bluuebunny/crossref_metadata_2025 huggingface dataset:
Digital Performance Model-Based Transformation Strategy Enables Significant Returns on Asset
Authors: Oestensen H.
Published: February 2025

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

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

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

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

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

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.
CO2 Foam Enables CCUS with Low Carbon Oil Production in Asia
Authors: Graue Arne , Alcorn Zachary Paul
Published: February 2025

Climate change concerns have accelerated efforts to reduce CO2 emissions from fossil fuel production. The IEA estimates that in 2035, 70% of energy consumption will still be from fossil fuels. Therefore, new energy resources must be developed, and the carbon footprint of fossil fuel production must be reduced. CO2 sequestration in geologic formations is a promising method to store large volumes of CO2 and help meet the goals of the Paris agreement. To mitigate climate change, gigaton volumes of CO2 must be permanently stored and this volume of CO2 is so vast that industry must participate. To attract industry, the economics of CO2 storage must be improved. This may be obtained by developing disruptive new technologies for CO2 sequestration which provide industrial revenues.

Combined CO2 enhanced oil recovery (EOR) and CO2 storage is effective to reduce emissions and provide energy security as part of the carbon capture, utilization, and storage (CCUS) value chain. However, early CO2 breakthrough and poor sweep efficiency are serious challenges in conventional CO2 EOR which severely limits oil recoveries and CO2 storage volumes. Adding surfactants when injecting the CO2 can generate in-situ foam, which reduces CO2 mobility, increases sweep efficiency, and improves CO2 utilization. An integrated enhanced oil recovery (IEOR) technology utilizing CO2 foam for low carbon footprint oil production has recently been developed at University of Bergen as an enabler of CCUS. To demonstrate the technology at a larger scale, we recently completed a CO2 foam field pilot in the Permian Basin of west Texas, initiated after conventional CO2 flooding.

The CO2 foam pilot produced an additional 40% of cumulative oil, compared to the baseline predicted conventional CO2 EOR performance. Cost savings exhibited a 20% reduced cost for CO2 and 75% reduction in expected operational time, compared to conventional CO2 EOR. This technology may represent a gamechanger with respect to sustainable economics in CO2 EOR because the Rate of Return was at a factor of 20. The pilot results confirm the ability of low concentration foaming solutions to reduce CO2 mobility, increase sweep efficiency, improve CO2 displacement, oil recovery and CO2 storage mechanisms. CO2 foam EOR may produce more oil in a shorter time, and more CO2 may be sequestered, compared to conventional CO2 EOR. CO2 foam EOR provides synergy between the need for increased energy production and the reduction in emission of anthropogenic CO2. Southeast Asia is the area of highest growth in energy demand and has increased CO2 emissions; thus, this technology is particularly relevant to Asia.
Automatic Detection of Hydraulic Fracturing Events by Real-Time Data Mining
Authors: Zhang Shuai , Sheng Mao , Meng Leifeng , Bai Kankan , He Jian , Tian Shouceng
Published: February 2025

Accurate, quick, reliable event detection is essential to Intelligent hydraulic fracturing. The real-time detection of events mostly relies on the expert knowledge. Although the machine learning algorithms had been used for events detection, the accuracy is still not stable that depending on the data quality and amount. This paper proposed an alternatively threshold-based algorithms with special sliding windows by integrating feature mining with expert knowledge from time-series data. The 378 stages of historical fracturing data were collected. More than 5 experts were organized to divide and label the six types of fracturing events involving wellhead pressure testing, plug ball seating, pad stage, proppant laden stage, flushing, and fracture closure stage from second-point data. The correlation between fracturing events and data features was built by using 10-seconds sliding window and forward difference to extract the slope features of pumping curves and their corresponding threshold values of pumping pressure, rate, and proppant concentration. Furthermore, the wavelet analysis was used to identify the fracture closure time from wellhead pressure after the fracturing is completed. Finally, the threshold-based algorithms by integrating feature mining with expert knowledge from time-series data were proposed to handle the fast detection of fracturing events. The model was tested on 200 stages of data and performed a high accuracy in fracturing events detection. Particularly, the accuracy of fracturing events detection ranges from 92.7% to 99.6%. The accuracy of Fracture Closure Pressure were 93.8%.
Multi-Scale Refraction Tomography Constrained by Geostatistical Information Method and its Application in Bohai Bay
Authors: Duan X. , Zhang Z. , Ding H. , Han M. , Gu Z.
Published: February 2025

As the largest offshore oilfield in China, Bohai Oilfield has made good exploration discoveries in various target strata in the past decade, especially in the exploration of deep buried hills. Because the shallow favorable targets have been nearly drilled out, the exploration focus of Bohai oilfield is gradually shifting to the middle and deep layers. The buried hill have become the most important research hots pot of Bohai oilfield, and several large gas reservoirs have been discovered in Archean strata. The main reservoir type of buried hill oilfield is fracture, which has complicated characteristics with low porosity, strong heterogeneity and complex fracture distribution. The average depth of buried hill is about 4500m, so it is difficult to accurately predict and describe good fractured reservoir in the exploration and development.

Because of the deep buried depth, the quality of seismic data is usually very poor. On the one hand, the effective signal of fractured reservoirs are very weak, and the low S/N seismic data is hard to recognize fractured reservoir. On the other hand, there are a lot of residual internal multiples, and their amplitude is even stronger than the reflected wave of fractures. Taking the normal processing procedure into consideration, most of the multiples like surface-related multiples have been well suppressed during the seismic data processing. In order to clearly identify the real reflection wave of fractured reservoir, it is necessary to suppress the internal multiples and enhance the effective signals.

Generally, the methods of suppressing multiple waves are divided into two categories: one is the filtering method based on signal analysis and processing, such as τ-p transform and f-k transform; the other is the prediction subtraction method based on wave equation, such as wave extrapolation method and inverse scattering series method. The first kind of methods have certain limitations: the dynamic correction speed of multiple waves and primary waves is similar, which weakens the effectiveness of τ-p transformation, multiple and primary waves have similar slopes, which weakens the effectiveness of f-k filtering. The second type of method mainly uses the generation mechanism of multiple waves to predict and suppress multiple waves. It comprehensively considers the kinematic and dynamic characteristics of multiple wave propagation and can adapt to complex underground structure. It uses its own data as a multiple wave prediction operator, requires less or no assumptions about underground structure, and attenuates multiple waves without damaging the primary reflected signal. For internal multiple waves, the current effective method is the inverse scattering series method based on the point scattering model. This method does not need a prior velocity structure model, and has a good adaptability to complex structures, but the calculation is very large.
Influence Analysis of Thermophysical Parameters of Hydrogen-Doped Natural Gas
Authors: Jiang Xinhui , Liao Kexi , He Guoxi
Published: February 2025

Hydrogen energy is critical to the achievement of a "dual carbon" approach and has a key role to play in the future development of new industries. Adding hydrogen to the natural gas pipeline can achieve large-scale, low-cost, and long-distance hydrogen transport. The objective of the study is to investigate the effect of varying hydrogen doping ratios (HDRs) on thermophysical parameters such as the compression factor of natural gas based on the Lee-Kesler-Plocker (LKP) equation of state. The developed procedure allows the calculation of thermophysical parameters under different HDRs. The investigation considers the effects of temperature, pressure, and HDR. The results show that pressure fluctuations significantly influence the compression factor, followed by the HDR and, to a lesser extent, the temperature. The order of sensitivity for each parameter to the HDR is as follows: Joule-Thomson coefficient (JT)> molar heat capacity at constant pressure (Cp) > molar heat capacity at constant volume (Cv) > compression factor (Z). These results provide a theoretical basis for understanding the transport of HDNG.
World Longest 9.625-Inch × 12.25-Inch Non-Directional Casing While Drilling in a Production Hole Section Through Highly Interbedded and Unstable Formations in an Operator.
Authors: Bethancourt R. , Kodiah B. , Aljabery R. K. , Al Reyami M. , Alsaweafi K. , Alwahedi K. , Faghaly T. , Ameh U. , Kosset T. , Nazarov T. , Khan Faisal Gul , Jouzy N. , Syafitri D. , Roberie J. , Balen A. V , Halma J.
Published: February 2025

The complexity of drilling 12.25-inch production hole in an operator encountered multiple drilling challenges such as total losses, wellbore instability, and unplanned trip due to bit failure drilling through highly interbed formations. These challenges affected both the drilling productivity and the well integrity. The adoption of Casing While Drilling (CWD) was a significant paradigm shifted in resolving these issues by maintaining well integrity and improving well delivery performance. This paper will illustrate how the CWD technology enabled drilling the longest 12.25-inch by 9.625-inch section in one run through unstable aquifer formations by saving 1.2 days of rig time and attaining achieving cementing quality.

The Non-Directional CWD Technology was deployed in a vertical production borehole section. The CWD used a casing string with a drillable body PDC bit and solid body centralizers for cementing. The approach emphasized careful engineering planning and preparation stages, customized to the requirements to overcome the main challenges for the CWD deployment in the pilot stage for final evaluation:

Drilling the section in one run and ensuring well integrity across aquifer zones during the cementing job. Drilling dynamics, hydraulics, bit design, offset well and casing centralization analysis were the keys to assess the feasibility of the CWD application through interbedded and unstable formations. Drilling with casing through highly interbedded zones with different hardnesses and mechanical properties was considered as one of the main challenges.

The CWD operation was finished in a single run with Zero QHSE incidents and minimum exposure of personnel to manual handling of heavy tubulars enhancing rig crew safety. The successful CWD implementation resulted in time savings of 1.27 rig days, in the production interval drilled in a single run (5,175 feet) through interbedded formations at a competitive Rate Of Penetration (ROP) of 47 feet per hour, enabling this to become a world longest 9.625-inch × 12.25-inch Non-Directional Casing While Drilling. The verticality was maintained at an average inclination of 0.3 degrees and a maximum displacement of 42 feet. The encountered losses were effectively reduced from 350 barrels per hour to 200 barrels per hour, demonstrating the plastering and wellbore strengthening effect of CWD which resulted in the subsequently good cement bond log across the aquifer formations. Exceptional cement bonding was maintained around the 9.625-inch casing indicative of good hole quality despite the significant dynamic drilling fluid losses.
Elastic Anisotropic FWI for Multi-Well Walk-Away DAS-VSP Datasets in a CCUS Pilot Field Onshore Japan
Authors: Nakayama Shotaro , Mouri Takuji , Podgornova Olga , Armstrong Philip , Khaitan Manish Lal , Bettinelli Pierre , Calvez Joel Le , Liang Lin , Zeroug Smaine
Published: February 2025

The application of Elastic Anisotropic Full Waveform Inversion (eFWI) to Multi-well Walk-Away Vertical Seismic Profiling (VSP) datasets, acquired with the Distributed Acoustic Sensing (DAS) technology, led to remarkable benefits in a carbon capture, utilization, and storage (CCUS) pilot project onshore Japan. Its application aimed to quantitatively estimate detailed subsurface properties by effectively accounting for complex wavefields in heterogeneous media through eFWI tailored to DAS data.

This novel approach employed in this study treats the DAS data as average strain along the fiber cable, enabling direct forward modeling and gradient computation based on strain measurements without necessitating data conversion. The inversion algorithm, which simultaneously inverts for multiple two-dimensional medium properties and source wavelets, incorporates three-dimensional well trajectory deviations and jointly utilizes multi-well datasets for a single inversion scheme to enhance subsurface property estimation.

Results from this application demonstrated the robustness of the algorithm to handle high background noise and significant amplitude variations inherent in our DAS datasets with minimal preprocessing. The applied inversion scheme effectively revealed detailed structural and stratigraphic features, aligning inverted compressional and shear velocities closely with existing well logs. Residuals between the observed data and simulated data confirmed the convergence of eFWI, leading to high accuracy of the estimated subsurface properties.

DAS-VSP has drawn significant attention in the industry, especially in the context of reservoir monitoring. By leveraging the ability of full waveform inversion, particularly its extension to muti-parameter inversion, a comprehensive and quantitative understanding of time-lapse changes in subsurface properties becomes realizable. These outcomes subsequently yield detailed insights into dynamic behaviors due to production and/or injection activities.
Improving Post-Salt Reservoir Image Quality Through Least-Squares RTM With DR-TLFWI
Authors: Yue M. , Chen Y. , Deng P. , Crabtree N. , Penman J. , Lange C. , Harrison A. J.
Published: February 2025

The reservoir imaging in Block 30, offshore Mexico, is challenged by complex overburden structures and sub-optimal acquisition setups, particularly in areas where intricate salt bodies overlay the reservoir. This study addresses these challenges by integrating Dynamic Resolution Time-lag FWI (DR-TLFWI) with Least-Squares Reverse Time Migration (LSRTM). The proposed DR-TLFWI optimizes contributions from low- and high-wavenumber components, improving velocity model accuracy for depths beyond the reach of diving waves. With the improved model, LSRTM compensates for illumination loss and enhances amplitude fidelity in complex subsurface conditions. The results demonstrate improved event focusing and gather flatness in the target reservoir, highlighting the effectiveness of DR-TLFWI with LSRTM for imaging zones with high-contrast in velocity.
Cutting-Edge Technique Fixing Control Line Leak in Corrosive Gas Wells
Authors: Kumar Rahul , Alkarbi Majed Ahmed , Al Hammadi Maryam Mohamed , Alkatheeri Mariam Taleb , Alkaabi Saeed Ahmed Sultan , Khalid Syed Farhan Bin , Makarem Abdallah , Hassan Mohamed
Published: February 2025

Well Integrity assessment is all related to barriers. Double barrier envelope policy is a well-known worldwide well integrity requirement. This can be validated through international standards: API RP90 (1 & 2), Norsok D10, ISO 16530 (1 & 2). Wells with positive pressure at surface must comply with double barrier policy. Failure of barrier envelope leads to sustained annulus pressure as well as loss of primary containment scenarios (LOPC). It is preferred and usually mandated to identify exact leak source for evaluating and implementing SAP curing methodologies (Rig or Rigless). Many a times, leak hunting is impacted by loss of downhole accessibility due to DHSV malfunction attributed to control line leak. In such a case conventional wisdom is to put the well on WO schedule and carry out costly Rig based repair.

There are very limited solutions available for normalizing highlighted situation in Gas well to restore DHSV barrier integrity, since such wells are inherently of higher well risk in nature and need durable fail safe well barriers for prolonged well life.

As an alternative to this proposed rigless advanced solution, pressure-activated sealants are available. However, such sealants have been observed to be of limited ability to build sufficient pressure through the leak, which is essential for polymerization. Success rate is dismal in cases of large leaks. It can potentially block the control line and may pop out under pressure. Also, it can be pushed back into the control line, if the Annulus A pressure is greater than control line pressure (in cases leak is exposed to annulus A pressure).
Case Study – Failure Analysis of Collapsed Production Casing in an Elevated Temperature Well
Authors: Zaifullah Z. , Nurdin A. I. , Widianto D. R.
Published: February 2025

During the production test of a newly completed geothermal well in East Java Province, Indonesia, steam leakage was observed from the annulus between the surface casing (20" 133 ppf) and production casing (13-3/8" 68 ppf). Identical pressure readings at the wellhead and annulus indicated leakage in the production casing. The highest recorded temperature during the production test was 145 °C, which was considered low compared to the anticipated maximum static temperature at downhole of around 320 °C. Inspection using a multi-finger caliper revealed that the minimum measured internal diameter (ID) of the 13-3/8" casing was 10.523", while the drift diameter for this casing was 12.259", indicating that the casing had collapsed. The collapse occurred close to the surface, specifically on the pipe body just below the casing collar at depths of 12.7 m and 14.6 m from the top of the annulus. The top 23.5 m of the annulus was not cemented, and water was observed in this annulus section prior to the production test.

This paper conducts a failure analysis of the casing, illustrating how an isolated, uncemented casing section with trapped water and heat acts as a pressure vessel, leading to high pressure buildup in the annulus. Additionally, it explores the relationship between water volume, temperature, and annulus pressure, contributing to the failure. The analysis demonstrates that the failure was caused by annulus pressure buildup due to the thermal expansion of water trapped in the annulus. The failure likely occurred at a temperature even lower than the maximum recorded temperature of 145 °C.

This paper will benefit drilling engineers, completion engineers, production engineers, and other parties working with elevated temperature wells, such as geothermal wells, wells in compacting reservoirs, steam-drive wells, or high-pressure high-temperature wells, in preventing similar casing failures.
Producer Acid Stimulation in Unconsolidated Sandstone Reservoirs: Continuous Evolution in Overcoming Challenges and Enhancing Well Performance in an Offshore Oil Field
Authors: Zheng Jv , Zhang Yanxu , Gou Xinjun , Cui Qingzhen , Wang Yuejie , Fu Yangyang , Yuan Xiaonan , Dai Xianghui , Li Wentao , Zhao Ning
Published: February 2025

The objective of this paper is to review the evolution of producer acid design and the well performance of different acid formulations in an offshore oilfield in China. The focus is on addressing the persistent near wellbore plugging challenges encountered after water breakthrough.

Over the past 15 years, acid stimulation in studied oil field has experienced five major modifications since 2007. Initially, acid design considered potential causes of plugging only. However, after evaluating well performance and identifying new challenges arising from acid stimulation, an optimized process was developed. This process includes plugging mechanism analysis, experimental acid formulation selection, acid deployment, and comprehensive performance evaluation. Evaluation parameters encompass changes in productivity index (PI), oil and liquid production, skin, flow efficiency, and the impact on production facilities. Simplifying the operational process to reduce operational cost (Opex) has been a consistent goal and a key consideration in acid design assessment.

Continuous optimization in acid stimulation has been achieved through years of experience and lessons learned. The acid system has progressed from hydrochloric acid (HCl) system to a slow-released hydrofluoric (HF) based acid system, and later to a moderate-strength HF based acid system, and later to a weaker chelant acid system. The novel chelant acid system has been introduced as an alternative to moderate-strength HF based acid system, offering low corrosion, low reaction rate, and high efficiency, making it particularly effective for stimulating plugged producers in waterflooded unconsolidated sandstone reservoirs. Initially, this acid system demonstrated moderate production incremental; however, through continuous acid formulation and process optimization, its current results are comparable to those of the moderate-strength HF based acid system. The operation process has become simpler, minimizing its impact on the production facility through ongoing optimization.

This paper also introduced the recent advancements in the emulsified acid system, which has improved acid distribution across intervals exceeding 500 feet in thickness. Implemented in 2021, this advancement has shown promising production outcomes.

This paper will primarily focus on the evolution of the chelant acid system and the latest application of the emulsified acid system. It will discuss the continuous optimization of acid systems and operational processes, and will also cover field well selection criteria, well performance, and the impact of these processes on production facilities.
Fluid-Modulus Inversion for Gas Prediction in the Deep-Water Area of Northern South China Sea
Authors: Xu Leyi , Liu Xumin , Zhang Weiwei
Published: February 2025

Deep-waters increasingly becoming a major field of hydrocarbon exploration, especially in the Northern South China Sea. Besides the extremely high expense of deep-water drilling, substantial-depth natural gas exploration is challenging due to the high complexity and uncertainties of deep water. These unpredictabilities often result in a significant uncertainy in potential reserves. The new method proposed by this paper can quantitatively predict fluid type and gas saturation by direct inversion of fluid modulus under quantitative prediction of reservoir parameters based on neural network. The main idea of such a novel method consists of two steps: Firstly, the critical porosity modeling method is used to find the relationship between rock reservoir parameters and rock elastic parameters, which paves a base for following gas prediction. Thereafter, the elastic parameters are found using the pre-stack elastic parameters inversion technique, and the elastic modulus K is used as a sensitive parameter for gas prediction. On this basis, neural network is used to find the inverse law of the above rock physical laws acting on the elastic parameters of pre-stack inverse bodies. Thus, the shale content and porosity of the reservoir are quantitatively predicted. Secondly, under the premise of quantitatively predicting the porosity and elastic parameters of the reservoir, the method of solid-liquid structure is used to bring the porosity as a known quantity into the inversion equation, in order to quantitatively predict the elastic modulus K of the reservoir fluid, and then realize the quantitative prediction of the gas saturation according to the high correlation between the fluid modulus K and the gas saturation. The method was proven feasible and reliable through multiple drilling in the deep-water area of Northern South China sea as it significantly reduces the uncertainty in prediction, which can be referred to other deep water hydrocarbon cases.
Multiplex Visibility Graphs-Based Hybrid Deep Learning Method for Recognizing Pipeline Operation Conditions Using Operating Data
Authors: Zhang L. , Wang P. , Liu L. , Liu J. , Chen Z. , Dong W. , Li C. , Fan L.
Published: February 2025

Accurate recognition of system operating conditions is the basis for ensuring the safe and stable operation of oil and gas pipeline network systems. However, the existing operating condition recognition mainly relies on manual experience, which cannot dynamically track the changes of pipeline operating status, and the operating data stored in SCADA system lacks operating condition labels, which makes it difficult to explore the potential value of the data. In this work, a hybrid neural network model based on multiplex visibility graphs (MVG) is proposed for operating conditions classification. Firstly, the generative adversarial network algorithms (GANs) are adopted to increase the sample size of abnormal conditions and unsteady conditions to deal with sample imbalance in oil and gas pipeline systems, and the reliability of the generated samples is verified by comparing the similarity of probability distributions of the generated data with the real data. Then, the operating data are mapped into the MVG. Next, an attention mechanism-based graph convolutional neural network (GCN) model and a long short-term memory network (LSTM) model are successively employed in the MVG for capturing spatial and temporal features in the operating data. By introducing the attention mechanism into the GCN model, the model can adaptively discover signal nodes with higher correlation to the target operating conditions, thus improving the classification accuracy. Finally, the proposed model is validated with historical operating data from a real multi-product pipeline system. The results show that the proposed hybrid method can be effectively applied to binary classification of scheduled operations/sling pump (accuracy = 0.95) and multiple classification of pipeline startup and shutdown/sling pump/switching pump/pigging (accuracy = 0.72). The proposed model has the highest recognition performance compared to both the single GCN model and the hybrid model without the attention mechanism. The proposed method is universal and can be promoted and applied to oil pipeline systems and natural gas pipeline systems, which is of great significance for realizing online monitoring of pipeline operating conditions and guaranteeing the safe transportation of oil and gas pipelines. It also provides a new idea for future research on the recognition of small sample operating conditions in oil and gas pipeline systems.
Casing Exit Excellence: a Holistic Strategy of Integrated Engineering Models and Well Integrity Analytics for Extended Reach Wells
Authors: Alharbi Atallah , Petre Rares , Gamal Hany
Published: February 2025

The increasing prevalence of re-entry wells, in response to global demand, necessitates casing exit operations in wells with low production potential, multi-lateral configurations, and obstructions. The rise of extended-reach drilling, facilitated by advanced technology, underscores the evolving landscape and challenges addressed by integrated analytics in this dynamic field. This paper concentrates on optimizing casing exit operations, particularly in extended reach drilling through the integration of technical torque drag and hydraulic model analytics, this approach aims to bolster efficiency while prioritizing well integrity.

The proposed methodology integrates torque, drag, and hydraulic considerations utilizing advanced simulation and analytics, this unified model comprehensively assesses downhole conditions, prioritizing well integrity for casing exit operation. By merging mechanical and hydraulic aspects in pre-job planning, the engineering team provided a holistic understanding, facilitating real-time monitoring and adjusting. This approach empowers dynamic parameter adjustments during casing exit operations, ensuring a proactive stance towards potential challenges. The proposed methodology delves into the technical intricacies of drillstring design and the strategic placement of heavy wall drill pipes.

The integrated model's effectiveness extended to overcoming challenges in the wellbore restrictions, drilling fluid system crucial aspects of casing exit operations for running in the hole, setting, and milling the window. The comprehensive technical engineering modeling incorporated recommendations for optimizing the drilling fluid system, flow rates, and pumping pressures. By technical optimization of these parameters, the integrated solution contributed to reduced friction factors, mitigating torque and drag issues during casing exits. Additionally, the model provided friction factor recommendations that significantly improved the overall drilling efficiency. Notably, the model's capability facilitated real-time adjustments in whipstock setting and milling parameters, allowing the system to seamlessly navigate complex downhole environments in the extended reach job. This approach adeptly manages buckling near liner hanger depth, guaranteeing a secure reach to the total depth crucial for the successful running and setting of the whipstock in a single trip at a setting depth of 18500 ft.

In conclusion, the holistic integration of technical torque and drag with hydraulic models’ analytics represents a significant advancement in casing exit operations, emphasizing the paramount importance of well integrity. This approach improves decision-making, and operational efficiency, and also serves as a proactive strategy to safeguard well integrity throughout the entire casing exit process.
Unplanned Casing Shoe Setting Led to a New Well Schematic Optimized Design for Futures Wells at Quesqui Field
Authors: Rivera A. , Rodriguez C. , Peralta J. , Hernandez A. , Maldonado R.
Published: February 2025

This paper documents the successful optimization of well schematic after an unplanned casing setting. That failure required the use of an Ultra Low Invasion Reinforcement Technologies while drilling Q-49 well in Mexico, which changed the well schematic design for all future wells. The use of these technologies allowed for the effective expansion of the operating window, ultimately leading to a successful completion of the drilling stage without the need for additional casing. The study highlights best practices and lessons learned during the drilling process.

The implementation of Wellbore Shielding technology (also known as Ultra Low Invasion) in the recent field development has proven to be an extremely resistant and flexible solution for real-time hole reinforcement while drilling. This technology has enabled continuous operations without compromising well integrity. Constant monitoring of concentration levels through SBT (Sand Bed Test) enables early detection of unfavorable conditions and prompt action, as demonstrated in the case study to be presented. The use of software while drilling also led to early detection and monitoring of the fluid conditions.

This application allowed the operator to increase the operative window over 900 psi, without destabilizing the wellbore. The drilling fluid density used exceeded well above the maximum permissible level of the density gradient, however no destabilization was observed. This solution also resulted in the elimination of an additional casing to isolate the two formation pressures. Since two different formation pressures had to be drilled with the same drilling fluid, the use of this technology represented a dramatic reduction of fluid loss rate, from 95 bbl/h to less than 1.9 bbl/hr.

This case study demonstrates how using an appropriate formation-strengthening technology, which is proven to significantly increase the operating window and significantly reduce lost circulation under dynamic conditions without suspending operations for loss control, optimizes operating costs and improves operating conditions, leading to the well's success.