bluuebunny/crossref_metadata_2025_split huggingface dataset:
Large-Scale High-Throughput Sensitivity Analysis of CO2 MMP to Optimize Gas Injection EOR Processes
Authors: Abedini A. , Qi Z. , Haas T. de , O'Brian D. 
Published: October 2023

Abstract
Performance of CO2 injection relies on accurate CO2 MMP/miscibility data at reservoir conditions. The CO2 stream typically contains gas impurities, and in most cases CO2 is combined with recycled gasses, which in either case the MMP changes – imposing additional uncertainties to deviate from expected CO2-MMP. Slim-tube is the most reliable tool; however, it is very time- and capital-intensive, making it impossible to provide high-throughput data to assess the impact of other gasses. Throughout a case study, we present a very efficient microfluidic platform to measure high-quality MMP data of CO2 with various impurities significantly faster and easier. In this work, a microfluidic platform was designed and used to determine the MMP/miscibility condition of CO2 in pure state and with several impurities (i.e., hydrocarbon mixtures, CH4, H2S, H2, N2, Ar) for an EOR operation in a depleted reservoir. 18 miscibility tests were conducted over ∼4 weeks to provide detailed data on how the MMP or miscibility of CO2 changes due to gas impurities – possibly the largest and fastest empirical study of MMP sensitivity ever. A high-resolution fluorescence microscopy along with an automated image analysis algorithm were employed to assess the miscibility condition. The MMP of a few gasses were also measured using the slim-tube to verify the validity of the microfluidic measurements, showing a tight agreement between the data. The results have demonstrated a reliable, accurate, and quick method to conduct a thorough CO2-MMP sensitivity analysis for gas injection processes. While each impurity may have a clear impact on the MMP, either in an increasing or decreasing manner, the interconnection between multiple impurities is generally unknown and differs as a function of impurity composition and reservoir conditions. The outcome of this work, eventually, gave a roadmap to provide a boundary of a miscible zone, in which the level of impurities is acceptable and not adversely affecting miscibility performance of injection, and while beyond this boundary, the impurities may negatively impact the recovery from performance of gas injection by increasing the MMP above the current reservoir pressure. Given the very small volume of oil sample, easier operations, and faster run-time required for this microfluidic approach, the miscibility/MMP study of a testing oil with various gas compositions can be determined in days – not obtainable with the slim-tube approach. The microfluidic platform utilized here provides accurate and quick gas injection related miscibility information, that can potentially open a new opportunity to better develop the current resources, improve the production efficacy, and mitigate uncertainties associated with gas injection plannings and operations. The benefits can be further extended for facility design, regulatory requirements, land acquisition strategy, workflow modifications, and reserve estimates.
Effects of Pressure on Oil Thermal Cracking: Insights on the Data from High-Pressure Optical Cell (HPOC) Experiments
Authors: Chen Yanyan , Tao Shizhen , Ma Ning , Li Yahui , Tao Xiaowan , Gao Jianrong , Lv Wanjun , Wei Lin 
Published: October 2023

Abstract
The role of pressure in the thermal cracking of crude oil are still under debate. In this study, a novel method employing high-pressure optical cell (HPOC) and fluorescence spectrometry was used to in situ monitor the thermal cracking of oil under elevated temperatures and pressures, and investigate the effects of pressure. Specifically, two HPOC cells loaded with crude oil samples were simultaneously heated on the same heating-cooling stage but at different pressures (i.e. 30 MPa and 100 MPa). Changes in the attributes of pyrolytic products (i.e. physical appearance, fluorescent colors and intensity, etc.) were real-time recorded. The fluorescence spectra were measured every half hour to semi-quantify the chemical evolution of pyrolytic products.
The results showed that the fluorescent color of pyrolytic products generated at 100 MPa evolved from blue to greenish yellow as heating, and finally turned to yellow at the end of pyrolysis experiments. In contrast, the fluorescent color in the 30 MPa cell finally turned to deep blue, instead of yellow, after 12 hours’ heating. A light blue bubble of primarily light, gaseous cracking products was observed after 5 hours’ heating at 30 MPa, whose volume expanded and finally filled up the whole cell. In contrast, no such bubbles were observed in the 100 MPa cell.
The λmax values derived from fluorescence spectra shifted initially toward longer but later toward shorter wavelengths in the 30 MPa cell (i.e. an initial redshift followed by a later blueshift), distinctly contrasting the evolutional pattern of monotonically shifting of λmax toward longer wavelengths as observed at 100 MPa (i.e. a constant redshift). The red-green quotient (Q =I650/I550) based on the λmax values can be used to semi-quantify the thermal maturity and stability of chemical substitute. The smaller Q values in the 30 MPa cell clearly suggested that the chemical compounds in the 30 MPa held higher thermal maturity and probably subject to a more intense thermal cracking.
The distinction between optical, physical, and chemical properties of pyrolytic products at different pressures are direct evidence proving that high pressure can hinder the thermal cracking of oil to gases. Therefore, the burial depth range at which petroleum can still remain as liquid phase might be extended at high pressure, which is vital for the establishment of exploitation strategies of deep reservoirs a high pressure.
Multi-Scale Sedimentary Forward Reservoir Modelling: A Disruptive Solution for Simulating Heterogeneity in Carbonates. Application to the Kharaib-2 Reservoir Unit
Authors: Massonnat G.J. , Michel Julien , Gatel Pierre , Ruiu Jérémy , Danquigny Charles , Lesueur Jean-Louis , Jean-Borgomano  
Published: October 2023

In mature hydrocarbon provinces, the intense operational activity on each field to monitor production through drilling and work-over operations, as well as the continuous updating of the reservoir models that drive these operations, leads each asset to concentrate its efforts on work at the scale of the field for which it is responsible. This focus is consistent with both data acquisition, which is targeted on the zones of interest, and with the restrictive use of this data prior to the modelling phase. By giving priority to statistics (Vertical Proportion Curves, occurrence tables between facies and rock-types, distributions of petrophysical variables, etc.), data analysis before modelling favors an understanding of reservoir heterogeneity at the scale of the field, to the detriment of a more regional understanding. Reservoir modeling techniques, which to date are essentially geostatistical, also maintain this focus on data rather than knowledge. By failing to challenge the relevance and the consistency of the geological concept used to apply interpolation techniques, the geostatistics-based modelling process deprives the user of the opportunity to validate or invalidate a number of assumptions that are nonetheless crucial in terms of model outcomes (Borgomano et al, 2020).
On the other hand, effectively integrating at the regional scale the stream of data coming from the different fields would require a common framework and unifying concepts. In vast regions such as the Middle East, the size and density of wells in each field relative to the distance between fields makes it tricky to create this truly integrative framework. Synthetic or a selection of representative logs are thus used to build regional models. The distances between fields larger than the range of any variogram make also geological processes the only possible unifying concepts. Thus, geological models should be built by simulating the geological processes at the regional level and including several fields, in such a way as to be consistent in terms of geology, with little dependence on available data, and with a variable resolution adapted to needs and constraints. In the absence of such models, forecasting in prospective areas is always uncertain, even during intensive exploration.
One of the major challenges of geological modelling is currently to test and validate at the regional scale the concepts used at the reservoir scale, to capitalize in regional models the detailed knowledge gathered in the fields, and to bring into reservoir models the regional understanding after validation. The disruptive approach presented in this paper addresses this issue, with a first application on the Kharaib-2, a carbonate reservoir unit from the Middle East.
Short-Term Production Optimization in a Time-Dependent Parameters Scenario
Authors: Parravicini M. , Montini M. , Vignati E. 
Published: October 2023

Abstract
Although the pandemic has slowed the entire world for some years, the energy demand is nonetheless set to increase significantly in the coming years, mainly driven by the expectation of an increase in prosperity in the developing world. Renewable energy is expected to play a major role as the main source of energy. Nevertheless, oil and gas production is supposed to remain a key player in the energy scenario with natural gas consumption constantly increasing and oil consumption peaking in 2030.
Lately, the world has found itself in the midst of its first global energy crisis - a shock of unprecedented breadth and complexity triggered by Russia's invasion of Ukraine following rapid economic recovery from the pandemic. This uncertain scenario caused oil and especially gas prices to skyrocket in 2022, reaching levels never seen before and characterizing the whole year as a year of record profits for most of the oil and gas players across the globe. This, in turn, caused a oil and gas upstream capital expenditures increased by 39% in 2022, the highest level since 2014.
However, estimates say that additional investment are still needed to ensure adequate supplies in the future, investments that are still uncertain to be granted at the horizon.
Transformation of Operations Through Digital Twin Application
Authors: Almessabi Ahmed , Al-saedi Khaled , U Ddin Muhammad , Al Naqbi Mohammed 
Published: October 2023

Abstract
The world is now witnessing a fourth industrial revolution with the rise of artificial intelligence and Digital Twin technology. The Oil and Gas industry, a pillar of global energy supply, has always been driven by cutting-edge technologies and innovative solutions to meet the ever-increasing global demand. In the quest for operational excellence and heightened productivity, O&G companies are now turning their attention to a revolutionary concept - Digital Twins.
Emerging as a significant component of industry 4.0, Digital Twin technology offers a powerful and transformative approach that holds immense potential for large scale organizations. The Oil and Gas Industry is one of many industries that are currently adopting this new technology to improve their safety, operational efficiency, production and decision-making measures.
When considering the fact that concepts such as data acquisition and modeling, visualization, simulation, real time monitoring and predictive control are not new to the Oil & Gas industry, leveraging digital twin technology seems like the natural step forward.
Analytical Method and Approach on Early Warning Indicators for Enterprise Capital Project Management
Authors: Baharom Ts-Muhammad Azri Ahmad , Abdullah Asrul Sani 
Published: October 2023

Executive Summary
Effective project monitoring and control are extremely critical for the successful delivery of projects, especially for a Project Management Office (PMO) overseeing a significant numbers of capital projects. Applying Early Warnings as part of the process allows PMO at enterprise level to identify projects with emerging risks before performance thresholds are breached, allowing for proactive mitigations, optimization of resources and preservation of value. This paper discusses the Early Warnings concept, followed by descriptions of the analytical approaches and methods used during the development of Project Early Warning Indicators (PEWI) system in PETRONAS for Enterprise Capital Project Management. Finally, the paper discusses the challenges and advantages as well as the opportunities for further improvements.
Drilled Dry Structures – From Yesterday's Failure to Today's Success
Authors: Das Debasis Priyadarshan , Tiwari Pankaj Kumar , Chidambaram Prasanna , Amir M Syafeeq B E , Tewari Raj Deo , Bt Baharuddin Salina 
Published: October 2023

Abstract
CO2 Storage in deep saline aquifers has potential and small-scale projects have been successful globally while some major projects like Gorgon are in their early phases. Several depleted hydrocarbon fields have been studied in Peninsular Malaysia for potential storage of ~480 to 570 MMscf of CO2 which will be produced daily once high contaminant gas fields will be brought into production. However, due to the limited storage capacity and late availability of such depleted reservoirs, it is necessary to investigate options that might address both storage capacity and timely availability for sequestering the produced CO2. Peninsular Malaysia's deep saline aquifers have the potential not only to address CO2 production in the region, but also to serve as regional CO2 sequestration hubs.
With a holistic approach, a screening matrix was developed to identify the strategic drilled dry structures or saline aquifers. It comprised fault density, presence of top seal(s), reservoir depth, thickness and extension, pressure, temperature, porosity, number of wells drilled & their integrity, and data availability. Structures that met the screening criteria were further analyzed for their CO2 storage capacity based on reservoir pore volume, producible water and CO2 compression factor. They were subsequently ranked based on the evaluated capacities and associated containment & well-integrity risks. Further the feasibility of clustering multiple dry structures (within a ~20km radius) was evaluated for nodal development.
Six potential candidates were discovered from the thirty-four screened drilled dry structures, with individual structures having theoretical storage capacities ranging from 0.5 to 23 Trillion Standard Cubic Feet (Tscf). Two possible clusters were also identified: with Cluster 1, comprising three structures with a potential storage volume range of 1.1 to 1.8 Tscf, and Cluster 2 comprising two structures with a storage capacity ranging between 1.9 to 7.2 Tscf. The study also emphasized the need for more detailed to establish the feasibility of identified storage sites. To date, detailed feasibility studies are being conducted on all these structures to narrow down the range of the storage capacities and to come up with an appraisal plan for additional data acquisition to reduce the uncertainties before these structures are taken up for Storage Development Planning studies.
This is the first-ever screening & evaluation of drilled dry structures/saline aquifers for CO2 storage in the Peninsular Malaysia area. These identified saline aquifers have significant CO2 storage potential that can help unlock the hydrocarbon potential in the high contaminant gas fields in the region on time and cater to the regional CO2 management in Southeast Asia.
Transforming Operations Data into Business Intelligence: Leveraging Natural Language Processing (NLP) and Machine Learning (ML) for Accurate and Sustainable Insights
Authors: Mukherjee S. , Shekhar S. , Martinez S. S. , Gupta A. K. , Kumar S. , Karrar A. Z. 
Published: October 2023

Abstract
Unstructured data constitutes a sizable portion of every organization. When left unattended, it becomes dark redundant data with associated storage costs. This paper introduces a novel approach to information extraction for data management in the oil and gas industry. The data management system (DMS) is a web application that seamlessly integrates structured and unstructured data at the back end, providing actionable insights to users on the front-end. The DMS offers intuitive and interactive features tailored for diverse use cases, including operations planning, sales planning, tender planning, and competition-market share analysis.
The approach consists of two key components: automated extraction and enrichment pipelines, and workflows based on natural language processing (NLP) and machine learning (ML). The automated pipelines efficiently extract, ingest, enrich, and quality-check data from multiple sources. Meanwhile, the NLP workflows focus on unstructured textual data and perform tasks such as named entity recognition (NER), relation extraction, and operation classification to extract relevant information. State-of-the-art language models, such as bidirectional encoder representations from transformers (BERT), are employed to contextualize operational comments and assign labels to the operations.
To evaluate the effectiveness and benefits of the DMS application in addressing various operational challenges, a series of experiments and surveys were conducted, comparing it with existing tools and methods. The results demonstrate that the DMS effectively delivers accurate and relevant information in a user-friendly manner. The key advantage of the DMS lies in its ability to bridge silos between different business lines, enabling stakeholders and users to monitor and analyze data from diverse sources, such as daily drilling reports (DDRs), daily workover reports (DWRs), production, testing, and rigless data.
CO2-Brine Relative Permeability of HPHT High CO2 Complex Carbonate Rocks: Case Study from Sarawak Basin, Offshore East Malaysia
Authors: Kashim M. Z. , Giwelli A. , Clennell M. B. , Esteban L. , Noble R. , Md Shah S. S. , Zaidin M. F. , Tewari R. D. 
Published: October 2023

Abstract
A high carbon dioxide (CO2) carbonate gas reservoirs located in East Malaysia's waters (denoted as S Field) was expected to be developed for hydrocarbons production while storing the CO2 separated from the produced CO2-rich hydrocarbons. High challenge was expected to de-risk the subsurface uncertainties of S Field due to its strong heterogeneity, large amount of CO2 initially present in the reservoir and high reservoir temperature conditions. The permeability of targeted injection is low which makes it more complex in term of characteristics. Relative permeability is one of the most important properties influencing the fate and transport of CO2. Generating relative permeability for CO2-brine in this kind of carbonate rocks system possessed a different challenge due to the CO2 solubility, CO2-mineral reactions and low permeability domain. Thus, this study presents three new sets of relative permeability measurement for drainage conducted in low permeability (&lt;10 mD) carbonate samples which is acquired from targeted injection zone is S Field. Unsteady-state method was employed for the experimental analysis and all the measurements were conducted at reservoir conditions. Mineral packed column was installed to mimic the aquifer brine conditions and eliminate the geochemical reactivity during the tests. Two-phase flow simulator was used to history match the data generated from the coreflooding experiments to generate the representative CO2-brine drainage relative permeability curve. Pre- and post-CO2 flooding core characterization, using RCA (Routine Core Analysis), NMR-T2 (Nuclear Magnetic Resonance) and X-Ray CT-scan were conducted to examine any petrophysical alteration that might take place during the experiment. The results from the three conducted tests showed moderate to high end point relative permeability to CO2 (&gt;0.55) and low to moderate irreducible brine saturation (0.01 to 0.4). Except for the lowest permeability sample, the post-CO2 flooding characterization showed minimal impact in term of petrophysical and micro-structural alterations due to the geochemical inhibition by mineral packed column. The lowest permeability sample was observed to be affected by high injection pressure which altered the properties of the core. Based on the generated relative permeability information, the injection of CO2 into the targeted zone is expected to provide better CO2 flow during CO2 injection and large pore-space capacity for CO2 occupancy which serve as a good candidate for CO2 storage.
Visual Twin for Pipeline Leak Detection
Authors: Hamilton M. , Al-Ammari W. , AbuShanab Y. , Sleiti A. , Hassan R. , Hassan I. , Kaan M.S. , Rezaei-Gomari S. , Rahman M. A. 
Published: October 2023

Abstract

Objectives/Scope
We describe a visual digital twin system to allow for both operation and training of a data-driven pipeline leak detection system. We show system design in terms of its data inputs and the software system which incorporates this data in real time. This system allows visualization of pipeline data and machine learning-driven leak detection in a pipeline sitting in a subsea context. The intended purpose of the system is to both train operators of the leak detection system in its use and also provide high situational awareness to those tasked with monitoring pipeline deployments. The visual digital twin system uses gaming engine technology to achieve high visual quality. We also construct a novel software system enhancement to incorporate live data streams into the gaming engine environment. This allows real-time driving of gaming engine visualization elements with which we may augment the gaming engine environment. In terms of visualization, we focus on addressing problems of large ranges of multiple scales and providing high situational awareness which minimize operator fatigue and cognitive load. We show how multiple camera views in combination with a convenient user interface can help to address these issues. We demonstrate a digital twin system for leak detection. We show its realtime operation in a gaming engine environment with the ability to instantaneously incorporate outside data sources into the visualizations. We demonstrate using simulated pipeline flow data from sensors such as pressure, temperature, etc. This is visualized in the context of a subsea pipeline on a sea floor. Given the large range of scales, we demonstrate how we can view both the full kilometer scale pipeline and smaller subsections in the context of specific sensor data streams. The overall system demonstrates a novel combination of advanced software systems which incorporates real-time data stream with visualization using a high-fidelity gaming engine. The data used represents a leak detection scenario where both operator training and situational awareness are key desired outcomes.
Enhancing Polyethylene Reactor Operation, Efficiency & Process Control Using Online Inference of Polymer Properties: Melt Index & Density
Authors: Kathuria Nishchay 
Published: October 2023

ABSTRACT
The main obstacle to controlling product quality in polymerization reactors is the lack of appropriate online polymer property measurements. In the UNIPOL Polyethylene reactor, a method for inferring melt index and density presented. From the available on-line temperature and gas composition readings, theory-based models are developed to anticipate quality characteristics. Online parameter updates are performed for these models’ movable parameters using infrequent laboratory measurements and back propagation-based parameter estimates. Operating data from LLDPE2 (UNIPOL PE) reactor is used to illustrate how this methodology is applied. These methodologies, when combined with the most recent recursive algorithms, allow inferring crucial polymer parameters, thus enabling way for Advanced Process Control by giving operators the ability to designate set point for the parameter once it has been fully validated. Knowledge of product property deviations from desired targets is required so that plant operators can take corrective actions to reduce the quality of off-grade material and produce consistent quality product.