bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction Method and Application of Engineering Sweet Spot for Horizontal Wells
Authors: Li Yunzi , Yu Chenlin , Liu Tianbo , Huan Yaqi , Luo Cheng , Yao Dandan 
Published: February 2025

Abstract
Horizontal well staged fracturing is an important method for the development of unconventional reservoirs. It has been proved by field practice that the effect of fracturing is not only related to reservoir oil and gas content, but also related to the fracability of the reservoir, that is, the cluster perforation in"Engineering Sweet Spot " (ESP) area with strong fracability can ensure the uniform opening of each cluster and maximize fracturing effect. Traditional engineering sweet spot evaluation methods involve logging interpretation and core experiment，which have disadvantages such as high investment, long interpretation periods, discontinuous test data and in accurate interpretation results, resulting in the insufficient consideration of fracability. Based on this, the "Mechanical Specific Energy" method is innovatively introduced to identify ESP by using the mechanical specific energy value that represents the energy consumption of the drill bit to break a unit volume of rock, to establish a wellbore trajectory mechanical profile and reflects the rock mechanical properties of the in-situ formation and evaluate rock fracability. This technique is low-cost, highly operable, and can be used as a new method for pre-fracturing evaluation of horizontal wells, optimizing perforation positions and other related fracturing parameters. It has currently been tested in tight sandstone, shale and deep coal seam gas reservoirs in China, providing strong support for the exploration and quality improvement of fracturing technology for unconventional reservoir.
Integrating Economic Simulation Through Reverse Engineering: A CO2-EOR Case Study Analysis in Mature Oil Field
Authors: Utaminingdiah S Debby Halinda , Muraza Oki , Sundja Ali , Pranata Ngurah Aryadhita , Susanta Pande , Palupi Annisa 
Published: February 2025

Abstract
This paper presents an innovative approach to Carbon Dioxide (CO2) Enhanced Oil Recovery (EOR) strategies, combining an economical simulation methodology with a reverse engineering approach. The study delves into the intricacies of CO2EOR, leveraging reverse engineering principles as a methodological cornerstone for devising comprehensive economic simulation models. By integrating reverse engineering, this analysis aims to uncover underlying mechanisms, refining the precision of economic assessments and fostering sustainable CO2 EOR strategies.
This research endeavors to holistically integrate the economical simulation approach, reverse engineering methodologies, and considerations from CAPEX, OPEX, and utilization factors. This integration aims to offer a comprehensive understanding of CO2 EOR strategies, paving the way for optimized resource utilization, improved cost-efficiency, and sustainable technical and environmental practices within the realm of enhanced oil.
The analysis also underscores the significance of basis calculations derived from utilization factors. By integrating utilization factors into the economic simulation models, the study aims to refine the accuracy of cost-benefit analyses and investment projections. These calculations, based on rigorous reverse engineering principles, provide insights into resource allocation, efficiency improvements, and overall economic feasibility in CO2 EOR endeavors.
In conclusion, the integration of an economical simulation approach with reverse engineering methodologies, complemented by considerations from CAPEX, OPEX, and utilization factors, presents a holistic framework for refining CO2 EOR strategies. This approach aims to guide industry stakeholders in developing more informed and sustainable practices, aligning economic viability with environmental stewardship in CO2 utilization for enhanced oil recovery. The findings underscore the significance of a multifaceted approach, integrating diverse methodologies to address the complexities of CO2 EOR, thereby fostering a more sustainable energy landscape.
Breaking Barriers: Forging A Strategic Path for CCS Through Innovative Management of Other Contaminants Beyond CO2
Authors: Zamri W. Wan Mohd , Yasin M. Muhammad , Hashim W. Wan , Sani A. Md 
Published: February 2025

Abstract
Planning for CCS (Carbon Capture and Storage) development involves end-to-end evaluation from source to storage. When CCS is brought up as a topic, the conversation tends to center on managing CO2 (Carbon Dioxide). However, emission sources consist not only CO2, but also other contaminants and impurities such as H2S (Hydrogen Sulfide), SOx (Sulphur Oxide) and NOx (Nitrogen Oxide) and H2O (water). The origin of the emission determines the type of impurities present. For example. SOx and NOx only appears from post combustion sources but not from Acid Gas sources (recovered from the AGRU (Acid Gas Recovery Unit)), and H2S often present from Acid Gas source. These impurities are required to be manage in meeting the requirements of both subsurface (e.g. injectivity) and surface (e.g. technology limitation); in which will add to the process complexity, full cycle cost, HSSE (health, safety, security, environmental) risk, operational complexity, etc. Due to this fact, proper trade-off analysis needs to be conducted to generate the best end-to-end development scheme, whilst considering the suitable technologies to manage these impurities and their associated waste/by-product management. This development scheme should not only consider the type of emissions that it will initially handle but should also considers the future emission sources that plans to tie-in to it. Consideration should also be on the sequestration sites’ requirements since future sites may impose a different set of requirements (e.g. different acceptance of impurity concentration). It is then crucial to conduct project framing, concept identification, concept selection and storage site identification during the initial stage of the project. This paper shares a case study on the development of a CCS hub whilst discusses the approach taken to develop its end-to-end configuration and the consideration of available treatment technologies to manage its impurities thus meeting the transportation and sequestration requirements.
Digital Performance Model-Based Transformation Strategy Enables Significant Returns on Asset
Authors: Oestensen H. 
Published: February 2025

Abstract
With a digital performance model as the coordinating layer between functions, companies can implement a performance infrastructure as part of a digital transformation strategy that fundamentally changes the way industry works today. By incorporating digital twin technology, workflow engines, physics-based dynamic simulation models, hybrid machine learning and AI, the digital performance model introduces a novel approach to industrial work processes, with digital coordination at its core to upkeep and execute industrial services and associated AI/ML capabilities with increased autonomy.
The digital performance model drastically increases the transparency of the tether that binds the day-to-day operational activities to asset and executive level performance metrics. This is done by establishing a matrix style digital map, with work processes on the horizontal and ties from operational inputs and outputs to asset and executive level metrics on the vertical. This map emulates the accounting structure currently used for profit and loss (P&L) by the client, but with the added benefits of cross integration of digital systems across the enterprise, real-time monitoring, updates, aggregation and necessary analytics on these metrics. This mapping, along with the incorporation of decision support capabilities through both generative and analytical AI, helps client enterprises that focus on improved decision-making capabilities, yield return on asset (ROA) of 3-4% higher than their peers who do not take such an approach.
Complex Fluid Mixtures Characterization of Gas Condensate Reservoir with High CO2: An Improved Gas Flow Assurance Analysis
Authors: Hendraningrat L. 
Published: February 2025

Abstract
Near-critical reservoirs such as gas condensate exhibit complex phase behavior due to the existence of a two-fluid system, comprising reservoir gas and liquid condensate. It will be more challenging when the fluid composition has complex mixture components such as naphthenic complexes, aromatic complexes, and contaminants. This paper presents a characterization of complex fluid mixtures of the gas condensate reservoir at an offshore field with very high CO2 and other contaminants content.
Extensive PVT fluid experiments started from preliminary quality checks on the fluid samples, have been carried out. Complex fluid composition analyses were measured with 25 components including complex mixtures. The equilibrium gas composition was analyzed using the natural gas analyzer (NGA). Meanwhile, the equilibrium condensate was analyzed using high-temperature gas chromatography (HTGC), which gives detailed hydrocarbon compositions. Standard PVT experiments such as constant composition expansion (CCE), constant volume depletion (CVD), and fluid properties were measured and validated through the thermodynamic modeling equation of states (EOS).
The EOS has been utilized to study the near-critical phase behavior of complex mixtures, emphasizing the complexity and wide-ranging volatility of gas condensates and reservoir crudes. A thermodynamical model EOS was developed using PVT software and achieved an acceptable match against all PVT experiments, including dew point pressure. An an improved analysis on gas flow assurance can be done by conducting thermodynamic modeling of solid precipitation. The predicted solid precipitation where only observed at very extremely low temperature and pressure that unlikely to occur in operating condition of this field. In conclusion, proper characterization of complex fluid mixture near-critical gas condensate reservoir fluid will strengthen the quality, assist engineers in gaining a better phase behavior understanding of complex fluid mixtures, and prepare field development plans with better depleting strategies, reservoir management, and flow assurance mitigation.
This paper contributes to the broader understanding of phase behavior of gas condensate reservoir with high CO2 content and complex fluid mixtures and offers practical using thermodynamic modeling for improved understanding of fluid phase behavior and flow assurance issue. The results presented herein will establish a foundation for further research and development in this field, eventually underpinning the development of more efficient and sustainable extraction technologies.
Novel Solid-State Gyro While Drilling Technology Provides Highest Positional Accuracy in Horizontal Wells
Authors: De Oliveira V. Costa , Rivas M. , Osman A. M. , Moorkan S. 
Published: February 2025

Abstract
Wellbore positioning is critical for accurate well placement, reaching required targets and minimizing collision risks in congested fields. Standard measurement while drilling services suffer from external magnetic interference and magnetic field strengths that reduce wellbore positional accuracy. Conventional gyro while drilling services as well suffer from increased uncertainty at higher inclinations and with prolonged run time. These technology gaps demonstrated needed improvements in this area.
As the Industry targets deeper and more outreach reservoirs and requires to improve drilling efficiency and safety in congested fields, a new technology was developed to deliver high gyro quality surveying while drilling. The new technology allows the use of three solid state gyros inside a drilling tool to obtain definitive gyro wellbore positional measurement while drilling with highest accuracy in all inclinations from vertical to horizontal. The technology incorporates the latest sensor speed and stability features to deliver this service in drilling environment conditions in all hole sizes.
Five candidate wells were selected to evaluate the technology response compared to industry standard measurement while drilling tools and equivalent high-quality drop-down gyro measurements. The comparison criteria focused on comparing similar survey stations inclination and azimuth readings within defined limits and evaluated the ellipse of uncertainty projected reduction compared to the existing measurements while drilling technology. A total of 13,977 ft was drilled with the novel solid-state gyro while drilling technology during the evaluation. Data showed close adherence to comparable technology within 0.2 inclination variance and 0.5 azimuth variance. The ellipse of uncertainty showed the potential to reduce semi-major ellipse size up to 55% and the semi-minor ellipse size by up to 15%.
The implemented solid-state gyro while drilling technology showed high adherence to approved industry multi-shot gyro technology but while drilling. This technology will open new prospects for drilling challenging wells in collision congested fields efficiently, with high positional accuracy, and with elevated safety of wellbore interference.
Transforming Intermittent Gas Lift Operations with Automation and Real-Time Monitoring
Authors: Shresth K. , Bhatt B. , Tomar A. , Vadivel K. 
Published: February 2025

Abstract
Intermittent Gas Lift (IGL) is a vital artificial lift technique deployed in low-productivity wells with declining reservoir pressure, high water cuts, or limited gas availability. Despite its utility, IGL operations often suffer from inadequate monitoring and optimization due to the well's complex dynamics and low production potential. Conventional simulation-based approaches, while insightful, can be computationally expensive and impractical for routine use. This paper introduces an innovative, data-driven framework leveraging empirical modeling, numerical algorithms, and machine learning to enhance the monitoring and optimization of IGL operations.
This process leverages readily available operational data to estimate well rates using the API RP 11V10 methodologies, enabling robust monitoring of the injection and shut-in phases in intermittent gas lift (IGL) wells while optimizing coordination of injection schedules across neighboring wells. At its core, the solution integrates an empirical production model with adaptable parameters designed to forecast daily production rates for IGL wells. This model provides flexibility and accuracy by tailoring predictions to specific site conditions. Complementing this, a numerical optimization algorithm identifies the ideal shut-in and injection periods to maximize production rates. By carefully balancing well dynamics and operational constraints, this algorithm ensures efficient gas lift operations. Additionally, an advanced genetic algorithm enhances compressor stability by synchronizing injection schedules across adjacent wells, minimizing disturbances and operational overlaps. This sophisticated approach also supports binary-based scheduling, allowing for precise control and reducing the total number of simultaneous gas injections to streamline the overall process. These integrated components work seamlessly to optimize IGL operations, enhancing both production efficiency and resource utilization.
This approach transforms IGL management from a reactive to a proactive paradigm. Real-time data monitoring and algorithmic outputs enable precise troubleshooting, efficient scheduling, and optimized gas injection cycles. The solution significantly reduces reliance on traditional diagnostic methods, such as wireline fluid level tagging and cycle studies, whose frequency now is reduced. Instead, operators now benefit from continuous insights, reducing field visits, operational costs, and associated health, safety, and environmental (HSE) risks.
Field implementation has demonstrated measurable improvements in production efficiency, time savings, and reduced carbon emissions. By integrating operational data with advanced analytics, this solution addresses key challenges in IGL optimization, offering a scalable, cost-effective, and environmentally conscious approach for modern oil and gas operations.
A Novel Method to Predict Threshold Sand Rates Detected by Acoustic Sand Detectors for Multiphase Flow Conditions
Authors: Nadeem Asad , Daneshvar Mina , Wang Qiuchen , Shirazi Siamack A. 
Published: February 2025

Abstract
Acoustic sand detectors (ASDs) are widely used in oil and gas production to detect and estimate the presence of sand in multiphase flow. ASDs capture noise generated by flow dynamics and sand impact on pipe walls. For developing the model described in this paper, significant experimental data has been gathered including data used from previous investigators. Experiments were conducted in a 76.2 mm ID horizontal pipe with sand sizes ranging from 75 microns to 300 microns. The threshold sand rate (TSR), the lowest sand rate at which ASDs fail to distinguish flow or background noise from sand impact noise, was calculated for each experiment. The influence of key parameters on TSR, such as particle size, liquid velocity, and flow regime, was analyzed and compared with existing data. A machine learning (ML) model has been developed to predict TSR using a substantial dataset collected from various large-scale experimental setups with pipe sizes ranging from 50.8 mm and 101.6 mm. The dataset includes sand sizes ranging from 25 microns to 600 microns across diverse multiphase flow regimes, including annular, slug, churn, dispersed bubble, bubble flow, gas-sand, and liquid-sand conditions, with superficial gas velocities from 0 m/s to 49 m/s and superficial liquid velocities from 0 m/s to 6.6 m/s. This novel approach predicts TSR using only one parameter: the background noise generated by flow in the absence of sand, recorded by ASDs. A previously developed model for predicting pipe elbow erosion was adapted to determine the characteristic particle impact velocity, enabling the calculation of a calibration factor for specific production conditions. A machine learning (ML) Random Forest regression algorithm was trained and tested on 257 data points, allowing the prediction of a calibration factor for new production conditions. The ML algorithms trained on experimental data were validated using real-world field conditions documented in the literature. Results demonstrated excellent training effectiveness and prediction accuracy across varying flow conditions and pipe sizes. Compared to the traditional C-factor method, ML techniques yielded more accurate TSR predictions. This study provides operators with a practical tool to predict ASD's performance under specific production conditions using only background noise measurements. These findings enable timely decisions for sand control, enhancing operational efficiency and safety.
Economic Evaluation of Carbon Sequestration and CO2 Flooding in Carbonate Reservoir
Authors: Tang Kang , Wang Xiaochen , Jia Junhong , Shangguan Yangnan , Wang Jinghua , Wang Lili , Yuan Guowei 
Published: February 2025

Abstract
Carbon capture, utilization and sequestration (CCUS) is one of the most promising and effective technologies to reduce carbon emissions, with significant potential for development in the coming decades. The CCUS coupling project utilizing CO2 Enhanced Oil Recovery (CO2-EOR) can pave the way to economically viable CO2 sequestration processes. However, the non-linear and non-convex relationships among CO2 sequestration capacity, CO2-EOR effectiveness, and economic benefits significantly increase uncertainty in technological policy formulation.
This paper establishes a comprehensive optimization framework utilizing the Particle Swarm Optimization (PSO) algorithm, encompassing the formulation of objective functions, the development of numerical simulation models for typical reservoirs, and single-objective and multi-objective optimization method. In single-objective optimization, two objective functions—oil recovery factor and CO2 sequestration efficiency—are accomplished through weight assignment. This approach optimizes the combined objective function across different injection modes, including Continuous gas injection (CGI), Water-alternating-gas (WAG), and Gas injection after water flooding (GIW), while also considering diverse preferences for development strategies. In multi-objective optimization, the optimal well control parameter sets for CO2 sequestration and Net Present Value (NPV) are derived under different injection modes using Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and the Pareto front.
The results indicate that the multi-objective optimization method constructed in this paper can effectively address the problem of Co-Optimization of CO2-EOR projects. Compared to the base scenario and the single-objective optimization, the MOPSO optimization solutions on the Pareto front demonstrate superior overall performance in terms of oil recovery, sequestration capacity, and economic benefits through reasonable injection and production parameters that control the CO2 flooding front and expand the sweep efficiency. Water-Alternating-Gas (WAG) exhibits advantages in recovery rate and NPV while demonstrating lower sensitivity to carbon subsidies and CO2 injection costs.
The comprehensive optimization method proposed in this paper will significantly enhance the technical decision-making capabilities related to CCUS coupling projects in carbonate reservoirs, providing guidance for safe and efficient CCUS technologies.