maps predict the existence of higher cumulative thickness and frequency of carbonate-rich layers towards the center of the study area. Also, the frequency of carbonate-rich layers reveals a positive relationship with the normalized production of nearby horizontal wells, which points to increased production as brittleness of the system increases. However, a variable degree of data dispersion is seen in this positive relationship, and it is attributed to different completion strategies over the years. One way, though, to explore the relationship between completion parameters, sedimentology, and well production is to use a correlation matrix that allows to differentiate the relative importance of each factor. For instance, we observe a strong and negative correlation between the frequency of carbonate-rich layers and the ISIP gradients, whereas the ISIP gradients and the well production exhibit a small, albeit positive, correlation. Completion and hydraulic fracture designs undoubtedly influence well production but there exists an inherent anisotropy, related to the presence of cm-thick, fragile, carbonate-rich layers between m-thick, plastic, clay-rich intervals, that affects the toughness of the system and, ultimately, impacts well production.
This work presents solution seeking through integration of multidisciplinary approaches with application of data analytics to create predictive methodologies. The use of two different disciplines such as sedimentology and completion design, rarely integrated together for an unconventional play, is novelty by itself. Correlation matrices could play a fundamental role in the optimization of field development plans in general and bespoke hydraulic fracture design.
A Novel Approach for Improved Accuracy in Water Volume and Reservoir Quality Assessment During Field Development in Organic Shales
Authors: Bustos U. D. , Bernhardt C. , Vera L. 
Published: October 2023

Abstract
Complexity in petrophysical interpretation in organic shales is related to the presence of inorganic and organic pores that house hydrocarbons, kerogen, and water. Advanced workflows were proposed for assessing hydrocarbon types and reservoir maturity. The water volume and saturation assessment are routinely done with archie equations, using resistivity, nuclear and/or sonic measurements. With proper matrix corrections on porosity computation and representative inputs of formation water salinity (Rw), wettability (N) and tortuosity (M), water saturation can be calculated. In organic shales, however, the archie equations parameters can be highly variable across a formation and, a well-known thermal maturity effect on resistivity logs (higher maturity tends to cause a drop in resistivity), leads to incremental uncertainty on formation water assessment.
During field development stage in Vaca Muerta formation, Argentina, a complete electrical logging acquisition is carried out in pilot wells. With this information, landing zones for placing the horizontal-producer wells are defined. Since the criteria for landing zone definition is strongly conditioned by water saturation, we focus on uncertainty reduction in water volume estimation as a first step and then calculation of a new practical reservoir quality indicator that matches local production behavior.
Given the independence to archie equations and similar depth of investigation, dielectric dispersion and nuclear magnetic resonance logging became of increasing interest for water and hydrocarbons volumes and saturation computation. In Vaca Muerta shale oil targets, we developed an integrated method based on dielectric dispersion, magnetic resonance, formation capture cross section and spectroscopy-derived formation chlorine for early assessment of representative water volume and saturation for best landing zone selection with producible hydrocarbons and minimum free water.
We built a matrix model from spectroscopy dry weights, nuclear and NMR logs, solving for minerals, kerogen, and organic/inorganic matrix-corrected porosity. With a clustering technique applied on data from a new high-resolution NMR T1-T2 processing and from Dielectric inversion, with inputs from total formation capture cross section and spectroscopy derived chlorine dry weight, we obtain two independent water volumes. Through iterative process, a representative water volume is achieved whenever differences are below two porosity units. Then, incorporating inorganic/organic corrected porosity, formation total water saturation is derived. Additionally, by splitting volumes of pore water and hydrocarbon in both organic and inorganic pores, we compute a continuous reservoir productivity index that considers both producible hydrocarbons and combination of bound oil and inorganic pore water.
Digital Transformation Thru MPD Automation
Authors: Ziadat W. , Kadadha A. 
Published: October 2023

Abstract
Digital Transformation through MPD Automation is revolutionising the oil and gas industry. Digital technologies provide access to higher-quality information about operations performance and allow companies to react quickly when unexpected events occur due to weather or equipment malfunctions/failures. Intelligent MPD (Managed Pressure Drilling) is an advanced form of Digital Transformation that applies sophisticated algorithms to optimise drilling operations. It takes full advantage of Big Data Analytics in order to maximise safety and performance during drilling operations by controlling pressure and flow rate, learning from machine twins, and providing greater insight into their operational performance.
Intelligent MPD's ability to monitor real-time variables makes it a safer alternative as it enables faster response times in various situations that arise during the drilling process without manual intervention from personnel onsite. Digital transformation with MPD automation also helps reduce costs associated with manual labour as it requires fewer people for operational tasks and allows for more efficient use of resources (Elvin, 2023 and Sanchez, 2022).
Other benefits of Digital Transformation through MPD Automation include improved accuracy in decision-making and greater transparency in operations processes. Advanced algorithms mean companies can make decisions based on reliable, up-to-date data rather than relying solely on human judgement, which can lead to mistakes or misjudgment, which can be costly. Additionally, Digital Transformation provides better visibility into the current state of the wellbore at all times, allowing for quicker response times when unforeseen circumstances occur. This drastically improves safety protocols and reduces potential risks for personnel onsite.
Gamification as a Tool to Boost Employees Engagement
Authors: Dubois A. , Kelamcherry B. V. 
Published: October 2023

Abstract
Gamification, the application of game elements and mechanics in nongame contexts, has emerged as a compelling approach to learning. It taps into our natural inclination for play and competition, and it is transforming learning into an enjoyable experience. Gamification is a game changer, as it boosts engagement, increases motivation, provides a meaningful experience, and accelerates the adoption of technology. This transformative approach revolutionizes training, making it more effective, interactive, and inspiring, while nurturing a lifelong love for learning and growth.
This paper provides an in-depth examination of the development of two distinct games and their practical integration into training deliveries. It explores the advantages of incorporating games into the learning process, considering both the student's perspective and the corporate viewpoint.
Application of Distance Based Generalized Sensitivity Analysis and Bayesian Inversion Method in Simulation of a Giant Fractured Carbonate Reservoir with Unstructured Grids
Authors: Fang Junling , Li Chen , Shi Wen , Yang Tao , Wang Hehua , AL Marzooqi Maitha , Al Ansi Rasha , Zhang Liang , Hu Xuezhi , Xu Jiafeng , Zhou Shuisheng , Xu Fengqiang 
Published: October 2023

Abstract
Multi-scale natural fractures bring challenges in geological modeling and flow simulation of carbonate reservoirs. History matching is extremely difficult due to significant heterogeneities and uncertainties, especially for those wells identified as "dominated by fractures". A novel, systematic approach is applied to model fractures explicitly, to perform flow simulation efficiently and eventually to match the production history accurately.
First, a discrete model using unstructured triangular grids were built to fully resolve the geometry and distribution of large-scale fractures. Then the contribution of small-scale fractures was modeled using flow-based upscaling algorithms to yield enhanced porosity and permeability of matrix grid cells. Finally, the connectivity list was calculated for each pair of matrix-matrix, matrix-fracture, and fracture-fracture cells for flow simulation. Then the Distance-based Generalized Sensitivity Analysis (DGSA) method is applied to evaluate the sensitivity of the uncertain parameters in the reservoir model. Conditioning with the well production history as "given" information, the Bayesian inversion method is employed to reduce the uncertainty of fracture properties including exact position, length, and permeability etc.
The entire workflow/approach was applied to a gigantic, naturally fractured reservoir with multi-billion-barrel oil reserves in Middle East. More than five hundred large-scale fractures are characterized in the simulation model explicitly using triangular prism grids. The resulted simulation model contains over 800,000 unstructured cells. It takes only one hour on a single CPU core to simulate the entire production history of over three decades for more than 100 production wells. The high simulation efficiency facilitates sensitivity analysis and history matching in which more than one thousand cases are simulated. In the meantime, due to the explicit representation of large-scale fractures, the rapid water breakthrough in some of the producers could be captured much more accurately than standard dual-porosity dual-permeability (DPDP) models. In the history matching process, the uncertainties of the sensitive parameters including most fracture and some matrix properties are systematically reduced following the Bayesian inversion method. The history-matched fracture network and matrix properties provides an accurate and efficient simulation model for future prediction and infill well optimization.
Time-Lapse FWI Using Borehole Seismic Data for CO2-EOR Monitoring in a Carbonate Field Onshore Abu Dhabi
Authors: Yamada Y. , Nakayama S. , Ishiyama T. , Mochizuki M. , Mouri T. , Shimizu H. , Aljaberi A. A. , Al Hamedi A. M. , Mohamed M. , Ahmed J. , Waqas M. 
Published: October 2023

Abstract
Time-lapse seismic plays a vital role in monitoring the dynamic behavior of reservoirs during carbon dioxide enhanced oil recovery (CO2-EOR). However, its application to carbonate fields onshore Abu Dhabi presents several challenges, necessitating appropriate datasets and analysis tools. For proper understanding of fluid behaviors in a carbonate field onshore Abu Dhabi, the time-lapse vertical seismic profiling (VSP) datasets were acquired with an optimum signal-to-noise ratio. Firstly, pre-processing was performed on baseline and monitoring surveys, resulting in improvement of data quality and repeatability in a quantitative manner. We then applied full waveform inversion (FWI) to estimate time-lapse velocity changes induced by CO2-EOR. After the parameter optimization, the inverted velocity showed updates in the correct direction while minimizing the data residual. To stabilize and optimize the time-lapse FWI application, three different approaches were performed: a parallel approach, a common model approach, and a target-oriented approach. As a result, the time-lapse velocity changes near the injection well were clearly recognizable in the reservoir of interest. This has suggested that time-lapse property changes associated with CO2-EOR operation can be captured through time-lapse FWI. Therefore, time-lapse borehole seismic imaging in conjunction with the applied FWI scheme can be an effective tool for precise monitoring of fluid behavior in a carbonate reservoir during CO2-EOR operations.