bluuebunny/crossref_metadata_2025_split huggingface dataset:
How Mechanical Principles Affect Production from Deep and Complex Gas Reservoirs
Authors: Hamid Osman , Abdalrahman Eslam M. 
Published: November 2024

Abstract
This study aims to elucidate the mechanical principles governing gas production from deep and complex reservoirs. Focusing on geomechanics, contact mechanics, fluid flow and deformation, poromechanics, and damage mechanics, the research seeks to determine their collective impact on the efficiency and safety of gas extraction.
Employing the numerical modeling approaches, the study investigates subsurface stress conditions, grain-to-grain interactions, fluid-rock interplay, and fracture development. A coupled fluid-solid interaction model is developed to simulate pressure distribution and strain on the rock matrix. The methodology integrates advanced constitutive models to address the non-linear and time-dependent behaviors of reservoir rocks under stress.
The research identifies key factors influencing reservoir performance, including stress-induced permeability and porosity changes and fracture and fault reactivation. Simulations reveal that optimal gas production is contingent on maintaining structural integrity, which is influenced by effective stress adjustments and material damage thresholds.
The study's novelty lies in its integrated numerical modeling approach, which synthesizes various mechanical principles within a singular predictive model. This model advances the current understanding of deep reservoir behavior by incorporating non-linear rock mechanics and real-time reservoir conditions.
Concluding that a multidisciplinary mechanical approach is vital for sustainable gas extraction, the study provides a strategic framework for production optimization in deep and complex reservoirs. The resulting tool offers significant improvements in predicting and managing the challenges of gas production, fostering enhanced recovery and minimization of environmental risks.
Holistic Approach to Tackle Proppant Flowback & Coal Fines Challenges through In-House Engineered Chemical Formulation
Authors: Nain Pooja , Qamruzzaman Mohammed , Mhaske Ganesh , Nischal Rajiv 
Published: November 2024

ABSTRACT
This paper addresses the challenges associated with coal bed methane (CBM) well dewatering, particularly the issue of proppant flowback and production of coal fines. The primary objective is to mitigate the impairment in fracture permeability caused by fines migration and assess the suitability of a developed Proppant and Fines Agglomerating Formulation (PFAF).
The devised PFAF system coats proppant surfaces thus enhancing agglomeration & preventing flowback. It was synthesized after extensive evaluation of various chemicals. Agglomeration Tests, Microscopic imaging and SEM imaging was conducted to select the best performing PFAF formulation. Optimal concentration was determined through measuring maximum proppant free production rate from proppant pack. Effect of PFAF on permeability was evaluated on fracture conductivity setup. A visualization assembly was fabricated to visualize the migration of fines in proppant pack before and after treatment with PFAF. Compatibility of PFAF with conventional fracturing fluid was evaluated through rheological tests to employ PFAF during Fracturing.
Extensive laboratory studies indicate that the developed PFAF, a reaction product of an amine and ester exhibits promising results thereby reducing clean-up time and cost. The proppant treated with PFAF agglomerated and descended slowly as one piece of consolidated mass indicating reduced proppant flowback, as compared to untreated proppant which fell as individual sand grains. Microscopic study shows uniform adsorption and SEM imaging indicates non-glue like behaviour with developed formulation. The Maximum Proppant Free Production rate significantly increased post-treatment, from 350 cc/min to 900 cc/min, indicating proppant consolidation. No significant change was observed in the permeability of the treated proppant pack. Fines Migration visualization shows the retention of coal fines in the treated pack, affirming the efficacy of PFAF in mitigating coal fines. No adverse behavior was observed in compatibility study of PFAF with conventional guar based fracturing fluid. Near Zero magnitude zeta potential values were obtained with PFAF which corroborated with other test results.
This paper introduces a novel Proppant and Fines Agglomerating Formulation (PFAF) to address fines-related challenges in CBM wells. PFAF demonstrates unique consolidation properties without compromising the fracture conductivity, as demonstrated by laboratory studies. The paper contributes additive information to the existing literature by presenting a practical solution for mitigating fines migration and proppant flowback in CBM wells.
Improved Fault Definition and Seismic Images Through Full Azimuth Angle Domain Imaging in A Dense Single Receiver Broadband Acquired Datasets within a Low Dune Area
Authors: Vargas Jose Edgardo , Utami Y. P. , Ofowena F. O. , Ahmed J. 
Published: November 2024

Abstract
Full azimuth angle domain imaging (CRAM-LAD) was applied on a +1000 km2 single-source, single-receiver full azimuth seismic survey targeting a better fault definition, fracture characterization and reservoir management of very subtle low-relief structures, geological environments of carbonate reservoirs in the Middle East desert environment. The previous seismic processing in this area was based on conventional pre-stack time and depth Kirchhoff migrations methods. New results allow us to have a better characterization of faults and fractures. Using EarthStudy 360 full-azimuth anisotropic seismic imaging in Local Angle Domain (LAD) technology, we were able to better delimitate structural inhomogeneities in the target reservoir levels, including small fault detection and characterization. The use of angle gathers allows us to improve the shallow velocities leading to better statics models incorporated in the Depth Velocity Model along with a multi-azimuth tomography also in the angle domain to improve the velocities in the overburden. Post-migration (imaging) procedures separate reflections from diffraction on directional angle gathers. Post-stack seismic attributes using diffracted events can show geological features that normal imaging methods cannot. Main achievements include the production of Full Azimuth, angle-dependent reflection and directional (dip and azimuth) data components. From Reflection angle gathers with higher signal to noise ratio, we obtained seismic section for a reliable geological interpretation preferred by interpreters and angle stacks with much less stretching effects in comparison with Kirchhoff methods more suitable for seismic Inversion. AVA amplitudes and attributes are superior in comparison to those obtained in previous studies. The deepest reservoir exhibits a better continuity for reservoir still on development. Directional gathers allow us to split its specular component from which we generated a seismic volume with higher signal to noise ratio in the reservoir levels. The diffracted energy also extracted using designed filter in this project, allows to extract geological features like small faults and fractures that traditional imaging methods are not able to map accurately. The rich azimuth information and angle ready datasets, obtained from non-regularized pre-migrated gathers, clearly define faulty planes, channels and karst zones from top to bottom. CRAM LAD imaging is widely used in the middle east for more effective imaging and analysis in fracture carbonate reservoirs. Here we demonstrate that can be used to improve the image quality for desert environments, especially in full azimuth single receiver datasets. AVAZ inversion for the definition of small fractures based in horizontal anisotropy measurements is also a benefit of the application of this kind of imaging technology.
Enhancing the Efficiency of Refining Processes: A Desktop Solution for Predicting FCC Yield Profiles
Authors: Maity Pintu , Khande Ajay , Majumder Supriyo , Dasila Prabha , Thota Chiranjeevi 
Published: November 2024

Abstract
The current state of FCC technology has the potential to dictate the overall profitability of any complex oil refinery worldwide, where the catalyst plays a central role in maximizing fuel and olefin yields. Innovations in FCC catalysis have led to higher conversion rates, improved selectivity, and better tolerance to contaminants. Consequently, every FCC catalyst changeover presents an opportunity to enhance the refinery’s profit margin. However, this potential increment in margin necessitates thorough testing and selection of candidate optimum catalysts which is often a time-consuming process. While various computational models based on first principles, databases, linear programming, or artificial intelligence exist to expedite this process, they typically serve as intermediaries between laboratory experiments and commercial yield predictions.
To streamline this process, the R&D team at Bharat Petroleum Corporation Limited (BPCL) has developed a proprietary Yield Prediction (Y-P) model. This model is designed to eliminate the need for experimental catalyst testing by estimating potential yield, selectivity, and conversion based on FCC feed and catalyst characteristics. The Y-P model employs a mathematical kinetic model using the tenlump scheme, integrating feed and catalyst properties into the prediction algorithm. By leveraging multivariate regression, lumped reaction mechanisms, and artificial neural networks, the model utilizes feedstock properties such as con-carbon, density, distillation, sulfur/nitrogen content, and catalyst properties like surface area, unit cell size, and acidity to predict commercial plant yields.
The trial results of the Y-P model have shown strong alignment with conventional in-house catalyst evaluation methods, demonstrating its reliability and accuracy. This model not only facilitates the selection of FCC catalysts but also enables their reformulation to meet changing market demands. By bringing catalyst selection to the operator’s fingertips, the Y-P model significantly enhances the efficiency and profitability of refinery operations.
Digital Twins Revolutionizing Oil and Gas Industry Optimizing Drilling Operations with Physics Informed AI
Authors: Gandikota R. A. , Chennoufi N. , Hadad T. 
Published: November 2024

Abstract
Digital technologies have disrupted every industry in recent years, and the oil and gas industry are no exception. One of the most significant advancements is the utilization of digital rig technologies, which have revolutionized drilling processes, monitoring, and well management. By implementing physics-informed-AI based real-time Digital Twins operating at the edge, companies can maximize performance, efficiency, and cost reduction.
In this study a digital twin with physics informed AI is presented. An efficient physics-based time domain model coupled with Machine Learning algorithms, forms the core of this innovative approach. The method utilizes performance data from small set of offset wells to continually optimize and update operating conditions. Its objective is twofold: minimizing predicted shock and vibrations and reducing mechanical specific energy while maximizing drilling rates.
The real-time edge implementation has demonstrated significant improvements from well to well, including enhanced weight transfer to the bit, consistent top drive RPM performance, and reduced shock and vibrations. As a result, average drilling performance has improved by 40%, translating to expedited drilling operations and decreased overall drilling costs.
Evaluation of a Novel Emulsion Preparation Method for Reducing Water-Cut in Production
Authors: Othman A. , Aljawad M. S. , Al-Ramadan M. , Kamal M. S. , Al-Taq A. A. , Al-Mohsin A. 
Published: November 2024

Abstract
High water cut associated with oil wells can shorten their production life. Globally, water accounted for the majority of the total output from wells. This excess water must be disposed of or treated to a specific standard, depending on the treatment objectives. The industry has developed numerous techniques to decrease the water produced alongside oil production. This study introduces a novel method to pump polymer and crosslinker emulsified system to block water production.
The system is designed to gel upon contact with water but form an emulsion in the presence of crude oil. The research was conducted in multiple stages. The objective is to create a water-in-oil emulsion using an emulsifier, introduce the same emulsifier to the crude oil, and then inject the polymer and the crosslinker. The viscosity of the polymer, polymer with crude, polymer with water, crude with emulsifier, and water with emulsifier were recorded. One emulsifier was selected from three emulsifiers when tested with crude oil and water separately the better type was U-108 emulsifier. Then the diluted polymer and the crosslinker were added and the viscosity was measured. The concentrations used in the study and added in the same order are; 53-60 vol. % of water or crude oil, 5 vol. % emulsifier, 2.5 wt. % pH buffer powder, 30-35 vol.% polymer or copolymer, and 4 vol. % crosslinker. These systems were mixed at two different mixing rates to mimic the pumping condition.
The initial evaluation of the diluted polymer viscosity at the standard condition and 100 s-1 had a viscosity of 12 cP. The emulsifier raised the viscosity of the linear systems in both oil and water samples, the emulsified linear water has a viscosity of 231 cP at room temperature, compared to 253 cP for crude oil. After 20 hrs at 94 C temperature, the viscosity was 1600 cp for the crosslinked water system and 3300 cp for the emulsified crosslinked water system, the emulsifier raised the viscosity value triple as the non-emulsified system. However, at the same conditions both the crosslinked and emulsified crosslinked crude oil had viscosities less than 20 cp. This preparation method can be applied to the field as a novel pumping method; the pumped emulsifier is proposed to make emulsion when it goes into the oil zone, while no emulsion is formed in the water zone. The formation of the emulsion in the oil zone will prevent crosslinking mechanisms. This emulsion is followed by water which raises the viscosity in the oil zone and not in the water zone which helps in viscous fluid diversion. When the polymer-crosslinker system reaches the water zone the gel is formed as no emulsion formed in the formation. A core flooding experiment was used to prove the two ideas shown in this paper.
Two concepts to show in the paper are a) the same fluid pumped into the formation and gelates in the water zone but not in the oil zone, and b) the diverting mechanism based on the viscosity value.
Utilization of Low-Grade Waste Heat by Capitalizing ORC to Maximize Hydrocarbon Yield and Reduce CO2 Emissions in Upstream Crude Processing
Authors: Amminudin K A. , Kamandahalli S. 
Published: November 2024

Abstract
Efficiency enhancements and energy conservation in existing operational facilities have become increasingly essential undertakings in the pursuit of optimization and decarbonization. This paper aims to shed light on how to use low-grade waste heat to improve the process, increase stable condensate production, reduce power consumption, and reduce the CO2 footprint in the gas processing train of a crude oil production facility using the Organic Rankine Cycle (ORC) system.
In the Gas Oil Separation Plant (GOSP), the gases separated from high-pressure production traps and low-pressure production traps are compressed and cooled by fin-fan coolers. The compressor's discharge heat serves as a low-grade waste heat source. The proposed approach is to utilize this low-grade waste heat for the benefit of process improvement by employing the Organic Rankine Cycle (ORC) system instead of releasing this waste heat into the ambient through air-cooled fin-fan coolers. Currently, the compressor discharge gases are cooled down to 145°F by using air coolers; however, with the ORC, the compressor discharge gases will be cooled down to the process requirement of 80 to 90°F
The cooling of the compressor discharge gas to 80 to 90°F by using the ORC system will help in condensing the heavier hydrocarbons upfront, resulting in an increase in condensate production, removing the moisture from the gas by condensing before entering into TEG system and reducing the volume of the gases to be processed in the subsequent steps. The lower gas temperature for the LP and HP compressor suction will result in less heat from compression, hence lowering the power requirement of the compressors. Practically, we can eliminate after cooler operation on a compressor train and generate additional power from ORC. The refrigeration system's condensing load will also be reduced due to low temperatures. The power generated by ORC will be connected to a local electrical grid to operate auxiliary equipment, which will offset some of the energy power imported from the grid.
This study provides an extensive overview of gas train systems and how the ORC system can be effectively used. Traditionally, the ORC system has been utilized merely to generate power; however, this study focuses on the technological benefits of improving the process, optimizing power utilization, increasing production, and minimizing CO2 emissions in an operating facility. This study aims to illustrate the advantages and potential improvements that can be achieved by applying the ORC system. The opportunities for implementing the technology are enormous, as it has the potential to deliver annual value creations in increased product recovery and electricity savings.
Predicting HPAM Polymer Yield Viscosity using Neural Networks
Authors: Maillon Rémi , Dupuis Guillaume 
Published: November 2024

Abstract
This work follows paper SPE- 216592-MS presented in October 2023 and gives an update of the machine learning model developed by SNF to predict HPAM polymer yield viscosity under various conditions. Major improvements to the model are as follows:
The random forest algorithm has been replaced by a neural network to predict EOR polymer yield viscosity. The rheological dataset has been extended to include more than 95,000 lab measurements compared to 69,000 previously. The rheological dataset has been filtered using the DBSCAN clustering algorithm to detect anomalies in the polymer flow curves and 1,030 erroneous lab measurements could be removed from the training dataset. A more limited selection of input features has been used for the prediction with only 7 variables compared to 8 previously. Only the polymer ATBS content was retained and deemed sufficient to characterize the polymer chemistry due to strong correlations between acrylamide and ATBS contents. A new web interface was developed to deploy the model to the end user.
These modifications helped improving the model's prediction accuracy while limiting the overfitting tendency. The mean absolute percentage error for the yield viscosity prediction dropped from 35.8 % down to 16.0%.
A second objective of the machine learning model was to assist lab technicians with polymer screening. The model was hence adapted to select suitable polymers based on the target viscosity and reservoir conditions (reservoir permeability, temperature, brine salinity and hardness) while ranking those selected polymers to minimize the required polymer concentration and cost.
In addition to those efforts, another app was developed to visualize the different polymer flow curves present in the rheological dataset while comparing them with fitted regression lines based on the Carreau – Yasuda equation. Parameters such as relaxation time λ or power law index n have been derived by fitting the Carreau – Yasuda model on 4,680 polymer flow curves.
Predicting System Surface Parameters Using Artificial Neural Network
Authors: Eltrissi Mohammad , Yousef Omar 
Published: November 2024

Abstract
The prediction of apparent surface torque and the system standpipe pressure holds immense importance in any automated system or digital twin solution. These parameters provide crucial insights that are instrumental in determining various factors in the digitalized drilling application workspace. However, obtaining these values prior to the operation poses a challenge due to their dependence on numerous lithological and operational parameters. Due to the problem of non-linearity, a statistical tool is favored in developing a prediction system for these features. Artificial neural networks (ANN), a statistical tool in machine learning (ML), can effectively predict the system standpipe pressure and the apparent surface torque.
A logical base data cleaning process is conducted to ensure consciousness cleaning of the dataset based on statistical feature exploration, feature engineering, and domain knowledge. A large dataset of 336 wells from a single operator across four concessions is used to train the ANN. This large dataset overcomes the problem of overfitting within the designed ANN, while extended training epochs avoid the underfitting problem. An extensive trial and error alternatives selection process was used to select the ANN optimum topography. The Nesterov-accelerated adaptive moment estimation algorithm is the optimization algorithm used to improve the ANN solution's training efficiency and convergence speed.
The developed ANN achieved 93.09% and 92.62% accuracy for the apparent surface torque and the standpipe pressure feature, respectively, in the non-biased testing of the result. The work investigating the low-order topography for the ANN shows poor accuracy against the high and more sophisticated topography of the ANN. One of the ANN's behaviors realized is that enhancing the prediction accuracy for one feature results in a deterioration in the prediction accuracy of the other.
Several attempts were made to create an automated drilling system; however, these attempts focused on the larger picture of the model and ignored the vital components that the calculated and predicted calculations are based on. System standpipe pressure and apparent surface torque prediction provide a solid foundation for an integrated system. The system's development used non-stochastic gradient decent tools to achieve the global minimum of the solution, contrary to most developed models' approaches to that topic. The high prediction accuracy of the developed ANN using the large dataset for training is a differentiator for this model.