bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling of Low Salinity Polymer (LSP) Interactions in Carbonates from Geochemical and Surface Chemistry Perspectives
Authors: Hassan Anas M. , Adila Ahmed S. , Al-Shalabi Emad W. , Tellez Arellano Aaron G. , Kamal Muhammad S. , Patil Shirish 
Published: February 2024

Abstract
Low Salinity Polymer (LSP) injection is a hybrid synergistic enhanced oil recovery (EOR) technique that improves displacement and sweep efficiencies by combining the advantages of both low salinity and polymer flooding methods. Nevertheless, proper design of LSP flooding at field-scale requires a predictive mechanistic model that captures polymer-brine-rock (PBR) interactions. Therefore, this study investigates the impact of water chemistry on polymer behavior in porous media in order to gain a better understanding of the PBR-system. In particular, we examine the effect of salinity and hardness on polymer viscosity and adsorption in dolomite formations during LSP flooding employing our in-house coupled MRST-IPhreeqc simulator. Furthermore, to capture the geochemistry of the LSP process, the MRST-IPhreeqc simulator incorporates surface complexation reactions as well as aqueous, mineral dissolution and/or precipitation reactions.
The findings of this study suggest that the 5-times spiked salinity and hardness scenarios are more favorable than those of 10-times spiked salinity and hardness, which were supported by their respective polymer viscosity losses of 75% and 82% for salinity spiking, and 58% and 63% for hardness spiking. Also, the effects of 10-times spiked Ca2+, 10-times spiked Mg2+, and 2-times spiked SO42-on polymer viscosity were studied with estimated viscosity losses of 61%, 61%, and 46%, respectively. The latter signifies the importance of sulfate spiking for reducing polymer viscosity loss while avoiding exceeding sulfate limit for scale formation and reservoir souring. For the effect of salinity on polymer adsorption, it was observed that the increase in salinity from the base case scenario (623 ppm) to 5- and 10-times spiked salinity, results in an increase in the dynamic polymer adsorption from 53 μg/g-rock to 59 and 68 μg/g-rock, respectively. Additionally, comparing the 10-times spiked Mg2+, 10-times spiked Ca2+, and the 2-times spiked SO42- scenarios, the 10-times spiked Mg2+ case resulted in the maximum polymer adsorption (87 μg/g-rock). This is due to the surface complexation reactions of magnesium surface species at dolomite rock surface with polymer molecules forming Mg-polymer surface complexes. In contrast, the calcium and sulfate do not form surface complexes through reactions with the polymer. This indicated that the divalent cation's design might impact the viscosity of the LSP solution, and therefore, it is crucial to carefully consider it when optimizing the LSP process in carbonates. Thus, proper design of LSP flooding at field-scale requires a predictive mechanistic model that captures PBR interactions which is covered in this work.
Improving Well Accessibility and Productivity with Multi-Lateral Tool Technology
Authors: Mashhad H. S. 
Published: February 2024

Abstract
Multi-lateral wells are a widespread choice for reservoir engineers to maximize reservoir contact. However, these complex wells pose challenges for rig-less well interventions, especially during acid stimulation. Multi-lateral tool technology can be used to improve well accessibility and optimize well productivity. This paper presents a comparison of the performance of several multi-lateral tools in dual-lateral, tri-lateral and quad-lateral Oil Producer and Water Injector wells.
Variety of tools, including hydraulically activated tools, electrically rotated tools, as well as hybrid hydraulic and electrical tools. The latest technological advancement in the Multi-lateral Wellbore access industry which is the integration of an azimuth determination unit to the multi-lateral tool assembly is compared with older technology in terms of performance and efficiency. The execution challenges, lessons learned, comparison between multi-lateral tool types, including the average time taken to access each lateral and the success rates are analyzed in detail and presented.
Deployment results showed that the multi-lateral tool technology significantly improved well accessibility during acid stimulation. When combined with advanced fiber optics coiled tubing technology, the success ratio was observed to increase significantly. It was found that the electrically activated and electrically rotated multilateral tool equipped with azimuth determination was by far the most effective way to steer coiled tubing into wellbore laterals. The lessons learned from this comprehensive analysis could be utilized to optimize future applications of multi-lateral tool technology. For example, the data collected from the fiber optics coiled tubing could be used to develop new tools that are even more effective at accessing and stimulating multi-lateral wells.
Overall, Multi-Lateral tool technology is a promising solution for the challenges of rig-less well interventions in multi-lateral wells. The technology can improve well accessibility, optimize well productivity, and provide a safe and efficient alternative to bull-heading.
Study on the Influence of DFN on Hydraulic Fracture Propagation for Horizontal Wells in Unconventional Resource - A Case Study from China
Authors: Wang Hui. , Zhou Wei. , Wu Rangbin. , Shang Yamin. , Li Zheng. , Li Guohao. , Huangfu Xuerui. , Zhao Mengjie. , Liu Xiaolong. , Wu Yutong. , Lian Liming. , Huang Xingning. 
Published: February 2024

Abstract
Fractured reservoirs are distributed with natural fractures of various shapes and sizes, which are the main reservoirs. Due to the uneven distribution of natural fractures, the current main method of fracture reforming is to communicate the natural fractures through hydraulic fractures and to establish a flow channel between the fractures and the wellbore. Clarifying the expansion law of hydraulic fractures in fractured reservoirs is conducive to enhancing the effect of reservoir modification. In this paper, a numerical model of hydraulic fracture extension in a reservoir based on a discontinuous discrete fracture model is established. Firstly, a reservoir fluid-solid coupling stress model is established, and then the discrete fracture model is used to construct the hydraulic fracture, which allows the hydraulic fracture to expand along the initially delineated mesh, and the minimum strain energy density criterion is used to determine the expansion path. According to the different distribution patterns of the seam holes, three reservoir types with fracture characteristics are established in this paper. The simulation results of reservoirs with different natural fracture characteristics show that natural fractures perturb the local stress field and deflect the extension path of hydraulic fractures. The more developed the natural fractures are, the more pronounced the perturbation effect is. According to the different relative positions of hydraulic fractures and natural fractures, they can be divided into frontal repulsion and lateral attraction, which are not conducive to the communication between hydraulic fractures and natural; increasing the net pressure inside the hydraulic fractures can enhance the dominant role of the hydraulic fractures when they intersect with natural fractures, and reduce the degree of deflection, which is conducive to the breakthrough of the natural fractures’ repulsion, and communicate with natural fractures in the direction of the dominant stress. The pressure at the injection point when the hydraulic fracture penetrates the natural fracture is mainly controlled by the flow energy loss in the hydraulic fracture and the leakage rate of the natural fracture. The optimization of the fracturing fluid performance can reduce the penetration pressure and enhance the extension range of the hydraulic fracture. The results of this paper provide references for the fracturing modification evaluation of fractured reservoirs.
Case Study of Drag Reducing Agent Application in Produced Water Reuse: Produced Water Throughput Improvement, Total Cost Reduction, ESG Enhancement
Authors: Moringo Nicole , Malekahmadi Fatee , Li Yifan , Price Brian , Muhammad Moin , Young Moosaa 
Published: February 2024

Abstract

Objective/Scope
In response to environmental, social, and governance (ESG) regulations, the industry has shifted to using produced water for hydraulic fracturing. However, the present infrastructure limits the volume of produced water that can be transferred. As infrastructure is not easily replaced, a water-based drag reducing agent (DRA) can chemically alter pipe friction. Formulating a DRA with shear stability and chemical compatibility in produced water remains a significant obstacle and requires thoughtful and specific chemistry.

Methods, Procedures, Process
A comprehensive water analysis of produced water is carried out, which includes examining the ion composition, total dissolved solids (TDS), total suspended solids (TSS), total organic carbon (TOC), and other chemical residues. Flocculation tests and flow loop experiments are performed to assess the chosen DRA's compatibility and drag reduction performance in the produced water. Finally, the text presents a successful field trial that involves treating and transferring over 2 million barrels of produced water in the Permian Basin, USA.

Results, Observations, Conclusions
The field trial demonstrated that the application of DRA significantly improved the water transfer rate for produced water. This improvement allowed for the use of produced water with the existing infrastructure and eliminated the need for fresh water on the job, which was not achieved previously. The use of DRA also led to a reduction in fuel consumption and equipment wear and tear. In addition, the field trial demonstrated that using DRA on the frac pad had extended benefits, as DRAs applied upstream remained fully soluble throughout the job. The properties of the DRA were not altered during water transfer, enabling the use of the same DRA on the pad. The trial results indicated that DRA could reduce the time required to reach the design rate and decrease wireline run time, ultimately resulting in time, chemical, and cost savings.

Novel/Additive Information
The specific chemistry resulting in DRA compatibility with produced water can reduce the friction reducer needed for the hydraulic fracture design, which makes the addition of a DRA cost-neutral while yielding numerous benefits aligning with the increasing ESG metrics in the oil and gas industry.
Role of Fluid Injection in Induced Seismicity
Authors: Conde Oliver Rojas , Misra Siddharth 
Published: February 2024

Abstract
Correlations between the fluid injection via water disposal (WD) wells and hydraulically fractured (HF) wells into subsurface earth and the subsequent increase in seismic activity is well documented. Our research presents a method to go beyond statistical correlations and quantify the causal relationship between subsurface fluid injection and induced seismic events, accounting for the confounding factors. The dataset employed for this analysis covers a 7-year period within the state of Oklahoma. The dataset comprises details such as the locations and key operational metrics of the water disposal and hydraulically fractured wells. Additionally, the dataset contains the location and magnitude of earthquakes, as well as the location and length of major fault lines. For this study, earthquakes (which can be induced seismicity) have a magnitude higher than 2. Our dataset contained 22,368 earthquakes ranging from magnitude 2 to 5.8 with a mean of 2.47 and mode of 2.2. Utilizing double machine learning (DML), we estimate the average treatment effect (ATE) to precisely quantify the causality. ATE results were derived from diverse spatiotemporal sample combinations, pinpointing the areal extent and temporal duration of heightened causality. This enabled us to explore the relationships between seismic activity and WD/HF wells in terms of the areal and temporal extents of the effects of the fluid injection into the subsurface through WD or HF wells. The ATE results reveal that 2 active water-disposal wells over 56 days cause 1 earthquake within a 4,400 sq. km area. Hydraulically fractured wells were found to have a more localized causal impact, such that 3 hydraulically fractured wells over 106 days lead to 1 earthquake within a 200 sq. km area. No detectable causal effect of fluid injection on earthquakes magnitude was identified.
Physics-Informed Machine Learning Application to Complex Compositional Model in a Giant Field
Authors: Bascialla Guido , Rat Coriolan , Sheth Soham , Dias Daniel , Heidari Mohammad Reza 
Published: February 2024

Abstract
Compositional reservoir simulation is a time bound activity demanding complex physics. We review the advantages of machine learning in complex compositional reservoir simulations to determine fluid properties, such as critical temperature and saturation pressure. A machine learning approach to predict critical temperatures during simulation based on the Heidemann-Khalil method is implemented, resulting in more accurate results with lower computational cost, outperforming the standard method and improving performance on a giant field model with compositional gradient and miscible gas injection.
The fluid column grades from black oil to gas condensate; accurate phase behavior and miscibility modelling involves a significant number of components. This makes simulation performance one of the biggest challenges. Critical temperature of the mixture is commonly used to determine phase state (phase labeling), a crucial process of reservoir simulation. Mislabeling can result in incorrect physics and convergence issues, particularly in cells with gas-oil displacement. Simulators generally use the Li correlation to calculate the pseudo-critical temperature from the weighted average of component critical temperatures, leading to inaccuracies. The Heidemann-Khalil method is computationally costly, proportional to the cube of the number of components, prohibiting its use for complex compositional simulation models. We use machine learning to efficiently incorporate it into simulation.
By using the machine learning approach, neural networks are trained based on a combination of feeds to reproduce the Heidemann-Khalil method with great precision. The accuracy of critical temperature and other fluid property determinations is thus improved, with machine learning ensuring a very low computational cost. Convergence is also improved. With the traditional Li method, especially at the beginning of gas injection, we faced numerical difficulties, and the runtime was slowed down. By implementing the machine learning based method, the convergence is smooth through the entire gas injection cycle, leading to a reduction of total iteration counts. We experience an overall four times speed-up of the simulation model, which greatly enhances the usage of this model in simulation studies.
The use of machine learning methods to replace physics in the simulator is an evolving area. By showing a field example of a successful application that improves both accuracy and performance, we contribute to fostering research into new possibilities where physics-informed models will enhance simulation studies.
Unleashing the Potential of Rate Transient Analysis: Transforming the Exploitation of Mature and Tight Fields, 40-Years Mature Field Case Study
Authors: Ali Amr Gharieb , Gabry Mohamed A. , Algarhy Ahmed , Ibrahim Ahmed Farid 
Published: February 2024

AbstractThis paper aims to investigate the challenges faced in the development of mature and tight fields, primarily resulting from reservoir depletion, high operational costs and reserves volumetric calculations uncertainty. The objective is to address the power of old, cheap data integration with the rate transient analysis technique. Additionally, the absence of sophisticated reservoir modeling data further complicates the situation due to the wells’ development many years ago. However, the paper intends to explore the potential of diverse sets of production data over an extended period, available for interpretation and integration with production operations data, to revolutionize the exploitation of mature and tight fields.The paper investigates the effectiveness of Rate Transient Analysis (RTA) in mature fields. This study introduces an innovative workflow that leverages existing data sources, such as Net Liquid above Pump (NLAP) and SRP SAM panel data, which was previously overlooked, to demonstrate the remarkable advancements made possible through RTA, particularly in the late-life development of mature fields and leverages the possibility to revive the old field again and convincing the management to re-invest in such fields. This research presents compelling outcomes achieved by applying the proposed workflow to old tight reservoirs.The results reveal a successful correlation between initially calculated static volumetric reserves and the lately dynamically calculated reserves, affirming the accuracy of the new RTA/Production data integration workflow in evaluating reservoir potential. Moreover, the study identifies previously unrecognized drilling locations and optimal fluid withdrawal spots, facilitating precise exploitation of these tight reservoirs. Additionally, the research uncovers the optimal spacing necessary to maximize permeability and drainage area in these formations, unlocking their full potential for enhanced recovery.The findings presented in this study significantly contribute to a deeper understanding and optimization of mature field operations by harnessing the power of RTA in conjunction with specialized operations such as hydraulic fracturing, operators can design optimal stimulation plans to further enhance reservoir performance. Additionally, integrating RTA results with simulation studies helps reduce uncertainty in history matching and forecasting, enabling more accurate production plans.
Enhancing Safety in Geological Carbon Sequestration: Supervised Machine Learning for Early Detection and Mitigation of CO2 Leakage in Injection Wells
Authors: Harati Saeed , Gomari Sina Rezaei , Rahman Mohammad Azizur , Hassan Rashid , Hassan Ibrahim , Sleiti Ahmad K. , Hamilton Matthew 
Published: February 2024

Abstract
The efficient and safe operation of CO2 injection wells during geological sequestration is crucial for successful carbon capture and storage (CCS) projects. This study explores the application of machine learning in creating a data-driven model for simultaneous prediction of the location and size of potential leak incidents along an active CO2 injection well based on wellhead and bottom-hole pressure and temperature data. Five different well-established machine learning algorithms were selected for predictive model development, including Support Vector Regression (SVR), K-Nearest Neighbor Regression (KNNR), Decision Tree Regression (DTR), Random Forest Regression (RFR), and Artificial Neural Network (ANN). A series of numerical simulations were performed to create a dataset based on a CO2 injection well model in a southern North Sea saline aquifer reservoir, accounting for various leak scenarios with different locations and sizes. The dataset includes three input features of wellhead pressure, bottom-hole pressure, and bottom-hole temperature, paired with two output variables of leak location and leak size. The research findings demonstrate that all models perform well in effectively pinpointing leak locations, but they face difficulties when it comes to detecting small leaks, particularly those with a CO2 leakage rate below 0.01 kg/s. The results obtained indicated that, with regard to model performance, the SVR and KNNR models tended to outperform the others during the testing phase. More precisely, the SVR model demonstrated exceptional performance in the context of leak localization, particularly when dealing with smaller datasets. Conversely, KNNR consistently showcased superior performance in the detection of leak size, regardless of the dataset size. The outcomes of this research can provide valuable insights into the behavior of leaky CO2 injection wells during geological sequestration and highlight the efficacy of supervised machine learning in detecting and predicting leakage in CO2 injection wells.
Relationships Between Rock Acoustic Dispersion and Petrophysical Properties for Enhanced Formation Evaluation
Authors: Ali S. , Ma S. , Al-Ofi S. 
Published: February 2024

AbstractUnderstanding the dissipation of acoustic energy in a fluid-saturated rock formation can divulge some important clues about their petrophysical properties. Rock properties such as bulk density, porosity, permeability, fluid type and viscosity affect the acoustic propagation velocity. The objective of the present work is to study the effects of the afore mentioned petrophysical properties on the acoustic dispersion phenomenon at a variety of practical application frequency scales.Experimentation was performed on sandstone rocks to measure petrophysical properties such as bulk density, porosity, permeability and acoustic velocities of compressional waves sweeping frequency, ranging from downhole logging (~20 KHz) to laboratory ultrasonic measurements (1 MHz). Sandstone samples, with different range of porosity and permeability were selected in order to study their effect on acoustic dispersion. Dispersion analysis for each rock type was performed and related to the petrophysical properties.Acoustic velocity measurement method at variable frequencies is described in this study. Laboratory measurements of p-wave velocity at variable frequencies ranging from 60 KHz to 1 MHz were performed on sandstone rocks of different porosity and permeability. Biot-Gassmann and squirt-flow models were applied to describe the p-wave velocity dispersion behavior. The dry measurements showed insignificant change in p-wave velocity with respect to frequency which reaffirms the claims of earlier studies of frequency independence of acoustic velocity at dry condition. The squirt-flow model predicted a transition from low to high-frequency regime at or above logging frequencies. These theoretical models can be used to derive acoustic velocities at any required frequency ranging from seismic to logging and ultrasonic frequencies. Squirt flow model can be used to derive rock permeability given the characteristic squirt-flow length is known and model parameter Z derived from laboratory measurements. Samples with higher porosity exhibit more dispersion compared to lower porosity samples can give a qualitative measure of the porosity. Further research and development in this area may lead to improved methods for characterizing sandstone reservoirs and optimizing hydrocarbon exploration and production.The novelty of the present workflow lies in utilizing and extracting additional potential of acoustic data to estimate petrophysical properties from multi frequency modern acoustic logs.