bluuebunny/crossref_metadata_2025_split huggingface dataset:
Thermo-Gas-Chemical Stimulation as a Revolutionary Ior-Eor Method by the in-Situ Generation of Hot Nitrogen and Acid
Authors: Rezaei Koochi Mojtaba , Mehrabi-Kalajahi Seyedsaeed , Varfolomeev Mikhail Alekseevich 
Published: September 2021

Abstract
As known, fracture's capacity and penetration are two key factors for fulfillment of the fracturing jobs including conventional and acid fracturing process. Penetration of acid into existing fractures can improve fracture capacity by etching of fracture surface. Increasing temperature of reservoir rock results in reduction of breakdown pressure. Thermo-gas-chemical technology by in-situ releasing of extra hot gases (N2 and steam) and acid provides a series of network with long fractures and permanent conductivity. A series of experiments in high-pressure and high-temperature (HPHT) reactor were designed to understand the performance and effectiveness of thermo-gas-chemical reaction and determine the optimum binary composition in order to release maximum temperature, pressure and acid generation to provide long conductive fractures. In parallel, dependence of breakdown pressure and temperature was modeled. Moreover, to understand the geometry and propagation of fractures, the effect of thermo-gas-chemical method was studied on core samples in core holder and then cores were scanned by 4D tomography. The preliminary results showed that during the thermo-gas-chemical reaction temperature in reaction zone reaches 207 ℃ and pressure 893 psi due to reaction products. It was found that reaction initiates just after the injection of activator and temperature and pressure increased instantly. This phenomenon acts as a strong impact to break formation rock. The pH of aqueous solutions during the reaction decreased from 8 to 1 and below which provides etching the surface of existing and new fractures. Observed that thermobaric parameters of reaction closely depend on the concentration and reaction activator. Experimental results show that the application of thermo-gas-chemical fluid instead of ordinary fracturing fluid, results in reduction of breakdown pressure from 3400 psi to 121 psi, due to induced thermobaric shock. Experiments on core samples and 4D tomography confirmed the formation of new fractures and expansion of existing ones. Thermo-gas-chemical technology by generation of in-situ hot gases and acid can provide a new high efficiency, cost-effective and eco-friendly method of EOR method for tight low permeability reservoirs and even depleted reservoirs without increasing water cut.
Integration of Chemofacies and Rock Mechanical Properties Using Machine Learning Algorithms: Implications for Geomechanics and Hydraulic Fracture Stimulations in Paleozoic Formations, Saudi Arabia
Authors: Hussain Maaruf , Amao Abduljamiu , Al-Ramadan Khalid , Olatunji Sunday , Negara Ardiansyah 
Published: September 2021

Abstract
The knowledge of rock mechanical properties is critical to reducing drilling risk and maximizing well and reservoir productivity. Rock chemical composition, their spatial distribution, and porosity significantly influenced these properties. However, low porosity characterized unconventional reservoirs as such, geochemical properties considerably control their mechanical behavior. In this study, we used chemostratigraphy as a correlation tool to separate strata in highly homogenous formations where other traditional stratigraphic methods failed. In addition, we integrated the chemofacies output and reduced Young's modulus to outline predictable associations between facies and mechanical properties. Thus, providing better understanding of lithofacies-controlled changes in rock strength that are useful inputs for geomechanical models and completions stimulations.
Reassessment of Petroleum Engineering Education: Is It the End of an Era or a New Start?
Authors: Babadagli Tayfun 
Published: September 2021

Abstract
Since emerging more than a century ago, petroleum engineering (PE) education has increasingly kept its popularity despite significant downturns in the industry. During these downturn periods, observed at least four times since the 1973 oil crisis, structural changes in university programs have been considered. On the other hand, during the "heyday" periods, institutions have had to tackle enormous demand from industry, severely increased enrollments, and reestablish resources to provide a proper service. In light of these observations and while experiencing the fifth downturn period over the last five decades, it is time again to ask the same question: "Shall we continue with the same PE education model or radically shift to a new model?"
In this paper, after reviewing more than fifty articles published over the last 85 years reporting the attempts made towards reshaping PE education, an option of restructuring PE programs is discussed. This option is less oil industry (and oil prices) dependent and more of a "general" engineering education program with an emphasis on the "geoscience" or "subsurface" engineering aspects of the PE discipline. Detailed discussions focus on curriculum updates to address the industry practice of "subsurface" related engineering applications. Viability of this option was discussed from industry, academia, and students’ perspective.
This restructuring option requires substantial changes to curricula, skill development, and teaching and learning styles. Fundamentals are essential to include in PE education similar to other general (or major) engineering disciplines such as mechanical, civil, chemical, and electrical engineering. The essential elements of engineering skills such as creative design, decision making, problem description and solving, management under high degree of uncertainty, and data collection and processing for optimization are to be included in the new model. Finally, the model proposed is critically discussed and analyzed from different perspectives (industry, academia, and students) considering current and prospected subsurface engineering applications.
Pressure Transient Analysis of Wells in the Fault-Karst Carbonate Reservoirs with Vertical Beads-on-String Structure: Case Studies in Shunbei Oilfield, Tarim Basin of Northwestern China
Authors: Wei Cao , Cheng Shiqing , Wang Yang , Shang Ruyuan , Zhu Lang , Yu Haiyang 
Published: September 2021

Abstract
The newly discovered the fault-karst carbonate reservoirs in Tarim Basin are formed by large-scale tectonic fault activities and multiple-stage karstification. The ground outcrop, seismic reflection and well logging show that the large caves, vugs, tectonic fractures and matrix coexist in the reservoirs. The fractures interconnecting with caves in series to form vertical beads-on-string structure is the most common pattern. It is found that conventional models are difficult to match the recorded pressure data.
To fill this gap, this work summarizes three kinds of patterns for this structure and presents three novel models to estimate formation properties. The physical models of multi-fracture-region multi-cave-region series connection are established by simplifying vertical beads-on-string structure. The corresponding mathematical models are developed, in which the flow in fracture regions obey Darcy's law, while the flow in cave regions obeys free flow. Importantly, the gravity is considered due to the flow along vertical direction. Then typical flow regimes are analyzed and sensitivity analysis is conducted.
Our work shows that pressure-derivative curves show similar ‘concave’ characteristic for the cave storage regime and vug/matrix interporosity flow regime. The difference is that the pressure derivative for cave storage regime is unit slope, while this slope is not equal to unity for vug/matrix interporosity flow regime. Therefore, large cave and vug/matrix medium can be distinguished by slope of pressure derivative. More than that, the typical characteristic of vertical beads-on-string structure on type curve is that the cave storage regimes and linear flow regimes alternately appear. This characteristic helps the engineers identify vertical beads-on-string structure. A novel finding is that gravity effect could lead to unit-slope pressure and pressure derivative at late times, which is traditional recognized as the presence of a closed boundary when it may not be the case. Lastly, this methodology is applied to two cases from Shunbei Oilfield in which it is difficult to obtain good fitting quality and interpretation results using traditional methods. Besides conventional properties, the proposed methodology allows us to estimate other properties (e.g., cave height, cave radius), which are not readily obtained from conventional methods.
Integrated Field Management System for LNG Assets: Maximizing Asset Value Through Representative End-To-End Modeling
Authors: Khan Osama Hasan , Ali Samad , Elfeel Mohamed Ahmed , Biniwale Shripad , Dandekar Rashmin 
Published: September 2021

Abstract
Effective asset-level decision-making relies on a sound understanding of the complex sub-components of the hydrocarbon production system, their interactions, along with an overarching evaluation of the asset's economic performance under different operational strategies. This is especially true for the LNG upstream production system, from the reservoir to the LNG export facility, due to the complex constraints imposed by the gas processing and liquefaction plant. The evolution of the production characteristics over the asset lifetime poses a challenge to the continued and efficient operation of the LNG facility. To ensure a competitive landed LNG cost for the customer, the economics of the production system must be optimized, particularly the liquefaction costs which form the bulk of the operating expenditure of the LNG supply chain. Forecasting and optimizing the production of natural gas liquids helps improve the asset economics. The risks due to demand uncertainty must also be assessed when comparing development alternatives.
This paper describes the application of a comprehensive field management framework that can create an integrated virtual asset by coupling reservoir, wells, network, facilities, and economics models and provides an advisory system for efficient asset management. In continuation of previously published work (Khan, Ali, Elfeel, Biniwale, & Dandekar, 2020), this paper focuses on the integration of a steady-state process simulation model that provides high-fidelity thermo-physical property prediction to represent the gas treatment and LNG plant operation. This is accomplished through the Python-enabled extensibility and generic capability of the field management system. This is demonstrated on a complex LNG asset that is fed by sour gas of varying compositions from multiple reservoirs. An asset wide economics model is also incorporated in the integrated model to assess the economic performance and viability of competing strategies.
The impact of changes to the wells and production network system on LNG plant operation is analyzed along with the long-term evolution of the inlet stream specifications. The end-to-end integration enables component tracking throughout the flowing system over time which is useful for contractual and environmental compliance. Integrated economics captures costs at all levels and enables the comparison of development alternatives.
Flexible integration of the dedicated domain models reveals interactions that can be otherwise overlooked. The ability of the integrated field management system to allow the modeling of the sub-systems at the ‘right’ level of fidelity makes the solution versatile and adaptable. In addition, the integration of economics enables the maximization of total asset value by improving decision making.
Model Prediction of Critical Gas Rate with Water-Cut Reversal Effect
Authors: Ekeregbe Merit P. 
Published: September 2021

Abstract
Accurate prediction of gas critical rate is critical to the successful management of gas wells. This paper covers the prediction of gas critical rate and presents limitations of old models with gas condensate wells with water-cut reversal.
Comparison of prediction methods or models with this new method will be explained using field data of condensate wells. The effect and relation of water-cut with critical gas rate determination will be presented and the best method that universally meets changing conditions of the well will be tested with field data.
Any method that must be acceptable must meet the dynamics of the well. No static model can predict accurately a dynamic well and reservoir performance. The old models of critical gas rate prediction show a static outlook, probably see the beginning of the well-life and cannot predict correctly when the fluid phases change in gravity. The late life prediction of the well performance is much more critical than the early life when the well has sufficient energy. The production envelope is more critical at depletion than at when the reservoir energy just kick. Therefore, any model prediction must be dynamic. The results from the old models show that they fail the dynamic test of the well performance. This limitation makes those model unusable in a late life of the well when water cut increases.
This study has revealed a method or a model for critical rate prediction that is accurate throughout the life of the well. The effect of water cut reversal is well tracked with this new model whereas the static nature of other models predicts a wrong minimum rate at a liquid load up rate. The field data reveals that the dynamic nature of well and reservoir performance can only be understood dynamically.
Human Factors and Performance: Reducing Errors and Improving Safety
Authors: Khan Salman , Boyd Leah , Velez Ferdinand 
Published: September 2021

Abstract
While companies employ a variety of tactics to reduce workplace incidents, behavioral-based programs have proven highly effective—particularly because such programs ensure that safety becomes a collective responsibility shared by all employees. However, training managers and their employees on the fundamentals of behavior-based programs such as the Siemens Energy Human Performance (HuP) program has proven challenging during the global COVID-19 pandemic. Training that traditionally was done in person, where people could interact and discuss root causes of accidents in the same room, had to be replaced with virtual training sessions.
This paper reviews the key facets of the HuP program, which includes safety training, raising awareness of employees’ susceptibility to human error, and how to design management systems as well as to promote behaviors to prevent safety incidents. It also reviews common practices in the program—including Stop Work authority, Safety Walk & Talks, daily toolboxes, and rapid risk assessment—and how they are being consolidated into one virtual training curriculum.
People bring their own personal mix of skills, knowledge, experience, attitudes, motivation, habits, and personality to their jobs—and to each task that they routinely perform. The novelty of the HuP approach is that it empowers workers to recognize where errors occur, use the proper tools to change their habits, and then contribute equally to their own safety and operational excellence, rather than relying on written policies and discipline. The efforts focus on safety within and outside the company.
Monitoring Reservoirs from Subsea Wells: Ultra-Long Step out VSP Acquisition Using Enhanced Back-Scattering Fiber
Authors: Roux Pierre-Francois , Barry Alexander , Johnston William , Mead David , Baker Mark R. , Raggio John S. 
Published: September 2021

Abstract
While DAS VSP has become relatively standard in dry-tree applications, acquiring data in subsea wells has remained a technical challenge as umbilical can be tens of kilometers long, thereby reducing the overall quantity of backscattered light to the topside interrogator. This adds to the attenuation due to connectors at the wellhead and along the optical path. Yet, the need for subsea DAS interrogation is high, particularly with the onset of complex, deep-water projects that will require on-demand monitoring capabilities.
In this article, we report on the successful acquisition and subsequent processing of a zero-offset VSP in an ultra-long step-out context. We simulated a subsea well with 69km worth of lead-in fiber to the wellhead, including attenuation at the wellhead mimicking the connectors. The attenuation was tackled by using an active, subsea amplifier (that would normally sit at the wellhead), and an in-house developed engineered fiber that provides a significant uplift in backscattered energy. We acquired this ZVSP both on fiber and with a standard wireline tool string for comparison. The approach presented here combines hardware and processing strategies to tackle the long step-out challenge. We demonstrate the ability to record seismic data even at very large step-out, a requirement for subsea well monitoring.
Assessment of Water Saturation Using Dielectric Permittivity Measurements in Formations with Complex Pore Structure: Application to the Core- and Log- Scale Domains
Authors: Azizoglu Zulkuf , Heidari Zoya , Goncalves Leonardo , Abreu Blanes De Oliveira Lucas , Silva Do Nascimento Neto Moacyr 
Published: September 2021

Abstract
Broadband dielectric dispersion measurements are attractive options for assessment of water-filled pore volume, especially when quantifying salt concentration is challenging. However, conventional models for interpretation of dielectric measurements such as Complex Refractive Index Model (CRIM) and Maxwell Garnett (MG) model require oversimplifying assumptions about pore structure and distribution of constituting fluids/minerals. Therefore, dielectric-based estimates of water saturation are often not reliable in the presence of complex pore structure, rock composition, and rock fabric (i.e., spatial distribution of solid/fluid components). The objectives of this paper are (a) to propose a simple workflow for interpretation of dielectric permittivity measurements in log-scale domain, which takes the impacts of complex pore geometry and distribution of minerals into account, (b) to experimentally verify the reliability of the introduced workflow in the core-scale domain, and (c) to apply the introduced workflow for well-log-based assessment of water saturation.
The dielectric permittivity model includes tortuosity-dependent parameters to honor the complexity of the pore structure and rock fabric for interpretation of broadband dielectric dispersion measurements. We estimate tortuosity-dependent parameters for each rock type from dielectric permittivity measurements conducted on core samples. To verify the reliability of dielectric-based water saturation model, we conduct experimental measurements on core plugs taken from a carbonate formation with complex pore structures. We also introduce a workflow for applying the introduced model to dielectric dispersion well logs for depth-by-depth assessment of water saturation. The tortuosity-dependent parameters in log-scale domain can be estimated either via experimental core-scale calibration, well logs in fully water-saturated zones, or pore-scale evaluation in each rock type. The first approach is adopted in this paper.
We successfully applied the introduced model on core samples and well logs from a pre-salt formation in Santos Basin. In the core-scale domain, the estimated water saturation using the introduced model resulted in an average relative error of less than 11% (compared to gravimetric measurements). The introduced workflow improved water saturation estimates by 91% compared to CRIM. Results confirmed the reliability of the new dielectric model. In application to well logs, we observed significant improvements in water saturation estimates compared to cases where a conventional effective medium model (i.e., CRIM) was used. The documented results from both core-scale and well-log-scale applications of the introduced method emphasize on the importance of honoring pore structure in the interpretation of dielectric measurements.
Scale Buildup Detection and Characterization in Production Wells by Deep Learning Methods
Authors: Cheng Jingru , Mao Deming , Salamah Majid , Horne Roland 
Published: September 2021

Abstract
This study developed an analytical tool for the detection and characterization of scale buildup from well data using deep learning methods. The developed method allows for a sensitive detection of the initiation of scaling as well as an accurate prediction of the magnitude of existing scale buildup. Scale deposition causes tubing ID decreases and therefore results in production declines, so a sensitive approach to detect the scale deposition is valuable to reduce the damage and losses due to this problem.
The underlying deep learning methods are both single- and multioutput, deep neural networks that consist of a combination of convolutional, long short-term memory (LSTM) and fully connected layers. We trained the networks on more than 30 sets of well data, with the objective of predicting the presence and the magnitude of scale. We started with cases of full scale deposition over the whole wellbore depth, and then extended our study to partial depth scale deposition. We built up a point-wise neural network model combining two blocks, which each contain several fully-connected layers followed by an LSTM layer specifically focusing on relatively smaller or larger tubing ID changes, corresponding to more or less scale deposition. To characterize the segmented scale deposition, we transformed to a three-dimensional problem, which can be solved by extracting the tubing ID changes and the scale deposition segment length.
The multioutput model was able to predict the tubing ID and volume changes at the same time, using a combination of convolutional and LSTM layers with residual network blocks and updated using a loss function that we defined. Tubing ID changes were extracted accurately with metric R-square more than 90%, while the length of the scale deposit could be classified into two classes (high scaling or low scaling) with good accuracy.
Though existing physical and chemical methods can be used to analyze scale deposition, the methods are often applied after considerable production decrease has already occurred. By using deep learning algorithms, our study came up with a new way to predict the scaling problem in advance with high sensitivity.