bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unlocking Conditioning Monitoring Platform Through Implementation Digital Enablement Platform to Ensure Equipment Availability and Flawless Operations
Authors: Zuhruffani Risnu , Rahman Faisol Amri , Rokhmad Iqbal Faishal 
Published: October 2023

Abstract
Equipment health and monitoring platform is critical to ensure condition monitoring program for rotating machinery equipment which generates flow energy to transfer hydrocarbons for production and well injection, generates power for processing plant needs and waste disposal purposes. EHMS platform has been implemented in EMCL as part of machinery protection system and not being used to mitigate risk of some bad actors.
Unlocking EHMS platform that has capability to provide Early warning system with features avoid unexpected downtime, continuous vibration analysis, automatically notify users of abnormal condition, and Failure identification with features such as Vibration Trend, Axial Position, Phase Trend, Frequency Spectrum, Orbit + Time Waveform, Probe Gap Voltage and Shaft Centerline while steady state. Furthermore, EHMS also provide analysis in transient State with Polar Plots, Bode Plots, Cascade Plot and Spectrum Waterfall. It has challenges to enable accessibility on the EHMS platform in real time is challenging for EMCL, because the dynamic vibration data has high frequency of data (more than &gt;1HZ), so it caused analysis and workflow only can be done thru those platforms cannot be done thru existing OPC data communication. Meanwhile the platform only available within isolated environment in Industrial control system, as we may know exposing those environments is very restricted due to some requirement and has cyber risk.
It does require strategic technology architecture deployment at scale that enable secured remote capability with compliance with IEC 62443, complex mitigation is implemented, put control point in place is needed to manage the risk and business value creation after implementation.
The architecture and design should be able to accommodate expansion of future control system topology that able to capture another operations process automation.
With implementation of digital enablement platform has provided benefit for conditioning monitoring technique to identify early failure indication through spectrum analysis in regular basis, detect for any misalignment thru band-alarm enablement, provide accurate and deep analysis while executing during on-site balancing on GTG, and minimized downtime in injection compressor to reduce flare rate.
Probabilistic Study on Gaslift Compressor Mapping with Network Modelling - How to Stabilize Gaslift Distribution and Optimise Overall Production with the Optimum Gaslift Compressor Combination?
Authors: Polycarp Maurice Anak , Chaw Kit Teng 
Published: October 2023

Abstract
Field X is a mature brownfield with aging facilities and relies heavily on gas lift as artificial lift. Field X runs gas lift compressors with a 4+1 operating philosophy. However, significant unplanned deferment up to 12% is associated with the gas lift compressors due to frequent tripping events (cascaded from high suction pressure). Historical data also shows that certain combinations of the gas lift compressors are more sustainable for better uptime and result in better overall production. This study aims to utilize GAP to execute a probabilistic study to investigate the relationship between compressor combination and the spread of gas suction across the different units, and to develop ideal compressor combinations for Operation's reference.
GAP is used to model the process layout of the five gas lift compressor units. Next, multiple sets/permutations of suction pressure configurations are used to run network solving iterations to estimate gas suction into each compressor. This is done for three scenarios i.e., when 2/3/4 gas lift compressors are operating concurrently. Subsequently, comparison is done on the standard deviation of each set of data (gas distribution across units) for each combination. This can also be done in graphical format via plotting of normal distribution graphs. For each scenario, the aim is to identify which compressor combinations result in relatively lower standard deviation, which means relatively uniform gas distribution across units and lower chances of unit tripping due to high suction pressure.
This study has developed a ranking of gas lift compressor combinations, subject to the number of units operating concurrently. This ranking is cascaded to Operation for reference when operating the compressors. This ranking is also mapped against production data (historical and moving forward). It shows that the combinations, with lower standard deviation outcome from the probabilistic study, do result in more stable gross production and water cut. Additionally, higher-ranked compressor combinations also demonstrate healthier discharge pressure (&gt;650 psig) compared to that of lower-ranked (600 psig). Comparison of field production data, between periods of higher-ranked compressor combination and those of lower-ranked, shows instantaneous gain of 1000 bopd. This is due to higher GLC discharge pressure, higher compressor uptime and better stability.
This paper will describe the integration between subsurface and surface to identify the optimum gas lift compressor combination to deliver a sustainable gas lift supply, and to ensure higher uptime of the compressors, while optimizing overall production. This analysis will be coupled with subsurface understanding on well line-up optimization to maximize gain.
A Surrogate Model for Numerical Reservoir Simulation of CO2 Flooding and Storage Based on Deep Learning
Authors: Dong Peng , Liao Xinwei , Zhang Lingfeng , Zhang Heng , Zhao Xurong , Xue Qishan 
Published: October 2023

Abstract
Numerical simulation is an important tool for CO2 flooding and storage simulation, which allows to obtain global approximate solutions of governing equation. However, the simulations often suffer from significant computational costs and convergence problems, especially considering the pseudo-component and CO2 storage mechanisms. This makes the scheme optimization tedious. Therefore, we propose a deep learning-based surrogate model to efficiently implement numerical simulation of CO2-flooding and storage. Proposed method consists of automatic encoder and prediction part. The auto-encoder consists of VQ-VAE model, which projects the reservoir's 3D properties into 2D space. The prediction part consists of ConvLSTM models, which accepts reservoir variables. Finally, the surrogate model outputs the dynamic characteristics of production and different CO2 storage forms. The results show that the original reservoir properties can be restored with high fidelity after autoencoder training. The correlation coefficient between the decoded attribute and the original attribute is greater than 0.98. For prediction part, ConvLSTM can accurately predict the dynamic characteristics of production and different CO2 storage forms. The average relative errors of the predictions in the training and validation sets were 4.37% as well as 8.91%. In addition, for computational efficiency, the surrogate model is two orders of magnitude faster than the numerical model. It is proved that the surrogate model can effectively replace the numerical simulation model and greatly improve the computational efficiency.
An Industry Game Changer Approach in Managing Medium-High Complexity of Well via Robust Risk & Reward Contract Strategy – A Win-Win Concept for Both Operators & Service Providers
Authors: Mohd Mokhtar Mohd Khaidir Bin , A Kadir Norazan Bin , Hamzah Nurul Ezalina Binti , Xiong Choi Yee , Yahaya Fadzil Bin , W Othman W M Safwan Bin , Md Saleh Siti Noor Asheiah Binti , Leong Chua Hing , Khairul Anuar Khairina Binti , Amsidom Amirul Adha Bin , Harun Raihan Bin , Nordin Azri Bin 
Published: October 2023

Abstract
The efficient and successful execution of oil and gas well projects are critical for companies operating in this industry. However, the conventional contract model, where operators company bear the entire economic and operational risk, has proven to be a deterrent in executing medium to high complexity wells. This manuscript explores a risk & reward contract strategy as an alternative approach to overcome the limitations of conventional contracts. The risk & reward model promotes risk sharing between the operator company and service providers, offering incentives for successful operations while mitigating the impact of failures. This manuscript discusses the benefits, challenges, and implementation considerations associated with adopting risk & reward contracts in the oil and gas industry.
Reservoir Management Integration of Asset Development and Asset Optimization Aspect in Managing DSF Field New Area Steam Flood Project
Authors: Nuruddin Mukhammad , Aji Muhamad , Pradana Candra Arya , Raharja Sulastama 
Published: October 2023

Abstract
DSF Area 13 is the newest steam flood project that has developed about 800 acres area, utilizing over 350 oil producers and 145 steam injectors at initial development. Steam injection optimization is quite challenging due to poor lateral connectivity and lower reservoir quality; hence the area was behaving with poor steam flood response. A reservoir management integration and effort are introduced in 2021 to improve overall area steam flood performance.
Method includes continues improvement on the Reservoir Management Strategy which integrates new development and optimization of the current asset. The integrated effort consists of geological new conceptual model development, new development well strategy, injection strategy updates, and drainage process improvement at existing wells. Those efforts are grounded by steam flood life cycle knowledge in gravity drainage model and aimed to improve steam chest progression which was slow before due to poor reservoir. The uniqueness of the area demands those efforts to be dependently executed and requires holistic reservoir management.
A geological new conceptual model reveals that the area has various geological characteristics which correspond to production-injection performance. New infill wells show massive bypass oil which is not expected at 7+ years steam flooded area. It also enables steam injection optimization by improving pressure depletion and lateral connectivity. It impacts faster heat maturity and furthermore will enable faster heat management and faster oil recovery simultaneously. Injection strategy update results to three difference approaches of steam injection. Conventional steam-increased strategy is applied to medium connectivity pattern and is aimed to accelerate breakthrough time. Revisiting injection is applied at high pressure pattern and will be executing through staging approach. Theoretical heat requirement is re-evaluated at pattern which has new infill wells to achieve production-injection alignment. Existing producer well is optimized by performing pump optimization, acidizing job campaign, and steam cyclic stimulation resulting in good production response and impact to improved reservoir pressure depletion in the high-pressure pattern. As a result, in area level, oil production shows an inclining trend which is not expected from the last 2-3 years’ forecast, adding more than 7.7 MMSTB reserves from previous year's booking.
This paper explains all efforts related to reservoir management to improve the performance of this new steam flood project from both optimization and development aspect. It explains how to demonstrate reservoir management at particular asset and ultimately to turn a poor steam flood area into an inclining trend. Hopefully it can be adopted to another area/ field which is experiencing the same poor steam flood performance.
Advancing Well and Remote Compressor Performance Through Data Analytics
Authors: Kongkiatpaiboon Suradech , Laosuwan Sarita , Tangsawanich Yuranan , Sopitviriyaporn Chain , Parimontonsakul Monthep , Kaivalkritiyakul Polake , Payaksiri Siriwan 
Published: October 2023

Abstract
Optimizing wells and remote compressors for operations can be time-consuming and hard work. Engineers and operation team struggle to uncover system bottlenecks and modify operating parameters. To automate the traditional optimization procedure, a novel data analytics workflow was applied alongside compressor simulation data. In this research, an innovative framework for optimizing facilities equipment has been adopted in this project to increase the value of assets via increased productivity.
The results of thousands of compressor simulation runs have been exported to a training database. The system's operational data in real-time has been mapped to simulation findings using a data science model. Optimization workflow for different kinds of problems has been speculated up and made. This process is integrated using cloud service and machine learning deployment environment to generate a database of recommendations. The bottlenecks in the system are then visualized using an interactive dashboard, which acts to eliminate the bottlenecks during normal system operation. With an R2 greater than 0.99, the data science model matches the simulation findings well. After a few sessions of brainstorming with specialists, the optimization method covers practically all operational restrictions. This environment is being used to deliver daily analysis and recommendations to the operations and engineering teams.
With the new framework, the traditional well and compressor optimization process has been transformed to a cutting-edge digital solution. The time required to locate wells and compressor bottlenecks has been significantly reduced. Opportunities that were previously not visible owing to inadequate time for workers to look at in the old approach have now been captured, and offshore adjustments have been completed much quicker than the prior approach. As a consequence, this initiative has the potential to provide large productivity gains. Furthermore, the analytical findings will serve as the basis for following facility optimization. Problems with wells and compressor packages will be identified as system constraints and used as input characteristics to anticipate downstream equipment failure or analyze troublesome wells to choke down. This project will provide a data foundation for other topside facilities in the future.
In summary, this study has pioneered a new, automated approach by bringing together operation, machinery domain, and data science experts, which allows for delivery of effective bottleneck identification and recommendation to enhance well and compressor performance. In the future, this will serve as the basis for an automated workflow and a digital twin of the facilities, both of which will improve the oil and gas industry's ability to manage the efficiency of its wells and compressors.
Environmentally Friendly CO2 Resistance Cement for Carbon Capture Storage Project: First Deployment in Offshore Malaysia
Authors: Riyanto Latief , Kumar Avinash Kishore , Shahril Muhammad Afiq , Lau Chee Hen , Sazali Yon Azwa , Amir Muhammad Syafeeq Ebining , Jain Bipin , Kapoor Saurabh , Yakovlev Andrey , Duong Anh Tuan , Chong Woon Phin , Medvedev Anatoly 
Published: October 2023

Abstract
Geopolymer cement technology was developed as alternative cement system that can withstand harsh acidic environment normally encountered in Carbon Capture Storage (CCS) fields or high CO2 gas fields. It is also environmentally friendly technology as the processes converts fly ash (waste from coal-fired power plant) into construction material. Following a successful yard test in 2021, it was agreed to validate the new technology by piloting in offshore well. The objective is to check the operational feasibility in offshore environment. The paper aims to describe the whole process starting from candidate screening, job design, operation, and evaluation of the pilot.
Ocean Sheen Caused by Complex of Solids, Micro Asphalts, and Iron - Investigation and Solution
Authors: Franca Leonardo , Fakunle Daniel , Da Silva Danilo 
Published: October 2023

Abstract
This work was done to propose a list of alternative water clarifiers. Discoloration (Sheen) was observed in sea water due to impact of overboard produced water quality and one of the causes is the type of clarifier being implemented (i.e., DTC - Dithiocarbamate), which will be replaced to a non-DTC. After this new chemical is implemented, a reassessment of the overboard produced water quality will be carried out to understand if there is the need to improve this water quality.
The included the following requirements as:
Improve the visual water aspect (ocean surface). Improve the water quality. Root cause investigation.
The potential causes of the sea water discoloration include:
Iron oxidation which is iron exposed to oxygen in the sea surface (20 to 50 ppm of iron is presented in the produced water going to the sea). Poor chemical treatment performance. Presence of Water-Soluble Organics (WSO). Presence of solids.
At the conclusion of the water clarification product selection test, the following observations were made:
A combination of 20 ppm of Coagulant A followed by 20 ppm of Flocculant D performed excellent, reducing the oil in water content from 36 ppm to 2.0 ppm after five minutes of residence time but with instantaneous action and separation (oil/solids and water). This combination was the best/first choice. A combination of 40 ppm of Water Clarifier C followed by 20 ppm of Flocculant D reduced the oil in water content from 36 ppm to 7.0 ppm after five minutes of residence time. 100ppm of Scale Remover E or Scale Remover F to treat Slop water. This recommendation helps to improve flow back from Slop tanks or cargo tanks with water from those vessels. Usually Slop water present many solids and iron content. Both products were tested against a Slop water/produced water combined.
Multiphase Flow Diagnostics for Evaluating Gas Shut-Off Performance to Minimize Carbon Footprint
Authors: Ravichandran Tenamutha , Sidek Sulaiman , Giniyatullin Artur , Abdul Latif Azzad Hafiz 
Published: October 2023

Abstract
This paper discusses the results of a flow diagnostics survey conducted to evaluate the performance of a Gas Shut-Off (GSO) campaign in Field A. The field faced challenges associated with a high Gas-Oil-Ratio (GOR) production, leading to excessive carbon emissions, reduced field production efficiency, and elevated carbon-per-barrel rates. Additionally, the presence of excess gas not only consumed valuable pipeline ullage but also caused transportation and flow assurance issues. Total field gas production had already exceeded the limit of surface processing facilities, which meant the Operator had to choke back production. Managing and processing excess gas in Field A requires substantial effort and capital expenditure for surface infrastructure and facilities.
The GSO campaign involved the installation of a 200 ft long 2-7/8 in. tubing straddle to effectively isolate the zones with the highest gas contribution. The identified well, DW#2, was a 7 in. × 4-1/2 in. cemented monobore across several commingled oil and gas-bearing zones. The well consists of five (5) clastic reservoir zones, each with multiple layers, all of which are perforated over an extensive interval exceeding 1,500 ft. This configuration poses significant challenges in accurately determining production allocation, addressing flow assurance issues, understanding reservoir connectivity, monitoring well performance, and forecasting total field production. In addition, the high degree of complexity meant that standard production logging methods could not accurately characterize flow or confidently distinguish the main gas-contributing layers.
Through the flow diagnostics survey, the paper evaluates the success of the GSO campaign in mitigating the high GOR production challenges and improving the field's performance. This paper aims to contribute to the broader understanding of managing excessive gas production in complex reservoir systems while striving to achieve enhanced field productivity and environmental responsibility.
Basic Sediment and Water (BSW) Artificial Intelligence Integration in Heavy Oil Steam Flood Central Gathering Station Facilities Through Machine Learning Regression Model Implementation
Authors: Jati Pandu Prabowo , Santoso Ade Anggi Naluriawan , Sani Ahmad Nurkholis , Kumar Muhammad Phanterektus 
Published: October 2023

Abstract
The Delta field, once at its peak production in 1992 with 290,000 barrels of oil per day and a daily steam flood injection of 1 million barrels, is now experiencing a significant decline in production. Operational challenges persist and are compounded by the complex integrated facility system in the Delta Field. These challenges include production measurement, treatment, steam generation, steam distribution injection, water disposal, and injection network. Dynamic operations involving steam distribution and well stimulation activities lead to fluctuations and changes in temperature-fluid characteristics. Moreover, the treatment of heavy oil from the Delta Field is difficult due to its high viscosity and the presence of emulsion stabilizers such as asphaltenes, organic acids, and generated sand.
Managing BSW (Basic Sediment & Water) has become increasingly challenging due to the complexity of operations and the extended production period of over 30 years since peak production. To address this, a BSW prediction tool was developed, utilizing existing data infrastructure to enhance processing facilities’ ability to manage BSW more effectively compared to previous practices. Previously, operators could only respond to increased BSW levels without an online prediction tool. The developed tool utilizes AI (Artificial Intelligence) and Data Science (DS) models to predict BS&W values with a remarkable R2 value of 0.945. However, challenges arise during data integration and preparation, including data supply for model training, data acquisition techniques, and measurement capabilities. Fortunately, the final DS/AI Model can provide an early warning up to 2 days before potential BSW upsets, improving safety performance in treating high-risk steam systems by enabling prompt decision-making, reducing errors, and increasing efficiency. As a result, the average percentage of BSW upset cases per year has significantly decreased from 15.08% to 1.05%. Overall, the application of this technology has led to improved management of BS&W.
In this paper, we will present an overview of recent collaborative initiatives that utilize data science and machine learning-artificial intelligence as an alternative solution to address the challenges related to BSW quality in the crude oil process separation of Indonesia's steam flood operation in the Delta Field. The project team's findings suggest that the adoption of DS-AI holds great promise in unlocking additional optimization opportunities in oil and gas processing, capitalizing on the vast and expanding dataset available.