bluuebunny/crossref_metadata_2025_split huggingface dataset:
Emission Source Detection and Leak Rate Estimation Using Point Measurements of Concentration
Authors: Roy Arjun , Nundy Sangeeta , Kim Okja , Chan Godine 
Published: February 2022

Abstract

Introduction
With the advent of global climate change, it has become incumbent on governments and industries to monitor and limit greenhouse gas emissions to prevent a catastrophic rise in the average global temperature. The Paris agreement [Paris 2015] aims to lower global greenhouse gas emissions by 40% (in comparison to greenhouse gas levels observed in 1990) by 2030. Methane is a greenhouse gas whose 100- year global warming potential is 25 times that of carbon dioxide [GWP] and whose atmospheric concentration has been increasing since 2007 [Nisbet 2016, Theo Stein, et al. 2021]. Thus, there is an increased requirement on industries from government regulators to detect, localize, quantify and mitigate both fugitive and vented emissions of methane.
There are several different technologies that are available for automated methane emissions management. These include arial and ground-based mobile sensing units that are based on optical-gas imaging, satellite-based imagery [Jacob et al. 2016] and stationary metal-oxide based sensors. A key criterion that often needs to be satisfied is continuous monitoring for early detection and mitigation of fugitive leaks. Fixed metal-oxide based sensors [Yuliarto et al. (2015), Zeng et al. (2019), Yunusa et al. (2014), Potyrailo et al. (2020), Wang et al. (2010) and Feng et al. (2019)] are low-cost sensors that can be used for continuous monitoring of a site and are typically used for detection of leaks and alerting. The main challenge is to extend utility of these sensors to not only detect presence of fugitive and vented emissions, but also be able to estimate the number of leak sources and their probable locations and the total volume of hydrocarbon leaked over a period.
This paper describes an approach used for detecting anomalies in emission data, identifying possible emission sources, and estimating emission leak rates using point measurements of concentration collected over a period along with measurements of wind speed and direction. This involves multiple analytics that combine concentration and wind-condition time-series data with physics models to predict the different outcomes.
Modeling Viscous Oil and Tar Mat Formation from Nanoscale to Macroscale
Authors: Mullins Oliver C. , Pan Shu , Wang Kang , Betancourt Soraya S. , Cañas Jesus A. , Kauerauf Armin 
Published: February 2022

Abstract
Viscous oil and tar mats often occur at and near the oil-water contact (OWC) and can result from multiple charges of incompatible fluids with regard to asphaltene stability. The most easily measured oilfield case is gas charge into oil, where the increase of solution gas near the gas-oil contact (GOC) causes local instability of asphaltenes which can then lead to viscous oil and tar mats at the OWC. However, the detailed mechanisms that occur in geologic time to transport destabilized asphaltenes over large distances from the GOC to the OWC has yet to be fully resolved. Asphaltene destabilization towards the top of the reservoir, transport and accumulation at the base of the reservoir can be treated within a conceptual multistep process: instability driven by diffusion of light ends into the oil at the GOC, Stokes falling and diffusion of asphaltene nanocolloidal particles to the base of the interval, convective transport to the base of the reservoir, and finally, local asphaltene equilibration at the base of the reservoir. This conceptual model lays the foundation and provides the framework for forward modeling the formation of viscous oil and tar mats at the OWC. Here we introduce a simple, one-dimension composite model that accounts for all key physics and chemistry aspects of asphaltene instability, transport outcomes and bulk phase transition. This model can be used to predict different reservoir realizations given specific charge fluids, timing of charge, and reservoir attributes. This model employs the asphaltene thermodynamic equation, the Flory-Huggins-Zuo equation of state, and its reliance on the asphaltene nanostructures in the Yen-Mullins model and is shown to be applicable from nanoscale to macroscale. In a broader context, these reservoir processes fall within the new technical discipline ‘reservoir fluid geodynamics’. The target applications for this modeling include identification of possible key reservoir performance drivers through generation of different possible reservoir realizations and as a job planner for data acquisition and analysis to differentiate among reservoir realizations for optimization of field development planning. This approach is a template for forward modeling a broad array of fluid and rock complexities through a comprehensive deposition, trap filling and geodynamics perspective.
Multiple Recovery and Re-Use of Commercial Kinetic Hydrate Inhibitors from Produced Water and Rich Glycol
Authors: Mozaffar Houra , Larsen Tore , Henderson Chris , Deshmukh Salim , Anderson Ross , Hoopanah Mohsen , Tohidi Bahman , Abadie Emilie , Richon Vanessa , Charlesworth Mark 
Published: February 2022

Abstract
Kinetic hydrate inhibitors (KHIs) offer an alternative to traditional thermodynamic hydrate inhibitors (THIs) for the prevention of gas hydrates. KHIs have several advantages over THIs, such as lower required volumes, easier logistics and reduced CAPEX. However, KHIs are once through chemicals leading to increased OPEX, are mostly non-biodegradable and therefore cannot be discharged to sea or disposal wells in fear of aquifer pollution. KHIs can also lead to fouling of process equipment, especially at elevated temperatures.
To resolve these issues, a new KHI polymer removal method using a solvent extraction-based technique has been developed. In this approach, an immiscible extraction fluid is mixed into the KHI containing aqueous phase where the KHI polymer partitions into the extraction fluid, which can then be separated from the aqueous phase. In some cases, the KHI separated this way can be re-used. This process has the potential to solve problems with KHI produced water treatment/disposal, including where KHI is used in combination with MEG, reducing the costs and process fouling and protecting the environment.
A new joint industry project (JIP) is underway with the aim of developing the concept into a commercial process for removal and possible re-use of KHIs upstream of PW treatment or MEG Regeneration systems. The first phase of this project is lab scale evaluation of the solvent extraction method for simulated removal and re-use of two commercial KHI formulations for a real gas-condensate field case. Both the removal efficiency and hydrate inhibition performance of 4 cycles of re-injected/re-used KHI has been successfully demonstrated. Removal of KHI from a real MEG system case was also successfully demonstrated. In the second phase of the JIP, lab scale tests were used to screen extraction and separation equipment and identify optimum process conditions. The upcoming third phase of this JIP is dedicated to demonstrating the selected process concept(s) on pilot scale in a flow loop.
In this proceeding we will give highlights of the early laboratory test results from a produced water case where two field qualified KHIs are removed from PW and reused 4 times, still showing adequate hydrate inhibition performance. Successful pilot tests will confirm the operability of this process in the field.
Revamping Polymer Architecture for Optimized Fracturing Fluids in Fresh and Produced Water
Authors: Popeney Chris , Harkness Kellen , Copeland Laura , Lee Jesse , Usoltsev Dmitry 
Published: February 2022

Abstract
The presence of extensive tight oil reserves in regions with scarce or intermittent supply of ground or surface freshwater resources underscores the importance of water reduction, reuse, and recycling strategies to ensure the sustainability of hydraulic fracturing. Currently, the advantages of using synthetic high viscosity friction reducers (HVFRs) does not extend to fluids composed of high salinity produced water or wastewater because these products cannot transport proppant effectively under such conditions. The use of a new fully synthetic polymer architecture bearing interchain association is described which provides a significant increase in brine tolerance and fluid rheology, giving rise to effective proppant transport and friction reduction in water salinity exceeding 200,000 TDS.
Two variations of the polymer architecture are described: a product that operates effectively in fluid below 100,000 TDS such as seawater (SW-HVFR) and a second high brine product that works in all fluids up to and above 200,000 TDS (HB-HVFR). Proppant transport was studied dynamically using a slot flow apparatus, which demonstrated performance with the new system that exceeded guar and greatly exceeded other conventional HVFRs at equivalent polymer loadings. Slot flow results indicated consistent transport performance throughout the salinity range investigated by proper selection of SW-HVFR and HB-HVFR.
Although high shear viscosity of the new polymers was inferior to that of guar, advanced rheological studiesindicated that the superior performance was due to enhanced viscosity and unusually high elasticity of the derived fluids within the relevant shear rate range between 1 and 100 s−1. In addition, anomalous dependence of viscosity on temperature is described, featuring a viscosity maximum above ambient temperature. This unusual rheology behavior was attributed to the associative polymer architecture of the new system.
The new HVFRs exhibit effective friction reduction within their intended salinity ranges as well as good tolerance toward biocides and clay control agents. Furthermore, the operational salinity range of SW-HVFR can be extended up to 200,000 TDS in the presence of certain production enhancement aids due to a synergistic effect on the polymer dissolution rate. Lastly, bottle testing indicated that the polymers are effectively broken by common oxidative breakers, enabling their flowback. These results demonstrate the flexibility of the new HVFR system to make total fluids utilizing any water source, enabling sustainable fracturing in a variety of situations.
Estimating Carbon Storage Capacity of Depleted Shales with Flow Regime Diagnosis and Transient Analysis
Authors: Chu Hongyang , Ma Tianbi , Gao Yubao , Zhu Weiyao 
Published: February 2022

Abstract
Recently, many attempts have been made to estimate carbon storage capacity of depleted shales. In this paper, based on flow regime diagnosis and transient analysis, a quick and reasonable semi-analytical method for estimating CO2 storage capacity of depleted shales is introduced. Our semi-analytical method has the capability to estimate carbon storage capacity of depleted shales by considering effective diffusion, gas adsorption, and stress-sensitivity phenomenon of the permeability. By combining Laplace transforms, Pedrosa's substitution, and Stehfest numerical inversion, a CO2 seepage model for an injection well with constant injection rate is solved. With these solutions, CO2 storage capacity can be easily estimated at an arbitrary injection rate. We verify the semi-analytical method against the numerical method. In addition, the influence of some critical parameters on CO2 storage capacity are studied. Accurate determination of maximal carbon storage capacity in depleted shales is significant. This study presents a new semianalytical approach to efficiently estimate the maximal carbon storage capacity. Our study provides an improved understanding of calculating the maximal carbon storage capacity in depleted shales.
Maximizing Oil Recovery with Variation in Ambient Temperature
Authors: Gajbhiye Rahul , AL-Dogail Ala , Al-Shammari Hulail , Alnaser Mustafa , Alshawaf Amar , Kamerkar Tushar 
Published: February 2022

Abstract
In the oil and gas industries, the gas-oil separation plant (GOSP) is operated at fixed operating conditions without considering the effect of ambient temperature (Ta) variations. Temperature is one of the parameters that can affect the GOSP process and its output. Ignoring the variation in the ambient temperature may lead to a loss in oil recovery and corresponding revenue. The separation process is highly affected by the variation of ambient temperature, where the ambient temperature varies greatly from summer to winter. As the plant is operated at a fixed operating condition that is not optimized results in low recovery of the GOSP output. The optimization process of high-pressure separator (HPS) and low-pressure separator (LPS) is required to compensate for the variation in ambient temperature, which leads to maximizing oil recovery and plant revenue.
Thus, this study aims to develop an intelligent optimization system to improve the oil production of the Gas-Oil-Separation-Plant (GOSP) considering the variation in ambient temperature. Accordingly, the objective of this work is to provide an intelligent system for the determination of the best-operating set-points of the pressure (optimum pressure) for the high-pressure separator (HPS), low-pressure separator (LPS) at which maximum hydrocarbon liquid recovery can be obtained through the GOSP plant at a given ambient temperature.
To achieve the objective of this study, a GOSP model was built by Petro-SIM process simulator software using a typical Saudi Aramco GOSP design. The data from the initial PVT analysis is considered as an input for the process simulator. Then the optimizer tools built in the Petro-SIM are activated to determine the optimum condition of HPS and LPS in an integrated fashion accounting for variation in ambient temperature for maximizing the liquid recovery.
The results showed increases in ambient temperature result in a decrease in the oil recovery of the GOSP plant. The oil recovery appears to increase with an increase in HPS pressure reaching the maximum and then decreasing with a further increase in the HPS pressure. Similarly, the LPS pressure affects the oil recovery when LPS pressure increases oil recovery increases reaching the optimum point and then decreases with a further increase in LPS pressure. An intelligent system can be built based on an optimization process for determining the optimum condition of LPS and HPS for compensating variation in ambient temperature.
Analytical and Numerical Assessment of Hydraulic Properties of 3D Partially-Open Rock Fractures
Authors: He Xupeng , AlSinan Marwa , Kwak Hyung , Hoteit Hussein 
Published: February 2022

Abstract
Modeling fractured reservoirs requires an accurate assessment of the hydraulic properties of discrete fractures. High-resolution Navier-Stokes simulations provide an accurate description of the flow within the fractures, including fracture upscaling. However, its excessive computational requirements make it impractical. The traditionally used cubic law for fracture upscaling is known for its poor accuracy. This work introduces a new analytical model based on cubic law to estimate hydraulic properties of 3D rock fractures with the irregular contact-area distribution.
We first briefly review the applicability of the existing models in the literature. Most of the current models exhibit major limitations related to prediction accuracy and practicality. For instance, most models neglect the effect of contact-area distribution. As a result, they fail to capture properly the heterogeneous and anisotropic flow behaviors caused by irregular contact-area distribution. In addition, some models work well only for idealized cases, such as parallel-smooth fractures with specific shapes of contact areas. Others are only valid for cases with fractional contact area and relative roughness less than certain values. Alternatively, we propose a dynamic indicator using a particle-percolation approach to quantify the effect of the contact-area distribution. We then introduce a more general modified cubic law, which accounts for different factors, including variable aperture, roughness, flow tortuosity, and contact-area distribution.
The proposed model predictions are then compared with the solutions of the 3D high-resolution Navier-Stokes simulations and experimental measurements collected from public resources. We assess the predictability of our model and benchmark it with other existing models from the literature with dozens of fracture cases. We show significant superiority of our model compared with other existing models, especially for cases with pronounced fracture anisotropy where the impact of the fracture contact area is dominant. Moreover, results show that the hydraulic aperture decreases with the increase of frictional contact area almost linearly. The contact-area distribution shows a significant impact on hydraulic properties even with the same size of contact regions.
We introduce a new analytical model to estimate the hydraulic properties of 3D partially-open rock fractures. Based on our benchmark that included dozens of fractures, our model is superior to commonly used models in the literature. The proposed model also retains the simplicity and efficiency of the cubic law, and can be easily implemented in workflows for reservoir characterization and modeling.
Identification of Objects in Oilfield Infrastructure Using Engineering Diagram and Machine Learning Methods
Authors: Ismail Muhammad Hami Asmai , Tailakov Dmitry 
Published: February 2022

Extended Abstract
A processing plant consists of huge number of equipment, which working together to achieve their designed function and target output. Each of the equipment (assets) need to be managed properly to ensure maximum uptime of the plant operation and avoid any unnecessary downtime. To properly manage plant assets, each of the equipment must be properly recorded in a Computerized Maintenance Management System (CMMS) for management and tracking. Normally, each equipment is referenced by their unique identifier called Tag, created during engineering design phase. Currently, in the industry the standard way of collecting equipment Tag is very manual, involving the visual screening of each page of as-built P&IDs and other drawings. Each identified Tags are then recorded in a database in a hierarchical manner (parent-child structure) and each of them will be assigned with appropriate classification according to the owner's standard. This current process is time consuming and labour intensive while AI could be implemented to solve this problem in the future.
Thus, this study was initiated with the objective to implement computer vision and deep learning architectures to identify objects that appears on as-built engineering piping and instrumentation diagram (PID). The algorithm then tasked to classify objects detected according to ISO 14224 Equipment Classification, match it to the corrent Tag that appears in the same drawing and finally create parent-child structure between the objects identified. The long-term aim of this project to prove a hypothesis: a good pre-trained model of P&ID object detection can be achieved.
Using and Computing Metal Temperature for Asme Piping Class Optimization
Authors: Bangert Patrick , Smith Peter , Helgesen Sondre , Eckenfels Benjamin 
Published: February 2022

Abstract
The choice of piping classes for industrial use has important ramification on engineering projects ranging from safety considerations to cost-material effiency and environmental footprint. Usually, suitable piping classes are found by consulting fixed tables, such as the ASME code for piping classes, together with other values like required pressure resistance and pipe temperatures, the later often being a decisive factor for choosing one piping class over another. For the pipe and flange temperatures today mostly the fluid temperature is used, while sometimes rough approximations based on fluid temperature percentages are used, or more rarely, slow, and expensive finite element calculations are performed for individual cases. In this paper we demonstrate the significant benefits of choosing correct piping classes using precisely calculated pipe temperatures and propose an AI-based approach for fast and precise calculation of these pipe temperatures based on a training set derived from a limited number of finite element calculations performed by the authors. We also analyze in detail the benefits of this approach and provide concrete examples.
Higher-Order Derivatives of Production Rate and Convolutional Neural Network for Production Forecasts
Authors: Haider Syed Tabish , Patzek Tadeusz W. 
Published: February 2022

Abstract
In recent years, many machine-learning models have been developed to predict future production of oil in gas in "shales". Long-short term memory (LSTM), the most widely used model, relies on the long-term production history for a reasonably accurate production forecast. All analytical and machine learning models, including LSTM, fail miserably in the absence of long production history. Our goal is to present a novel method of production forecasting using only 24 months of production data. The first and secondorder derivatives of the distance traveled give speed and acceleration to describe the trajectory and dynamics of a moving vehicle. Similarly, higher-order derivatives of hydrocarbon/water production rate vs. time uncover hidden patterns and fluctuations in a well that act as differential markers of its future recovery factor (RF). In this paper, we couple production data and their higher-order derivatives with other known parameters for a well, i.e., well length and initial production. The time-series data are passed into a Convolutional Neural Network (CNN) with two hidden layers of 16 nodes each, and one output layer. The model is trained to predict recovery factor (RF) in the 10th year of production. We analyze the first 24 months of production data for the Barnett (1500), Marcellus (800), Haynesville (800), and Eagle Ford (1000) shale wells. All wells have a minimum pressure interference time of 34 months. The production rate vs. time and its first, second, and third-order derivatives are coupled with the well length and initial production rate, and the data are normalized with their respective maxima. For the Barnett wells, the CNN model predicts recovery factors in their 10th year of production with an average accuracy of 90%. For the Marcellus, Haynesville, and Eagle Ford wells, the prediction accuracy in the 8th year of production is 89%, 92%, and 91%, respectively. Further, we divide the wells into three groups (A, B, C) depending on the range of their recovery factor (A:RF=0-0.3, B:RF=0.3-0.6, and C:RF=0.6-0.9). We show that the clusters of wells grouped by their RFs strongly correlate with the distribution of the higher-order de rivatives of production from these wells. Thus, we posit that the detailed production history and its derivatives are the most important variables that define distributions of maximum recoverable hydrocarbon from a source rock. Our novel method uses only 24 months of production data to predict future recovery factor with an outstanding average accuracy of 90%. We show that the higher-order derivatives of high-resolution production data available from the operators could be an excellent tool for well screening and predicting future production with reasonable accuracy.