bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Success Story of the First Application of High Performance Water Base Mud (HPWBM) in Corridor Block: Case Study in Suban Field
Authors: Permana Budi R , Widyaningsih Ratih , Hamid Fadia , Isa Boga 
Published: October 2023

Abstract
The Suban field’s gas reservoir, located at approximately 2000 mTVDSS, is composed of Pre-Tertiary rocks including Igneous and Metasediments, as well as Tertiary sedimentary rocks like LTAF (Lower Talang Akar Formation) and BRF (Baturaja Formation). Before accessing the gas reservoir, drilling activities must navigate through overburden formations, predominantly shale-based Palembang and Telisa Formations. A particular challenge encountered within the Telisa Formation is the presence of overpressure conditions.
Historically, drilling through overpressure-prone formations has posed formidable obstacles. Earlier approaches employed water-based mud (WBM) for drilling, which encountered issues such as shale instability and slow rate of penetration (ROP), necessitating the use of multiple drilling bits.
In 2012, a shift was made to Synthetic Oil-Based Mud (SOBM) to address these challenges and enhance hole stability. The outcome was not only improved hole stability but also a noteworthy surge in the rate of penetration (ROP). Consequently, SOBM applications have been employed for drilling gas-fractured reservoirs in the Suban and Sumpal regions since 2012. However, the adoption of synthetic oil-based mud (SOBM) presented its challenges, including significantly higher costs compared to water-based mud (WBM) and challenges in handling and disposing of non-environmentally friendly drilling waste associated with SOBM.
Recent developments have introduced High-Performance Water-Based Mud (HPWBM) as an alternative to Synthetic Oil-Based Mud (SOBM) for drilling. While HPWBM is not a complete replacement for SOBM, it aims to match its performance in terms of shale inhibition, wellbore stability, and rate of penetration (ROP). Additionally, HPWBM offers the potential for cost reduction and improved drilling performance.
To incorporate HPWBM into the Suban field, a meticulous technical review and analysis were conducted to align the formulation with the reservoir’s mineralogy. The implementation was initially carried out in a specific Suban well, focusing on the 12.25" hole section due to overpressure.
Key goals in designing the HPWBM included achieving superior shale inhibition, countering clay swelling and dispersion, and maintaining stability at high application temperatures. This alignment was particularly critical in the 12.25" section to minimize standpipe pressure (SPP) and Equivalent Circulating Density (ECD) and avoid formation fractures. A comprehensive laboratory testing process led to the development of 134 HPWBM formulations, with Formula 134 emerging as the optimal choice to fulfill the required objectives.
The successful implementation of HPWBM, specifically formula 134, in the Telisa Formation marked its efficacy in delivering performance on par with SOBM. This achievement not only demonstrated cost-efficiency but also streamlined and effective drilling operations.
Uncovering the Actual Safety Integrity Level (SIL) of Critical Safeguards in Aging Hydrocarbon Facilities as the Basis for the Development of a Performance-Based Maintenance Strategy of Critical Safeguards; Lessons Learned from Rokan Block
Authors: Safrudin Safrudin , Yudhy Muhammad Riandhy Anindika , Joelianto Endra 
Published: October 2023

Abstract
Equipment aging is one of the main challenges faced by companies operating oil and gas processing facilities with a brownfield engineering approach. As equipment ages, it breaks down more quickly, making it more susceptible to failure. The consequences of equipment failure can be disastrous, especially if the equipment is listed as a critical safeguard that protects the system from hazardous conditions. Failure of critical safeguards will increase the likelihood of hazardous scenarios occurring and thus impact the overall risk ranking of operating the facility. As a risk management effort, it is important to validate the safety integrity level (SIL) of the critical safeguards currently installed at the facilities and design an appropriate maintenance strategy to maintain the SIL value as originally designed. However, given the vast amount of equipment that is scattered over a large area, this is a very challenging effort.
This paper discusses a novel attempt to validate the actual critical safeguards SIL in brownfield facilities by conducting a hybrid of an extensive reliability-centered maintenance study and a regression-based machine learning model to obtain the actual equipment failure rates based on incomplete historical failure data stored at the Computerized Maintenance Management System (CMMS). A recalculation of the current SIL level and comparing the results with the original SIL design will provide information on the level of degradation that occurred and the appropriate solution.
To illustrate the application of the technique, a case study is presented based on the experience in implementing the life cycle of a safety instrumented system (SIS) according to the IEC 61508/61511 guidance as a maintenance strategy to maintain the SIL of a critical safeguard. Through these efforts, critical safeguard SILs are maintained on designed SILs and are capable of achieving credit risk reduction aimed at hazardous scenarios while setting the database for further improvement in the adoption of digital technology.
Dynamic Fracture Characterization Using Multiphase Rate Transient Analysis of Flowback and Production Data
Authors: Zhang Zhengxin , Sun Guoqing , Zhou Xingze , Dang Kaiyan , Su Xing 
Published: October 2023

Abstract
This study presented a comprehensive method for characterizing reservoir properties and hydraulic fracture (HF) closure dynamics using the Rate Transient Analysis (RTA) of flowback and production data.
The proposed method includes straight-line analysis (SLA), type-curve analysis (TCA), and model history matching (MHM), which are developed for scenarios of two-phase flow in fracture, stimulated reservoir volume (SRV), and NSRV domains. HF closure dynamics are characterized by two key parameters: pressure-dependent permeability and porosity controlled by fracture permeability-modulus and compressibility. The above techniques are combined into a generalized workflow to iteratively estimate the five parameters (four optional parameters and one fixed parameter) by reconciling data in different domains of time (single-phase water flow, two-phase flow, and hydrocarbon-dominated flow), analysis methods (SLA, TCA, MHM), and phases (water and hydrocarbon phase).
We used flowback and production data from a shale gas well in the US to verify the practicability of the method. The analysis results of the field cases confirm the good performance of the newly developed comprehensive method and verify the accuracy in estimating the static fracture properties (initial fracture pore volume and permeability) and the HF dynamic parameters using the proposed generalized workflow. The accurate prediction of the decreasing fracture permeability and porosity, fracture permeability-modulus, and compressibility demonstrates the applicability of the workflow in quantifying HF dynamics. The field application results suggest a reduction of the fracture pore volume by 30%, and a reduction of the fracture permeability by 98% for shale gas well.
Instead of a single analysis method for RTA, this paper proposed a comprehensive analysis method that includes SLA, TCA, and MHM. The interpretation results of the three analysis methods are mutually constrained, which can reduce the non-uniqueness problem of inversion. Compared with the others fracture characterization workflow that need fixed input and output parameters. This proposes general workflow not only completely characterizes the fracture closure dynamics but also can select the unknown parameters (to be determined) according to the actual scenarios of a well and the demands of reservoir engineers.
Generation of Chloralkali and Hydrogen from Produced Water
Authors: Omporkas Terath Kumar , M Basri Ana Hasrinatullina , B Md Noor Mohd Hisham 
Published: October 2023

Abstract
To flawlessly orchestrate various contaminants in any hydrocarbon production wells is an important art and at times determine business profitability margin. As introduction, contaminants can be classified into two main categories namely: soluble contaminants and insoluble contaminants. The constituents of soluble contaminants include acid gases, mercury, and organo-metallic compounds. On the other hand, insoluble contaminants are substances that do not dissolve in hydrocarbon. Examples of such contaminants include salt, emulsion, and moisture. These contaminants can cause operational difficulties such as corrosion, scaling, and reduced efficiency in production facilities hence the management of these contaminants normally requires the implementation of separation and treatment processes which later would increase the operation expenses.
It is worth emphasizing that although these contaminants are considered undesirable in hydrocarbon production process, they hold promising potential as valuable resources for other industries. A prime example is type of salt present which can be utilized as a viable input for chloralkali generation. chloralkali production encompasses the electrolysis of salt (sodium chloride) to yield sodium hypochlorite and hydrogen.
Commercialization plays a significant and intriguing role in the utilization of these contaminants. By identifying and developing business framework for the contaminants, it is really worth to consider industry partners which can turn what would otherwise be waste or disposal burdens into valuable resources. This commercialization process involves establishing common interest partnerships with other industries that can benefit from these contaminants, ensuring proper treatment and processing to meet quality standards, and creating a supply chain for the contaminants to reach their respective markets and perhaps beyond compliance to local authorities’ laws and regulation.
Before advancing to the circular economy phase, it is essential to prioritize the establishment of a sustainable foundation within the existing linear economy ecosystem. This is crucial to ensure a consistent and sustainable supply of resources for circular economy endeavors.
Machine Learning Sweet Spot Identification and Performance Validation Utilising Reservoir and Completion Data from Unconventional Reservoir in British Columbia, Canada
Authors: Leem Junghun , Musa Ikhwanul Hafizi , Mazeli Abd Hakim , Che Yusoff M Fakharuddin , Jowett David , Redpath Darcy , Saltman Peter 
Published: October 2023

Abstract
Production in an unconventional reservoir varies widely depending on reservoir characteristics (e.g., thickness, permeability, brittleness, natural fracturing), and completion design (e.g., well spacing, frac spacing, proppant volume). A comprehensive method of data analytics and predictive Machine Learning (ML) modeling was developed and deployed in the Montney unconventional siltstone gas reservoir, British Columbia, Canada to identify production zone "sweet spots" from reservoir quality data (i.e., geological, geophysical, and geomechanical) data and completion quality data (e.g., frac spacing, fluid volume, and proppant intensity), which were utilized to enhance and optimize production performance of this unconventional reservoir.
Typical data analytics and predictive ML modeling utilizes all the reservoir quality data and completion quality data together. The completion quality data tends to dominate over the reservoir quality data, because of a higher statistical correlation (i.e., weight) of the completion data to observed production. Hence, resulting predictive ML models commonly underestimate the effects of the reservoir quality on production, and exaggerate the influence of the completion quality data. To overcome these shortcomings, the reservoir quality data and the completion quality data are separated and normalized independently. The normalized reservoir and completion quality data are utilized to identify sweet spots and optimize completion design respectively, through predictive ML modelling.
This novel methodology of predictive ML modeling has identified sweet spots from key controlling reservoir quality data and as well as prescribed optimal completion designs from key controlling completion quality data. The trained predictive ML model was tested by a blind test (R2=79.0%) from 1-year of cumulative production from 6 Montney wells in the Town Pool, which was also validated by recent completions from 6 other Town Montney Pool wells (R2=78.7%).
The Evolution of Estimated Ultimate Recovery (EUR) Estimates Over the Project Life Cycle: Lessons Learnt from three Examples
Authors: Nah Wenbin , Adam Peter Alastair 
Published: October 2023

Abstract
Estimated Ultimate Recovery (EUR) of oil and gas projects is an important quantity to track over the project life to help understand and mitigate any deviations in project performance. In general, the range of uncertainty narrows over time as more data is available to constrain the estimates. However, this is not always the case. This paper presents three examples covering both gas and oil fields at different stages of their lives, which show how EUR and the corresponding range of EUR uncertainty varies over the life of each field.
The lesson from these examples is that the range of uncertainty tends to be too narrow, both in the appraisal phase, as well as in the production phase. EUR estimates may be too narrow in the appraisal phase due to the lack of available datapoints to constrain the volumetric based assumptions. In the production phase where forecasts are often based on simulation models, a limited number of geological realizations will naturally constrain the range of results from simulation, even when many realizations of lesser parameters are conducted.
In one of the examples, there was a sudden increase in the range of EUR, and the paper discusses the reasons for this, which are mostly attributed to additional data. The paper further notes the possibility of an anchoring bias underlying the results and provides multiple reasons on why EUR and Reserve estimates may be affected by this bias, such as continuity of the project team, as well as internal/external pressures.
It is thus important for technical personal, management, investors and lenders to keep an open mind, and not be pigeonholed into overly narrow ranges of uncertainty. Be brave to challenge the estimator to justify apparently narrow ranges of EUR uncertainty, as well as any suspected anchor bias in the ever-evolving EUR estimates.
Alkali-Polymer EOR Flooding in Europe: Part I - Surface: Water Softening Field Tests in Presence of Back-Produced Polymer
Authors: Marx Markus , Grillneder Roland , Lüftenegger Markus , Krenn Christof , Kornberger Martin , Hincapie Rafael Eduardo 
Published: October 2023

Abstract
Maximized depletion of mature oil reservoirs is gaining substantial importance. One of the last measures to be considered in the late life of a suitable brownfield is likely to be alkaline-polymer-EOR. OMV is currently carrying out alkaline polymer flood projects in Austria and Romania. Since AP flooding introduces significantly increased complexity, it is vital to take maximum advantage of available expertise and to not pass up any opportunity to generate additional field-based data whenever possible.
Field tests performed in the Matzen Field (Austria) were used to qualify an ion-exchange-based water softening process in saline produced water and in the presence of back-produced partially hydrolyzed polymer (HPAM). A stringent goal was set to reduce the divalent cation concentration in produced water to less than 5mg/l at the test location. Evaluations were thoroughly performed under a broad variety of operating conditions and water characteristics. A thorough analysis and evaluation was performed on the impacts of various concentrations of oil and polymer or altered viscosities, as well as modified operating conditions such as flux rates.
Observations describe different mechanisms related to water salinity and polymer-induced viscosity, as well as the way in which they affect the efficiency of the process. The WAC ion exchanger unit was optimized and adjusted to ensure compliance with the targeted divalent cation concentration in the presence of more than 400ppm of back-produced HPAM and more than 23 g/l of TDS content. Under these conditions, optimum operating conditions and risk mitigation measures were identified in order to achieve the best unit availability and service time between regeneration cycles. Last of all, an economic outlook was created in terms of expected chemical consumption for ion exchanger operation in the presence of back-produced polymer.
The outcomes of this field trial provided crucial insights into operational aspects, allowed significant risk reduction, and represented the basis for designing upscaled water softening systems which were implemented in alkaline polymer flood projects in OMV assets.
Top 8 Digital Safety Trends in Oil and Gas in 2023
Authors: Daher Elie 
Published: October 2023

Abstract
Top 8 digital safety trends in Oil and Gas in 2023
Oil and Gas is one of the most hazardous industries to work in. From exposure to toxic gases to working at dangerous temperatures, lone worker management to fall detection, there are a host of safety issues that are common to the industry. It is also an industry undergoing a revolutionary digital transformation. With changes, come challenges and new opportunities. This paper looks at the top digital safety trends that are taking place within the industry.
The top trends that will be investigated in this paper include: Real time safety solutions (RTLS): Real-time location services have re-defined personnel monitoring. From real time access control to personnel monitoring, geo-fencing to vehicle collision avoidance, real-time location tracking has a wide range of applications that can be used to make the industry safer.Remote monitoring: Remote monitoring technologies are one of the most adopted digital technologies used by Oil and Gas companies around the world. From a safety perspective, remote monitoring has changed the game for confined space monitoring. Gas monitors, breathing air apparatus can all now be monitored remotely reducing the number of onsite personnel required contributing to a safer and more efficient worksite.Connected worker solutions: Networked devices are transforming lone workers to connected workers. Through a comprehensive solution of apps, industrial IIoT infrastructure and connected devices, workers can now directly communicate and connect with the command center ensuring enhanced safety for lone workers.Smarter equipment: Equipment, devices and tools are also getting smarter. All traditional equipment can be ‘smarter’ by using data, AI and IIoT technology to work with each other to revolutionize their role in safety.AI solutions: AI is being increasingly used in Oil and Gas for a range of safety solutions. Computer Vision for example has a versatile range of applications that ensure safer workers. From PPE compliance to fall detection, Computer Vision and other AI technologies can be used to ensure safer Oil and Gas workers.Automation of hazardous and high-risk jobs: Robots and drones have been taking over high-risk jobs to keep workers safe.Training in the digital era: Better trained workers are safer workers. With the rapid rise in digital technologies, there are a growing number of digital and hybrid training and coaching sessions that are becoming more popular. AI is also being increasingly used in training to ensure a highly trained workforce.Digital twins: More organizations are investing in digital twins to detect, predict and even help prevent incidents from happening in the real world by better understanding their impacts in the digital world by gaining a clearer comprehension of their consequences in the digital sphere.
AI Innovations in Waterflood Management, The Path to Autonomous Operations
Authors: Khataniar Sanjoy Kumar , Biniwale Shripad Suhas , Elfeel Mohamed Ahmed , Tellez Camilo Ernesto 
Published: October 2023

AbstractIn the S-Field operations office, a daily battle ensues in the quest to increase production and maximize profits from waterflooding. One of the main control mechanisms applied to optimize the waterflooded reservoirs is by controlling the water injection and pumping rates of producers to balance patterns, maximize sweep, and maintain reservoir pressure. The reservoir surveillance team has been using a simple spreadsheet analytical approach that was quite limiting as the number of injection patterns increased, and the flood matured, leading to a water breakthrough. There was a need for a more sophisticated approach that could leverage artificial intelligence (AI) technology, especially since the entire asset was undergoing significant digitalization of its operations.This paper presents various innovations in bringing real applications of AI for waterflood management. This includes innovations in business processes, application of design thinking methodology, agile development, and AI. The AI waterflood management solution combines cloud technologies, big data processing, data analytics, machine learning algorithms, robotics, sensors and monitoring system, automation, edge gateways, and augmented and virtual reality (AR/VR).Design thinking principles and a human-centric approach within an agile innovation framework were utilized for rapid prototyping and deployment. A waterflood management framework that addressed the business's operational, tactical, and strategic aspects created the backdrop for designing the solution architecture. New injector-producer modeling techniques that leveraged AI and were fit-for-purpose for reservoir surveillance and production engineers were prototyped. An interactive pattern flood management tool, adapted from streamline simulation-based waterflood analysis methods, was developed for injection pattern analysis and intelligent optimization workflow.Field pilot testing for over a year proved that the prototype could reliably detect injector-production interactions and recommend operating set points in relevant time. Reduced time to decision, improved analysis efficiency and reliability of short-term forecasts, reduced field visits and health-safety-environment (HSE) exposure, and finally ease-of-use has been experienced. The learnings from this project are being leveraged to develop a deployable solution and move the needle toward autonomous waterflood operations.