bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing Frac Fluid Recovery in Unconventional Shale and Tight Gas Fracturing with Eco-Friendly Flowback Aids
Authors: Essam Ahmed , Abdelgawad Khaled , Ibrahim Ahmed 
Published: February 2024

Abstract
Hydraulic fracturing, a method used to stimulate oil and gas from shale and tight formations, faces challenges like fluid blockage and fast declining oil or gas production rates due to the significant impact of capillary forces on water retention. To address this, surfactants and microemulsions are commonly employed as flowback additives to reduce surface and interfacial tension, enhancing water recovery during hydraulic fracturing. The effectiveness of a flowback additives is highly impacted by the harsh subsurface conditions, such as high salinity and high temperature. This study aims to assess the performance and effectiveness of methylimidazolium chloride based ionic liquids as flow back additives at high salinities and temperatures and compare it to commercially used flowback additives.
Different experimental analysis was conducted to screen different concentrations of methylimidazolium chloride based ionic liquids (X1,X2,X3). Surface tension measurements were conducted to estimate the critical micelles concentrations (CMC). Spinning drop tensiometer was utilized to measure interfacial tension (IFT), in addition to contact angle measurements were used to evaluate the impact of the flowback additive on the wettability and the displacing behavior. Seawater was used as the base fracturing fluid with a salinity of 57,000 ppm and temperatures up to 90°c for all experiments.
Different formulations were used and yielded surface tension values varied from 30 to 45 mN/m with CMC values ranging between 0.5 to 1 gpt which is similar to commercial flowback additives. The different formulations showed different IFT values with the lowest IFT value of 3 mN/m in the case of X3. In addition, contact angle measurements showed changing the rock surface to be more water-wet after treating the rock with the flowback additives.
According to our results, methylimidazolium chloride-based ionic liquids can be used as environmentally friendly flowback additives especially in harsh conditions of high temperature and high salinity where the conventional additives are not suitable. This will contribute to the development of more sustainable and environmentally conscious practices in hydraulic fracturing operations. Ultimately, the utilization of these additives can help minimize water blockage, and formation damage impacts, and enhance shale gas production.
Automated Plug-And-Perf Decreases Operational Risk and Improves Conveyance Efficiency
Authors: Walden J. , Mishra A. , Ostermann N. , Padilla L. 
Published: February 2024

Abstract
Across upstream oil & gas operations, automation continues to be an increasingly important way to reduce risk and improve efficiencies, even for complex tasks historically requiring close human attention. Traditional manual processes are prone to higher inherent risks and inefficiencies, leading to unplanned non-productive time and cost overruns. However, the integration of advanced technologies such as robotics, closed-loop systems, and digital twins has paved the way for automated systems that are revolutionizing the industry.
Within unconventional completions, wireline conveyed perforating or "plug and perf" jobs are a critical part of the hydraulic fracturing operation. A typical plug-and-perf job requires 6 or more individuals to convey the perforating gun string via wireline winch and pump down units over the course of a multi-well completion. This operation requires focus from all personnel involved since variable downhole conditions can cause costly unplanned events such as pre-set plugs or pumped-off tool strings downhole. These events result in unplanned well interventions such as coil tubing fishing jobs, which can be extremely costly. Natural inconsistencies introduced from different personnel conducting the operation also leads to variation in job efficiency.
Halliburton has developed a novel system that takes real time data from the downhole tool string, the wireline winch, and the pumping units on location to feed a closed-loop system to convey perforating gun strings safely and efficiently within a wellbore. The system enables the winch and pumps to respond in real-time to variable downhole conditions and also predicts tool motion ahead of the string and continuously updates a forward-looking model using a digital twin. This method reduces the risk of unplanned events like pre-set plugs and pumped-off tool strings. Use of automated plug-and-perf has demonstrated up to a 12% faster run time while using less fluid than the same operation run manually.
This paper highlights the benefits of automating the complex and manual task of pump-down perforating, including the reduced risk of unplanned well interventions, improved operations efficiency, and fluids consumption optimization. It has been demonstrated that all of these lead to decreased costs for both service company and operator, based on deployments in both the Permian and Denver-Julesburg Basin of North America. The use of automated perforating operations leads to a safer and more productive oil and gas industry, where human expertise is augmented by technology to achieve better outcomes.
A Four-Dimensional Geostress Evolution Model for Shale Gas Based on Embedded Discrete Fracture Model and Finite Volume Method
Authors: Wang Qiang , Wang Yufeng , Zhao Jinzhou , Hu Yongquan , Lin Chen , Li Xiaowei 
Published: February 2024

AbstractStress changes associated with reservoir depletion are often observed in the field. The four-dimensional stress evolution within and surrounding drainage areas can greatly affect completion of infill wells and refracturing. To accurately predict the four- dimensional stress distribution of shale gas reservoir, a coupled fluid- flow/geomechanics model considering the microscopic seepage mechanism of shale gas and the distribution of complex natural fractures (NFs) is derived based on the Biot's theory, the embedded discrete fracture model (DEFM) and finite volume method (FVM). Based on this model, the four-dimensional stress prediction can be realized considering the mechanism of adsorption, desorption, diffusion and slippage of shale gas and the random distribution of NFs. The results show that in the process of four- dimensional stress evolution, there will be extremes of σxx, σyy, σxy, Δσ, α and stress reversal area at some time, and the time of occurrence of extremes is different at different positions. The key to determine this law is the pore pressure gradient with spatio-temporal evolution effect. Different microscopic seepage mechanisms have great influence on the storage and transmission of shale gas, which leads to great differences in the distribution of reservoir pressure and four-dimensional stress. The influence of microscopic seepage mechanism should be considered in the process of four- dimensional stress prediction. The larger the initial stress difference is, the more difficult the stress reversal is. When the initial stress difference exceeds a certain limit value, the stress reversal phenomenon will not occur in the reservoir. This research is of great significance for understanding the four-dimensional stress evolution law of shale gas reservoir, guiding completion of infill wells and refracturing design.
A New Model to Predict the Dielectric Properties by Employing Artificial Neural Networks
Authors: Hassan A. M. , Oshaish A. A. , Al-Ofi S. M. , El-Husseiny A. A. , Mahmoud M. A. 
Published: February 2024

Abstract
Studying the dielectric properties of reservoir systems can provide crucial information such as water saturation, total porosity, and brine salinity. Different models have been developed to estimate the dielectric parameters such as the conductivity and relative permittivity. However, multiple assumptions are used, and considerable computational processes are employed. This work aims to develop a new model to predict dielectric properties utilizing artificial neural networks (ANN).
In this study, the relative permittivity (dielectric constant) and conductivity for shale rocks were predicted based on the raw measured data. Multi-frequency dielectric measurements were carried out using a coaxial dielectric probe. The model input is mainly the reflection caused by the samples at different frequencies. The relative permittivity and conductivity of the samples were predicted using the ANN model at a frequency range of 1MHz to 3GHz, in order to capture multiple polarization mechanisms. Different types of error indexes were determined to indicate the prediction performance. Also, a comprehensive analysis was conducted to optimize the performance of the developed ANN model.
The results showed that the ANN-based model can effectively predict the dielectric parameters. A correlation coefficient of more than 0.94 was obtained. The optimized ANN model consists of one hidden layer with 7 neurons and uses Levenberg-Marquardt (trainlm) function during the training phase. Thereafter, a new equation was extracted from the developed ANN model to allow fast and accurate estimations for the dielectric properties. The developed equation can be applied to a wide range to estimate the relative permittivity and conductivity of shale rocks.
For the first time, an AI-based model is developed to estimate the dielectric properties of shale rocks. The proposed model can predict the dielectric constant and conductivity in less time and with high reliability.
Best Practices & Inhibitors Utilization to Prevent Sulfide Stress Cracking During Coiled Tubing Intervention in HPHT Sour Wells - A Case Study from Oman
Authors: Kumar Mayank , Abdullah Irfan , Pathak Himanshu 
Published: February 2024

Abstract
Coiled tubing (CT) strings are costly and long lead items which are prone to sulfide stress cracking (SSC) and stress corrosion cracking (SCC) when exposed to sour conditions. This paper provides the real-time observations where CT exposure to harsh sour well conditions led to the occurrence of premature failure in CT strings resulting from SSC. The paper will also describe how the application of improvised protection practices can lead to quantifiable enhancement of CT string life.
The paper will also explain the optimum grade selection for a CT string which remains the foundational factor influencing the life expectancy of the string and also outline best practices for CT string maintenance and management on and off jobs. Analyzing mass data of CT string utilization spreading over 200 plus well intervention CT runs shows the dominating control factor is the application of SSC inhibitors with unique scavengers to inhibit the corrosive environment the string is exposed to. Chemical protection in combination with appropriate fatigue derating was primarily evaluated in this study.
The results were exceptional having observed zero sulfide stress related failures resulting from exposure to hydrogen sulfide (H2S) concentrations as high as 70,000 ppm (7%) and high-pressure high-temperature (HPHT) conditions over a period of four years.
When it comes to the observation of coiled tubing life expectancy, the application of improved practices and procedures and the inference resulting from these practices, are time-bound and dynamic. The pre-job evaluation of the scope and wellbore conditions was a key turning point as it affected the job design. Specific jobs may have sour conditions, but with a lower partial pressure, the design of chemical protection can remain mild. Practices in this paper proved effective for a range of H2S and downhole conditions where H2S partial pressure was always above 0.05 psi. Concurrently, the study concluded that a scope that calls for prolonged CT string exposure to downhole conditions and prohibits access to pumping chemical, will not be the right candidate to implement the recommended practices in this study.
In conclusion, using effective scavengers and inhibitors along with implementing the recommended practices such as coiled tubing fatigue derating remained the basis for establishing the best protection practice for such unforgiving environments.
The study this paper presents dispenses an operating range for a coiled tubing to safely operate in specific high H2S environments. Implementation of synonymous design of chemical protection and coiled tubing derating routine establishes a moderate precedent for operating low carbon allow pipes in harsh sour environment while maximizing pipe life.
Innovative Logging Methodology and Design to Acquire Data in Challenging Multilateral Horizontal Wells
Authors: Aljuaydi H. S. , Mehmood S. X. , Aljassem H. M. , Miftakhov I. I. 
Published: February 2024

Abstract
Acquiring high quality downhole production contribution data for multilateral wells is essential to quantitatively evaluate reservoir performance. The complexity of multilateral completion design and unavailability of advanced logging tools act as innovation drivers to address existing challenges and enhance the current equipment menu in the market. This paper presents an upgraded version of both logging hardware and approach which ultimately resulted in significant improvements of lateral accessibility and data quality.
The upgraded logging tool utilizes the Total Flow Systems (TFS) which comprises of passive acoustic measurements and multiple active response sensors: temperature, and pulse neutron tools (PNL) in water flow log under different well conditions. The spectral acoustic recording system captures acoustic signals generated by fluid movement across a wide range of amplitudes and frequencies within an extended scanning radius. A numerical flow modeling was utilized to quantify inflows corresponding to temperature gradient changes within the active zones. The phase split is then integrated via simulation model given the inputs of water profile provided by PNL.
Implementation of the upgraded version showcases high-resolution production profile eliminating the need for mechanical spinners utilization. The generated numerical temperature modeling provided accurate interpretation to the effective production zones in the horizontal section. The analysis of the data collected from two laterals of well P-1 clearly demonstrates that the primary oil production comes from the motherbore, with the oil distribution profile being evenly spread along the entire open hole section. Temperature data from lateral indicates its subdued performance, characterized by a high water cut in the produced fluid. Quantitative analysis of flow profile for well P-2 suggests the top of the logging interval inside the motherbore was contributing around 80% of the oil inflow, whereas the lateral was dominated by uniform inflow. In addition, the majority of water inflow in the lateral was located across bottom depths. In both laterals, inflow zones detected by acoustic sensor during flowing condition had uniform signature. The absence of localized noise events confirms matrix flow without fracture flow contribution.
The new methodologies and upgraded hardware proved that it can be used as the new standard for production logging in challenging wellbores for production optimization and better reservoir management. The data analytics features improved the decision-making process and accurately represented the reservoir condition.
Predicting Interfacial Tension in CO2/Brine Systems: A Data-Driven Approach and Its Implications for Carbon Geostorage
Authors: Khan Mohammad Rasheed , Tariq Zeeshan , Ali Muhammad , Murtaza Mobeen 
Published: February 2024

AbstractCO2 Interfacial Tension (IFT) and the reservoir rock-fluid interfacial interactions are critical parameters for successful CO2 geological sequestration, where the success relies significantly on the rock-CO2-brine interactions. IFT behaviors during storage dictate the CO2/brine distribution at pore scale and the residual/structural trapping potentials of storage/caprocks. Experimental assessment of CO2-Brine IFT as a function of pressure, temperature, and readily available organic contaminations on rock surfaces is arduous because of high CO2 reactivity and embrittlement damages. Data-driven machine learning (ML) modeling of CO2-brine IFT are less strenuous and more precise. They can be conducted at geo-storage conditions that are complex and hazardous to attain in the laboratory. In this study, we have applied three different machine learning techniques, including Random Forest (RF), XGBoost (XGB), and Adaptive Gradient Boosting (AGB), to predict the interfacial tension of the CO2 in brine system. The performance of the ML models was assessed through various assessment tests, such as cross-plots, average absolute percentage error (AAPE), root mean square error (RMSE), and coefficient of determination (R2). The outcomes of the predictions indicated that the XGB outperformed the RF, and AdaBoost. The XGB yielded remarkably low error rates. With optimal settings, the output was predicted with 97% accuracy. The proposed methodology can minimize the experimental cost of measuring rheological parameters and serve as a quick assessment tool.
The Utilization of 3D Printing in O&G Applications
Authors: Tavares J. A. , Aldakhil D. A. 
Published: February 2024

3D Printing, also known as Additive Manufacturing (AM), is a manufacturing method that has revolutionized the production of engineered parts, by enabling companies from multiple different industrial sectors such as transportation and medical, and ultimately Oil & Gas to prototype or re-build and replace any part, regardless of geometrical complexity. Such disruption has been enabled by the unique characteristics of 3D Printing that allow for logistical flexibility and full independence when manufacturing parts to repair existing equipment and re-commission them for operation. This use-case is particularly valuable if the objective is to repair equipment considered obsolete, where finding replacement parts poses a challenge.
Detachment from the traditional supply chain is a unique strategic position for any company to explore in order to better predict and control its product output. More so in an industry such as Oil & Gas, where business and operation continuity are heavily prioritized, and any potential disruption can cause significant losses of revenue and fossil fuel supply. 3D Printing, due to the flexible nature of the technology, can play a considerable role in tackling challenges that compromise Oil & Gas production by minimizing downtime. While exploring the potential of 3D Printing, decreasing downtime is a consequence of localizing production of components, which in itself results in reduced lead times and cost compared to importing the same commodities from overseas markets.
Also, from a carbon emission perspective, 3D Printing is a less wasteful process than subtractive manufacturing (i.e. milling and machining) and facilitates the production of complex geometries. In addition, 3D Printing plays a role in eliminating physical storage of components as a support technology that enables digital platforms serving as databases of digital replicas for existing components to be ready for 3D Printing on demand. This type of database is typically designated as Digital Inventory. This means that in the future, companies may be able to keep lower stock of critical parts knowing they can be replicated as needed. Consequently, less energy will be consumed by negating the need for large stock-up activities, and with the relocation of manufacturing activities the shipping carbon footprint is reduced.
Automatic Recognition of Homogeneous and Heterogeneous Reservoirs Using Deep Learning Technique
Authors: Retnanto Albertus , Wahid Md Ferdous , Indico Romeo , Paderres Janessa , Malyah Mohamed , Moussa Mahmoud 
Published: February 2024

Abstract
Reservoir analysis and characterization primarily rely on well test data to indirectly infer crucial reservoir parameters, including wellbore storage, permeability, storativity ratio, and initial reservoir pressure, by examining pressure responses from downhole. While prior studies have showcased machine learning algorithms’ effectiveness in accurately classifying reservoir models with over 90% accuracy, these efforts often have been limited to fewer than ten distinct models. This study introduces a streamlined convolutional neural network (CNN)-based architecture for deep learning, extending its capabilities to classify twenty diverse reservoir models. Pressure transient signals were generated for these models, encompassing both homogeneous and dual porosity reservoirs, each exhibiting ten unique boundary types, including infinite acting, circular sealing, constant circular pressure, single sealing, single constant pressure, angular sealing, channel sealing, channel constant pressure, U-shaped sealing, and rectangular sealing. Utilizing 300 pressure signals for each model, we harnessed well-test analysis software to simulate these signals. To accommodate variations in real field data duration compared to simulated data, we employed an outer product transformation of pressure transient and pressure derivative data, converting it into a grayscale image with values ranging from 0 to 1 and resized to 30×40 dimensions. This transformation preserved crucial patterns, regardless of magnitude and duration. Optimizing the CNN's hyperparameters, including filter number, size, max-pooling, and stride, was achieved through Bayesian optimization, resulting in a five-level deep ResNet architecture. Our model evaluation adopted an 85-15 data split for training and testing. The network's performance was assessed using key metrics, yielding impressive results, with an overall accuracy of 93.3%, sensitivity of 93.4%, specificity of 99.7%, and an F1 score of 93.2%. Furthermore, the model demonstrated consistent accuracy above 90% for fifteen of the twenty reservoir models, even when tested with diverse magnitude and duration pressure signals. This study's innovative approach highlights the effectiveness of a simplified CNN architecture in accurately classifying a broad range of reservoir models, offering reduced training time without compromising accuracy. These findings lay the foundation for future research aimed at further advancing deep learning's potential in reservoir characterization, promising valuable contributions to the field.