bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating Surface Roughness Effects on Novel Indentation Testing of Drill Cuttings
Authors: Rached R. M. , Gramajo E. , AlDajani O. , Albahrani H.
Published: November 2024

Conventional methods to determine geomechanical properties (e.g., cores, drilling logs) are limited, costly, and usually confined to reservoir intervals. This study explores drill cuttings as an additional data source for geomechanical properties using micro-indentation testing. Specifically, the research focuses on the effect of surface roughness and aims to establish the minimum requirements for a surface finish that ensures consistent and reliable results. Our findings offer a systematic and cost-effective method to obtain geomechanical parameters, benefiting oil and gas projects and energy transition initiatives.

The approach involved extracting rock discs from eight different rock types: 2 shales, 3 carbonates, and 3 sandstones, and then polishing them using a series of various grit sandpapers, suspensions, and pastes. The surfaces of the polished rocks were scanned using optical profiling to quantify their roughness. Data preprocessing techniques were applied to remove outliers and correct surface level trends, and then root mean square (RMS) and average roughness (Ra) roughness parameters were calculated for each profile. Grid micro-indentation experiments were then conducted to measure the micro-indentation elastic modulus. The results were analyzed and compared for different surface finishes to identify the minimum roughness requirements for each rock type.

The study reveals that the sensitivity of instrumented micro-indentation testing to surface roughness is influenced by grain size and mineralogy. In most cases, rock surface polishing incrementally down to a P4000 grit sandpaper provides consistent microindentation results. By understanding the impact of surface roughness on geomechanical testing, this research contributes to improving the reliability and accuracy of geomechanical parameter determination and applies to most rock types and specifically, to drill cuttings. These insights have significant implications for the energy industry, presenting a cost-effective and accessible approach to obtain comprehensive geomechanical properties along the entire length of a well.

This study introduces new insights into the minimum surface finish requirements necessary for consistent and reliable microindentation results. By addressing the challenges in geomechanical testing, this research paves the way for a cost-effective approach to acquire geomechanical properties across entire wellbores.
Experimental Investigation of Degradable Solids Diverter Efficiency for Different Completions Used in Matrix Acid Stimulation
Authors: Zhao H , Tavarez F. A. , Gomez M. P. , Almubarak M. , Hill A. D. , Zhu D.
Published: November 2024

A known challenge in carbonate acid stimulation is achieving a uniform acid distribution in long wells along a heterogeneous reservoir. Completion strategies such as running a limited-entry-liner may not be sufficient to achieve the desired conformance. In recent years, degradable solids have become one of the most common diverters for acid stimulation. However, their behavior in uncemented completions is not well understood. In this study, parallel dual-core flow tests were conducted to evaluate the impact of completion type (cased hole vs. open hole) on diverter efficiency.

In the dual-core flow setup, two limestone cores with a permeability ratio of 30:1 were used, and experiments consisted of three main flow stages. First, 15wt% HCl was injected, which tended to create a wormhole in the high-perm core. A specified volume of a solids diverter slurry was then injected to improve the flow distribution. The final stage consisted of injecting 15wt% HCl until breakthrough. The flow rate of each core was recorded throughout the test to evaluate the diversion effect for both open hole and cased hole completion scenarios. CT scans of both cores were obtained to visualize the wormhole geometry after each acid stage.

Test results showed significantly different diverter performance for the cased hole vs. open hole scenarios, leading to insightful guidelines on diverter efficiency depending on completion type. After the first acid stage, a dominant wormhole was created in the high-perm core only, mainly due to the large permeability ratio. This behavior was very similar for both completion scenarios. In the open-hole test, the diverter formed an external filter cake on the injection face of the high-perm core. The second acid stage easily broke through this external filter cake and continued to propagate the original wormhole. As a result, acid diversion was limited, and a relatively short wormhole was created in the low-perm core. However, in the cased-hole test, a relatively higher volume of diverter entered the perforation / wormhole structure, leading to a more effective diversion and hence a more even stimulation of both cores after the second acid stage was injected.

The large-scale dual core flow setup in this study is the first of its kind and using it in conjunction with the implemented workflow has provided valuable insights into how various completion types will impact solids diverter efficiency during matrix acid stimulation. As completion lengths continue to get longer, a good understanding of solids diverter efficiency, especially for open hole completions currently being implemented in the Middle East, is necessary to develop reliable models that can be used for a more effective treatment design.
Retrieving Operation Insights with GenAI LLM: Comparative Analysis and Workflow Enhancement
Authors: Lee M. X. , Wang Z.
Published: November 2024

Identifying drilling risks for upcoming wells demands insights from offset wells. While some risks may lead to non-productive time (NPT), others remain latent within daily drilling reports (DDRs), which is crucial for comprehensive analysis. Manual extraction from DDRs is often time-consuming. In this paper, we systematically explore the methods to leverage the Large Language Models (LLMs) with enhanced workflows to automate insights extraction and improve risk identification accuracy. We compared LLMs customization approaches and state-of-the-art NLP models to validate LLMs efficiency in the drilling domain. We show that the adaptation of prompt optimization, Retrieval Augmented Generation (RAG) with the pre-trained LLMs before fine-tuning the model, already shows a significant improvement, achieving an accuracy ranging from 80-85%, with precision and recall surging from less than 50%, as observed from our benchmark oil and gas finetuned GPT-2 model (Marlot et al., 2023), to over 70% with our customized model. We addressed the challenge of imbalance training examples during the finetuning of the state-of-the-art GPT-2 model, which caused the underperformance of the benchmark model, particularly in addressing rare drilling event classes.

This paper demonstrates LLMs able to improve the generalization and reliability of the imbalance drilling risks, with proper workflow enhancements. Subsequently, we also finetuned both on-premises LLMs (Llama2, Llama3, Mistral), and cloud LLM GPT-3.5 Turbo, which exhibits promising outcomes, achieving better performance to over 90% accuracy, and over 80% precision and recall, compared to the earlier approach using prompt and RAG. This paper contributes to advancing the application of LLMs in the oil and gas industry, for domain-specific tasks, addressing the common challenge of imbalance operation training data, and demonstrating our enhanced workflow outperforms both the benchmark finetuned GPT-2 model, pretrained GPT-4 model, and the finetuned GPT-3.5 Turbo.
Succesful Pilot Water Conformance Control Project at The X Field of The North Caspian Depression in Kazakhstan
Authors: Aliyev A. , Koszhanov A. , Samenov E.
Published: November 2024

Polymer used for water shut off (WSO) and conformance control (CC) is required to withstand both the temperature and salinity of the reservoir over prolonged timescales of months. For these reasons it is typical to find them as copolymers of acrylamide and acrylic acid, otherwise known as partially hydrolyzed polyacrylamides (HPAM).Selection of a polymer depends on more than the stability; it also depends on the application and desired effect. All lead to the final CC or WSO design.Type of application CC vs WSOLow MW vs high MW polymerChargeconcentrationPermeability and Homogeneity of the reservoirconcentration, topside viscosityDistance to the targeted treatment zoneconcentration of polymer, crosslinker, retarderHistory of the reservoirWell logs for streaks etc.production issuesconing or viscous fingeringWSO polymers tend to be lower molecular weight to be able to penetrate deeply into smaller pores. lower viscosity on topside facilities also allows higher concentrations. Down hole, these are able to then set up strong well held rigid gels that prevent water flow

One of the best tools to identify the zones were WCC can give a better result is tracer test. Tracer applications in the oil industry began in the mid-20th century (Hutchins et al. 1991; Serres-Piole et al. 2011, 2012). Today, tracers and nanoparticles are integral to various oil and gas drilling and testing operations. These substances are introduced into borehole fluids to trace fluid pathways, migration mechanisms, and well connectivity. Tracers are extensively used in drilling, water flooding, and E&P stages to assess hydrocarbon migration, optimize flooding strategies, and estimate residual oil saturation (Sor) and enhanced oil recovery (EOR) (Bjornstad et al. 1990; Clayton 1967; Michael Shook et al. 2004; Michael Shook et al. 2017; Sun et al. 2021; Sun and Ertekin 2020).

The X field is at a late stage of development of its only production target - the Valanginian horizon. The development of this horizon is envisaged with waterflooding. The reservoirs of the Valanginian horizon are mainly composed of 3 layers. Despite some differences in capacitive parameters, they are all hydrodynamically connected.
Drone Measurements Ensure Global OGMP 2.0 Level 5 Compliance
Authors: Smith B. J. , Cooper I. M.
Published: November 2024

The need for globally deployable, consistent emissions measurement programs is increasing as clean energy feedstock demands and regulatory pressures continue to rise. Many programs, such as Oil & Gas Methane Partnership (OGMP) 2.0, require high-accuracy and precision ‘bottom-up’ emissions calculations and facility-scale ‘top-down’ measurements. The challenge the industry faces is deciding where to allocate resources between abatement and measurement/reconciliation of GHG emissions. This work shares the deployment of a fit-for-purpose global emissions measurement program that is critical for measurement and reporting frameworks.

All surveys were conducted with an independently validated, field-proven, drone-mounted miniature tunable diode laser absorption spectrometer (TDLAS) designed to measure methane emissions in the atmosphere from energy-industry sources emission sources. High-fidelity anemometer data and high-accuracy and -precision methane measurements are necessary to ensure low uncertainty methane fluxes calculated using mass-balance algorithms. Facility-level emission rates are reported through a single measurement or the aggregation of several equipment group-level measurements. Standard operating procedures relevant to the application of any new "advanced" methane technology were developed to scale for global demand of methane measurement and reduction, ensuring consistency of solution across the globe and across operators.

Surveys were scaled for numerous operators across 6 continents and growing from 0 to 24 global drone service providers (DSPs) over 7 years. Logistical challenges were overcome to ensure technology was transported into the regions of interest and quality of service maintained. Regional DSPs were trained and certified to ensure consistency of service operation and excellence in execution. The measurements from these campaigns show a dramatic increase in impact over time, and begin to highlight, in resolution, the differences and similarities between regional emission footprints, both onshore and offshore. Many of the emissions measurements shared with operators have resulted in change of behavior, accelerated repairs or the inclusion of validated data to internal/external methane measurement initiatives. Reconciliation between top-down and bottom-up emissions can be challenging, showing the importance of "continuous improvement" initiatives such as OGMP 2.0.

This work shares the challenges of deploying novel technologies for the novel emissions measurement globally, highlighting operator-accepted cost-effective solutions that ensure global consistency across multiple assets in multiple countries, onshore and offshore, facilitating swift and transparent reconciliation.
Mechanistic Model for In-Situ Diverting Acids Flow in Carbonate Formations: From Lab to Field Treatment Optimization
Authors: Ali Mahmoud T.
Published: November 2024

In-situ diverting acids are commonly used in carbonate acidizing treatments to improve acid coverage by redirecting acid to lower injectivity formation. Most of the models to represent the flow of these in-situ diverting acids, in literature are semi-empirical with very limited predictive capability. Some studies use numerical simulations to accurately capture the physics and dynamics of the process, however, they are computationally intensive, even on lab scale.

This study presents a computationally fast physics-based model to simulate and predict the pressure response of in-situ diverting acids. The model divides the formation into three zones, namely, wormhole, disturbance, and virgin zones. The change in acid concentration is tracked in the wormhole and porous media by calculating the overall reaction rate and acid velocities. An abrupt increase in viscosity occurs as pH rises above 1 or 2, which generates disturbance/turbulence in the flow. A Forchheimer-like equation is used to account for the increase in pressure due to flow turbulence.

Core flow experiments were conducted at 175°F and 250°F using limestone and dolomite cores treated with in-situ gelled acid (ISGA) and VES-based acid to validate the model. The comparison with linear experiments revealed that, unlike semi-empirical models, the current model captures the complete pressure response observed experimentally. Additionally, it scales very well with the injection rate. Radial experiments from the literature were used to validate the radial version of the model. Remarkably, the model is not only in agreement with radial experiments but also scales well with radial core dimensions. Synthetic one-layer and two-layer radial models were built, and both models revealed that the initial permeability and permeability range have a strong effect on divergence. A multi-layer example is presented to shed light on the model's ability to capture the impact of diversion on fluid distribution.

This study describes the development of a mechanistic model for in-situ diverting acid flow under linear and radial geometries. The model specifies the effect of formation permeability contrast, temperature, mineralogy, and wellbore diameter on chemical diverter selection and on acid strength, volume, and injection rate optimization. This study provides useful recommendations on how to design the placement of in-situ diverting acids and the critical factors that need to be considered.
Advancing Geoscience with Multi-Modal AI: A Comprehensive Copilot
Authors: Altynova A. Y. , Kozhevin A. A. , Dubovik A. S. , Khudorozhkov R. L. , Suurmeyer N. , Martin T. Jones
Published: November 2024

In geology, LLMs (Large Language Models) have been applied to various tasks, including document analysis, geological consultation, workflow planning and reservoir simulation [1, 2, 24]. However, existing applications often focus on single-modality data or specific subdomains within geoscience, limiting their overall effectiveness in addressing the full spectrum of industry needs. As a result, LLM-based agents are not yet fully present in all the stages of the seismic exploration pipeline.

The objective of the multi-modal AI copilot extension presented in this paper is to address these limitations by creating a comprehensive, integrated system capable of processing and analyzing diverse geoscience data types. This extension aims to enable seamless interaction between users and their geoscience projects (including but not limited to seismic processing, interpretation and well data analysis) through natural language interfaces, while executing complex commands and analyses on multi-modal data, including seismic volumes in SEG-Y format, through integration with 3D visualization software APIs.
Electrifying Offshore Oil and Gas Assets with Floating Offshore Wind: A Demonstrator for Future Energy Solutions
Authors: Benhamou A. , Forby B. , Barrier P. , Ester F. , Crews C. , Barral S. , Marmier R. , Villette S. , Mazevet N.
Published: November 2024

TotalEnergies is a global integrated energy company that produces and markets energies: oil and biofuels, natural gas and green gases, renewables, and electricity. The company affirms its ambition to be a major player in the energy transition and shares a vision of what its activities could be to achieve carbon neutrality by 2050, together with society. In order to achieve its ambition, the company undertakes numerous actions to reduce its carbon emissions and produce more low-carbon electricity.

TotalEnergies has launched a project to demonstrate the possibility of electrifying existing oil and gas assets in the North Sea via the installation of a Floating Offshore Wind Turbine (FOWT), which will be connected to the existing Culzean Field gas platform located in the Central North Sea area approximately 230 kilometres east of Aberdeen. The involved TotalEnergies affiliate is leading the design, construction and installation project, strongly supported by the OneTech branch which is supporting the follow-up of the engineering and takes the benefit of the electrification project to develop a R&D program.

This project involves conducting a hybridization showcase on an offshore oil and gas asset and qualifying a novel semi-submersible floating substructure WTG concept.

This paper presents the specifics of the project, the challenges, and the benefits brought by the electrification of an offshore oil and gas platform with a Floating Wind Turbine. In particular, the following topics are addressed:

A comprehensive project overview, Design, fabrication, and installation challenges of a floating wind turbine adjacent to an oil and gas asset, Modifications of the existing oil and gas platform to accommodate the connection of the floating wind turbine, Quantified greenhouse gas (GHG) savings and implications for future energy projects.

This publication presents a novel approach to reducing carbon emissions from offshore oil and gas assets by demonstrating the possibility of electrifying them with Floating Offshore Wind Turbines. The project showcases the viability of such hybrid systems for future projects everywhere else in the world, and provides valuable insights into the design, construction, and installation challenges of floating wind turbines adjacent to oil and gas assets.
Assessment of Static Models by Performing Fast-Track History Matching
Authors: Parra H. , Singh M. , Jeddou H. Ben , Su S. , Kloucha C. Kada , Al-Kindi Z. S. , Mustapha H.
Published: November 2024

Implementation of robust geological analysis for static reservoir characterization does not provide a guarantee about the prediction of dynamic behavior in the reservoirs. The reservoir properties are often populated using geostatistical methods in a static model. These methods are dependent on input data density and quality. The fluid movement inside this reservoir is simulated during dynamic modeling. In the case of many complex reservoirs, it is observed that the dynamic model response doesn't match with observed historical well data and modification in a static model is needed. This loop of dynamic-static modeling impacts the project timeline significantly. The objective of this work is to provide a dynamic validation tool used in a very early stage to perform quick assessments of the static model by doing a fast-track history matching.

In this paper we have developed a solution that provides fast-track assessment of static models to check the response of dynamic properties before starting the full history-matching exercise. The solution uses the static reservoir properties and observed data as its main input. It provides a list of fluid properties based on some basic fluid types. It enables use of core constants to define special core analysis (SCAL) function and rock compressibility to define rock compaction.

After running the fast-track simulation, the key performance indicators (KPIs) are checked to assess the quality of the model. The liquid production rate and pressure in all the wells have been chosen as KPIs to demonstrate the impact of static model properties on the dynamic model. A mismatch analysis is performed on these KPIs in all the wells. The wells are classified as good, acceptable and needs improvement, categories based on different thresholds in mismatch analysis. This mismatch is analyzed in map view, which is used to identify key challenges in static model for improvement.

This approach provides a quick assessment of static models and their modifications before starting the detailed dynamic modeling exercise. It saves significant time in the projects, enabling geoscientists to have an early evaluation of the behavior of the models from the dynamic point of view, reducing the number of future iterations in the history-match phase.
The Future is Here. Utilizing Physics-Informed AI Models to Optimize Highly Intricate Water Injection Systems Within a Mature Oil Field
Authors: Franco F. , Sepulveda W. , Marchán L. , Manzo B. , Cattani M. , Medina J. , Aguilar Ledwin , Somale J. , Guamán J. C. , Florez F. , Kartoatmodjo G. , Correa R. , Vallejo R. , Pinto T. , Ontaneda N. , Gimenez E. , Johnson L. , Jacobsohn G.
Published: November 2024

This paper discusses the development of an integrated model-based solution for a mature oilfield. The solution integrates state-of-the-art hydraulic models with cutting-edge Physics-Informed AI, to create a solution for the optimization of the water injection and disposal Network, which is impacted daily by the dynamics of fluid composition and the changing operational parameters in wells and surface flowlines across the field. The solution is capable of thoroughly assessing production and injection systems for improved forecasting and operational excellence

The primary goal in developing this solution is to enhance asset management and improve decision-making by considering the interactions and synergies between injection wells, surface flowlines, and facilities constraints. The solution was developed using calibrated well and flowline models built within the automatic Flow Assurance Smart System (FASS), a digital solution that utilizes high-frequency data, machine learning insights and automatic updates of the hydraulic predictive models. This digital solution allows for the continuous evaluation of injection/disposal components including injection wells and hundreds of kilometers of surface pipelines.

The implementation of this solution led to notable enhancements in the overall field injection and disposal systems, driving significant improvements in operational efficiency. The solution enables a comprehensive evaluation of various operational scenarios by integrating the number of injector wells with network model and aligning these with the broader field strategy. This integrated approach supports the identification of immediate optimization opportunities and provides insights into the long-term dynamic impact on field development.

The system's advanced capabilities play a crucial role in identifying bottlenecks within the field's injection and disposal processes. By optimizing water injection rates and ensuring the efficient disposal of produced water, the system enhances the reservoir's pressure maintenance and overall productivity. This level of optimization is essential for achieving the aggressive oil production targets set for the asset. Moreover, the ability to evaluate and adjust to multiple scenarios allows for a more resilient and adaptive field management strategy, ensuring sustained performance and reliability in meeting the asset's goals
An Award-Winning Automation Strategy Taking PDO To Top Quartile Performance
Authors: Al Mawali S. , Al Alawi A.
Published: November 2024

The global demand for energy continues to rise, with hydrocarbons remaining the primary source until renewable technologies mature. The upstream oil and gas industry faces increasing complexity due to depleting traditional fields and stricter regulations, necessitating sophisticated operations. Despite significant investments in automation, many plants underperform due to deficiencies in process control performance.

In 2020, Petroleum Development Oman (PDO) launched a transformative automation strategy to enhance control performance, reduce process upsets, alarms, and interventions, and ensure sustainable remote operations. This manuscript provides an overview of PDO's strategy, detailing the methodology, implementation phases, and the significant value realized.

Key steps included establishing KPIs for controller uptime and compliance, setting performance targets, and deploying digital tools for real-time monitoring. By 2021, 100% of PDO facilities were covered, with controller uptime improving from 78% in 2019 to 95% in 2024, and compliance from 73% to 91%. These improvements led to a 30% reduction in alarm rates and a 90% reduction in operator interventions.

PDO's strategy demonstrates how a structured, data-driven approach can optimize automation investments, enhance operational efficiency, and achieve significant financial benefits. The insights gained provide a benchmark for other organizations seeking to improve their automation performance.
Drag Reducing Agent Application in Produced Water Reuse: Improved Movement Efficiency with ESG Enhancement
Authors: Price B. , Li Y.
Published: November 2024

In response to environmental, social, and governance (ESG) initiatives, the oil and gas industry has broadly transitioned its efforts from using freshwater for hydraulic fracturing (frac) to using produced water for hydraulic fracturing whilst minimizing freshwater usage. However, the present infrastructure in place for movement of water to frac sites limits the volume of produced water that can be transferred in many situations. As infrastructure is not easily replaced, a polymer-based drag reducing agent (DRA) can chemically alter pipe friction to allow for use of existing infrastructure with minimal supplementation. Formulating a DRA with shear stability and chemical compatibility in produced water remains a significant obstacle and requires thoughtful consideration with use of specific chemistry.

Our approach to addressing this challenge involves use of a widely known and accepted chemistry that is currently in broad use globally. Polyacrylamide polymers are used in many applications in municipal water treatment, industrial water treatment, in mining applications, in dust suppression applications, as hydration aids, as friction reducers, and as drag reducers. The broad use and availability of this chemistry makes it a great fit for application as a DRA.

The paper will discuss our methodology and several case histories that demonstrate the feasibility of this application and the inherent benefits with regard to efficiency gains and ESG enhancement.
Data-Driven Analytics for Integrated Fracture Characterization in a Lower Cretaceous Reservoir, Offshore Abu Dhabi
Authors: Hong Y. , Kirstetter O. H. , Al Neyadi A. A. , Aljaberi F. , Al Kaabi A. S. , De Ribet B.
Published: November 2024

Fractures are crucial factors that affect the productivity and safety of hydrocarbon reservoirs. However, conventional methods of fracture detection and characterization are often limited by data availability and quality. In this study, we present a data-driven analytics approach that integrates four different seismic methods to predict and characterize fractures in a Lower Cretaceous Reservoir in an offshore oilfield in Abu Dhabi. The four methods are: Velocity Variation with Azimuth (VVAZ), Amplitude Variation with Azimuth (AVAZ), Amplitude Fracture Estimate (AFE), and Fault Likelihood. We apply these methods to 3D seismic volumes and verify their results with well data, drilling records, and well test data.

We integrate seismic data, drilling records, and well test data to evaluate the results in characterizing the fracture zone within the Lower Cretaceous Reservoir, which exhibits strong anisotropy, significant time shifts on seismic data, and notable mud loss during drilling. The VVAZ and AVAZ methods estimate fracture orientation and density from azimuthal variations of velocity and amplitude, respectively. The AFE method measures fracture intensity from azimuthal fractional energy. The Fault Likelihood method detects fracture zones from seismic discontinuities related to faults.

The results show that each method provides different aspects of fracture information that are complementary to each other. The VVAZ, AVAZ, VVAZ & AVAZ joint inversion methods reveal a dominant fracture orientation of NE-SW, consistent with regional stress directions, and a high fracture density in the central crest part of the field where mud loss was observed during drilling and high permeability zone was detected by well pressure test. The AFE method and the Fault Likelihood method identify several fault-related fracture zones that crosscut the field, some of which coincide with mud loss and pressure anomalies.

This study demonstrates an integrated approach that combines the four fracture prediction methods and can provide a comprehensive and reliable fracture characterization in the Lower Cretaceous Reservoir. This is essential for optimizing drilling and production strategies, ensuring safe operations, and maximizing hydrocarbon recovery in line with ADNOC's strategic objectives for its offshore assets.
Saturation Distribution of Carbonate Reservoirs with FWL Tilt in Lower Shuaiba and Kharaib Formations
Authors: Ali M. Y. Al , Kobenko I. , Ebeid M. , Alkhatib M. , Khattak I.
Published: November 2024

The Lower Shuaiba and Kharaib formations in Abu Dhabi's onshore fields present unique challenges for reservoir characterization due to their complex carbonate structures and post-depositional tilting. Understanding fluid distribution within these tilted reservoirs is crucial for accurate saturation modeling, which directly impacts reservoir management and hydrocarbon recovery.

Traditional saturation models often fall short in accounting for the effects of tilting and subsequent fluid redistribution. This paper introduces a novel approach to saturation modeling that incorporates the imbibition effects resulting from these geological processes. By leveraging the Brooks-Corey SHF model and integrating multi-domain data, we propose a simplified, yet robust saturation height model tailored for these specific formations.

Our methodology addresses the discrepancies observed in saturation levels across different reservoir zones, particularly in the flanks affected by strong imbibition. Through a comprehensive workflow that includes static rock typing, uncertainty analysis, and validation in both 2D and 3D domains, we aim to provide a practical solution that enhances the accuracy and applicability of saturation models in complex carbonate reservoirs.
Artificial Intelligence Associated Drones Solutions for Waste Disposal Management in the Process Industries
Authors: Reddy Burri Harsha Vardhan , Ramakrishnan Vijayaraghavan , Almuaikel Maha , Alanazi Khalid , Alshaikh Ahmed Ali
Published: November 2024

The paper aims to provide an overview of "Waste Management Solution," an Artificial Intelligence computer vision solution that can detect the location, classify, and quantify waste on a geospatial map constructed by aerial images collected with drones. The objective is to demonstrate how drones with integrated AI solutions can drive efficiency, productivity, and innovation in industrial operations. The solution comprises drones collecting aerial image data and implementing cloud-based AI/Machine learning (ML) models to detect waste materials. By integrating drone technology, AI, and mapping techniques, the solution supports industrial organizations, government authorities, and environmental agencies in achieving their net-zero goals, aligned with the Saudi Green Initiative 2030, and aiming to create a cleaner environment. The solution offers a cost-effective method for processing industries facilities and the environmental and urban planning of smart cities by digitizing waste management practices, replacing time-consuming manual industrial waste inspections. The benefits of the solution are demonstrated through our experience of an actual project conducted with the project management office of a large oil and gas operating company.
Dilution for Efficiency: Cost Reduction Strategies in Oil Production by Removing Desalter and H2S Stripper Units
Authors: Al Alawi Kathiya , Yarubi Nasser , Joshi Amol
Published: November 2024

This paper presents a strategic approach to optimize the ALG Project by eliminating the desalter and H2S stripper units, aiming to reduce project costs, Unit Technical |Cost UTC (CAPEX and OPEX) while maintaining operational efficiency. Alternatively, the study explores the feasibility of diluting ALG fluids with total fluids in the Main Oil Line (MOL), emphasizing cost-effectiveness, risk reduction, and simplification of both capital expenditure and operational complexity.

Desalter and H2S stripper units are essential for treating produced fluids to meet quality and safety standards. However, these units incur significant capital and operational costs.

Furthermore, the feasibility study associated with diluting ALG fluids in the MOL underscores the importance of evaluating technical, economic, and environmental considerations. It involves assessing compatibility between different types of fluids, evaluating the impact on pipeline integrity and performance, and ensuring compliance with regulatory requirements and industry standards.

The study has considered a multiple of scenarios of other stations upset since final technical proposal is based on dilution of oil with other stations fluids at the main oil line MOL.
Safe Welding Initiatives
Authors: Alsultan Abdullah
Published: November 2024

Maintaining safety while operation and maintenance process is a challenge. Nowadays, challenges can be tackled by new approach such as adopting 4IR technologies. One of the maintenance activities is welding process which associated with safety and risk concern such as fumes inhalation, physical hazards, heat zone radiation, and welding Hazards. To mitigate these concerns, new technology can be adapted such as "Automated Pipe welding Technology" and "In Line Weld Test Tool (IWTT)".

Automated Pipe welding Technology

Automated robotic pipe welding technology is a new generation with advance features to support welding activities. Deployment of this technology has been successfully completed, and it is a part of initiatives towards solving challenges using 4IR technologies. The Automated robotic pipe welding machine will be very useful to handle high capacity fabrication works including, but not limited to, Preventative Maintenance Shutdown, T&I workloads, emergency jobs and corroded piping replacements along with routine welding jobs. It can for example, weld 8″ OD pipe just in eight minutes with the highest accuracy and weld quality instead of four hours regular welding period using manual welding processes. The core of the Automated welding technology is its unique vision-based penetration control system. It is equipped with a video camera integrated into the welding arm that can continuously analyze the image of the root-pass welding.

In Line Weld Test Tool (IWTT)

In Line Weld Test Tool (IWTT) is a new technology supporting pipe replacement activities during the welding stage. Deployment of this technology has been successfully completed with proven the effectiveness of the technology. IWTT will be very useful to reduce maintenance turnaround time and total cost as well as maintaining oil production sustainability. IWTT provides a verified and monitored atmospheric barrier (ambient isolation) to perform welding process safely and efficiently. Also, it provides a fast and efficient method of verifying the integrity of new weld joints and to perform the hydrostatic weld test for the new weld joint. This will reduce the system downtime, minimize environmental impact, and increase the safety in the field activities. In addition, it will minimize the overall consumption of the treated water to perform hydrotest.
Maximize Workfront Availability to Reduce Project Delivery Time and Cost Using AI Advisors
Authors: Daniel Varghese
Published: November 2024

Projects are declared ‘completed’ when construction activities are completed and pre-commissioning is done and handed over for commissioning and operations. So, the most important objective for a project management team is to ensure availability of continuous Workfront to carry out construction activities and any disruption or delay in availability of Workfronts will delay the project completion and cause other side effects like idling resources and reduced productivity, all of which will contribute to the final cost overrun. There is a solution: using AI Advisors within a digital Project Management Information System to ensure continuous Workfront availability and so solve the problem at the source.
Offline Tubulars Bucking Service with Triplex Catwalks
Authors: Hassan T. F. , Hussain M. , Al Rumaithi Khaled , Alzaidi Saif , Al Reyami M. , Al Suwaidi M. , AlSaadi H. , Al Shamisi E. , Sumaida A. , Al Mazrooei A. Saeed , Aljabery R. K. , John J. , Al Nuaimi M. , Hamdy I. , Suleiman A. Y. , Arbadov G. , Anwar M. , Alhammadi A. , Elzeky M. , Mohammad M. A. , Hashmi M. Asef , Al Karbi H. , Alnuaimi M.
Published: November 2024

Significant operational drilling hazards and incidents while handling and running the drilling tubulars such as drops, hand injuries, line of fire and working at height, moreover the drilling tripping speed repeatedly started to comprise the drilling efficiency and the planned well delivery. The newly introduced offline bucking service and triplex catwalks technology is a progressive shift towards an efficient well construction operation by revolutionizing tubular handling as a significant improvement to maintain safe work environment, reduce the CO2 emission, well delivery optimization, rig automation, and mechanization.

The main objectives of Offline tubular management system a fit for purpose solutions were: Eliminate the operational HSE risks: Less personnel on the drill floor.Remote operationsDrill floor incident.Line of fire.Pinch points.Drops.Working at height.Support the environment and vision to the sustainability by: Reduce fuel consumption. Consequently, the emitted CO2.Consequently, emitted CO2.Improve the operations efficiency and well delivery by: Maximizing the tripping speed (running & pulling) for all tubulars (Drill string, Casing, and Tubing).Reducing the planned trip time.minimizing the flat time.Cost optimization by: Saving the cost of rig days.Saving the cost of other services at rig site.Simple to implement in all the operator's drilling rigs fleet.It doesn't affect the rig operation as the full operations are offline remotely at the ground ahead and simultaneously of the drilling activities
Unlocking the Potential of AI in Revolutionizing the Seismic-Driven Subsurface Workflows
Authors: Abubakar A. , Zhao T. , Hu W. , Lau L.
Published: November 2024

Artificial intelligence (AI), especially deep learning (DL) methods, is revolutionizing the seismic-driven subsurface workflows, enabling more accurate understanding of subsurface structures and properties. Due to the sheer size of seismic data, applying DL methods to seismic data at scale has proven to be nontrivial. In this work, with DL applications ranging from preprocessing, imaging, to enrichment, we demonstrate how AI running on high performance computation devices adds value throughout the life cycle of seismic-driven workflows. The first solution is first arrival picking that substantially reduces the total turnaround time, by minimizing the QC process that usually requires significant user interaction. The second solution is automatic salt body interpretation for seismic imaging, which uses innovative DL models and latest computation resources to eliminate the costly horizon-based manual salt interpretation. Finally, is a 2D-to-3D seismic image conversion solution, crucial for initial reconnaissance of new frontier and CCUS site screening and monitoring. Such conversion is only feasible with advanced DL workflow and GPU devices with large on-device memory. We present all three solutions with field data examples, while also discussing the importance of high-performance cloud computing infrastructure as the key enabler of such AI solutions in the subsurface domain.
Predicting Scale Depositions of Barium and Strontium Sulfates Using Novel Artificial Neural Network Algorithms
Authors: Askar Mohamed Mostafa , El Ela Mahmoud Abu , El-Banbi Ahmed H. , Sayyouh Mohamed H. M.
Published: November 2024

Numerous scale types normally deposit inside oil production wells; however, sulfate scales are probably the most alarming types due to their high strength and insolubility. Several company cases of slickline scratching and coiled tubing milling fail to clean and remove heavy depositions of barium and strontium sulfates. Observations of the current study show that these sulfate scales deposit due to cooling of super-saline formation waters inside offshore producers and pipelines, besides the mixing of incompatible waters.

Prediction of sulfate scale deposition is challenging. Many of the currently-available prediction software products have drawbacks in sulfate prediction due to the limited experimental data, the uncertainty of ion pair interactions, and the extremely-low solubility of these minerals. Therefore, more experimental work is still needed to investigate extreme field conditions and complex water chemistries.

Different Machine Learning (ML) algorithms are being used in the oil industry with successful applications that are adding and/or replacing the traditional methods. Therefore, the scope of the current study is to utilize ML algorithms in scale prediction. The study investigated actual field scale depositions that were collected from multiple offshore fields from 1998 through 2020 with more than 1400 data records. The available database contains 14 input features including water chemistry, water production rate, oil production rate, gas production rate, pressure, and temperature. Feature engineering was adopted to define the most important features to build the ANN models. The available data was split into training and testing datasets. Several Artificial Neural Network (ANN) models were developed to predict barium and strontium sulfate scales downhole in production wells. A comparative analysis was performed between the developed ANN models against a commercial scale prediction software and empirical correlations. The ANN models outperformed the other traditional methods concerning deposition probability or classification of scale type. Moreover, the ANN models could also predict the amount of scale with accuracy of 93% and 75% for strontium sulfate and barium sulfate, respectively. The Mean Absolute Error (MAE) of scale percentage was 3.6% and 8.2% for strontium sulfate and barium sulfate, respectively.

The paper novelty is the inclusion of actual scale deposits from different fields to build ANN algorithms capable of predicting the real composition of sulfate scales not detecting their super-saturation level. Predicting the type of scale leads to optimize company resources and dedicate personnel efforts to severe cases of hard scale depositions without the need of well intervention.
Nanotechnology Integration Excellence in Improved Oil Recovery: A Bumpy Road to Sustainable Future
Authors: Sergeev V.
Published: November 2024

This is a knowledge-sharing manuscript, which focuses on the methods for planning and execution of new technology integration projects in petroleum and geosystems industry. Described in the manuscript proper method of arrangement of the new technology integration projects is based on years of practicing a multidisciplinary approach to the new technology integration for improved oil recovery (IOR). This approach involves the use of knowledge of fundamentals in economics, management, marketing, law and engineering. The new technology integration in such a globally immense and inflexible industry as oil and gas exploration and production (E&P) is a challenging task. The E&P industry segment distinguishes itself from the others by its large share in global trade, capital-intensive nature, and extra-slow pace of projects for the new IOR technology integration. Obstacles to successful integration of new technology in the E&P industry are present at all scales. In the context, it may be regulatory complexities, infrastructural challenges and commodity market volatility. At the business and corporate level, it may be risk aversion, information asymmetry, corporate culture and sometimes stakeholders’ or management's skepticism. Any of these obstacles can hinder or prevent the adoption of the innovation. Therefore, even though the benefits and competitive advantages are clear, being a conceptually new technology conveys many hidden obstacles and entry barriers for integration, making this road a bumpy one. This manuscript delves into practice of the new technology integration on an example of the nanotechnology called Emulsion System with Nanoparticles (ESN) applied for subsurface flows control in petroleum and geosystems industry. Recommendations given in this paper may help both technology-developers and E&P companies overcome some of those above-mentioned obstacles and barriers. The knowledge shared in this manuscript is based on a multi-year experience of the ESN technology integration in several gigantic national oil companies and international joint ventures for oil and gas E&P in Europe, the Middle East and South-East Asia. Analysis of the accumulated international experience in the ESN technology integration helped to find common flaws in the process of pilot projects arrangements, as well as helped to develop a proper methodology for the new technology integration process. This study aims to shed light on the hidden obstacles existing nowadays and to equip decision-makers with actionable strategies to facilitate new technology integration in the global petroleum and geosystems industry.