bluuebunny/crossref_metadata_2025_split huggingface dataset:
Remote Operations of Oil & Gas Fields – A Key Enabler to Achieve Operational Excellence and Digital Transformation
Authors: Banihammad Basma Ali , Al Zaabi Abdulla Rashed , AlAhbabi Nasser Hassan , Al Kindi Hamoud , Al Ameeri Abdalla Qambar , Abdul Zahir Usama , Alhammadi Sara 
Published: October 2022

Abstract
ADNOC is pursuing Digital Transformation by transforming the operating model of assets from manned operations to de-manned operations. The vision is to establish a Centralized Digital Control Centre capable of fully controlling onshore and offshore assets remotely; hence optimize the operational efficiency.
NEB ADNOC Onshore is pursuing an ambitious Digital Transformation plan aligned with ADNOC 2030 strategy. The strategy mandates to deliver a Digital Control Centre capable of fully controlling the NEB asset remotely.
As a POC, an in-house initiative was materialized to leverage the capabilities of installed advanced technologies to replicate Rumaitha Control Room in AlNouf while providing all infrastructure required to fully operate RA facility remotely, without compromising the safety and integrity of facilities.
Data Analytics and Machine Learning Predictive Modeling for Unconventional Reservoir Performance Utilizing Geoengineering and Completion Data: Sweet Spot Identification and Completion Design Optimization
Authors: Leem Junghun , Mazeli Abd Hakim , Musa Ikhwanul Hafizi , Che Yusoff M Fakharuddin 
Published: October 2022

Abstract
Performance of wells in an unconventional reservoir are largely diverse due to different geology, reservoir characteristics, and completion design. A comprehensive method of data analytics and predictive Machine Learning (ML) modeling was proposed to identify production zone "sweet spots", and optimize completion designs from reservoir quality (e.g., geological, geophysical, and geomechanical) data and completion quality data (e.g., frac stage spacing, fluid volume, and proppant intensity, in order to enhance performance of production wells in unconventional reservoirs.
Typical data analytics and predictive ML modeling approach utilizes all the reservoir quality data and completion quality data together, which mostly leads to domination of the completion quality data over the reservoir quality data because of 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 the 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 modellings.
The patent-pending methodology of predictive ML modeling has been exercised in recently developed wells of the Montney unconventional shale gas formation, British Columbia Canada, and identified sweet spots from key controlling reservoir quality data and as well as prescribed optimal completion designs from key controlling completion quality data.
Integrated Reservoir, Geology and Geomechanical Characterization for Unconventional Development: Application to the Diyab Play
Authors: Pourpak Hamid , Hernandez Eider , Higelin Maxime , Jaber Maysaa , Mansoor Khalid , Sullivan Luther , Baud Emmanuelle , Su Kun , Zeeshan Baig Muhammad , Al Marzooqi Hassan , Van Laer Pierre , Alharthi Amena D. , Al Hashmi Abdulla Ali , Brooks Trevor Daniel , Al Arbai Mohamed Elsayed 
Published: October 2022

Abstract
The Diyab Formation is an organic-rich carbonate rock with low permeabilities and is one of the first unconventional targets to emerge in the Middle East. Vertical and horizontal exploration wells were drilled during the past years with proven productivity in the United Arab Emirates (UAE). Coupled geomechanical and reservoir characterizations of the Diyab formation are crucial for the successfulness of Stimulated Rock Volume (SRV) Creation and hydraulic fracturing operations which can have a direct impact on production performance. The objective of this study was to perform a full characterization of the Diyab formation based on extensive datasets that include logs and cores. The outcome of this integrated characterization work is used to assess the behavior of the Diyab formation across the concession block.
First, we present the geology and general context of the studied area. Next, we detail the current understanding of the structural lineaments and natural fractures across the block. Then, based on full characterization work originating from data acquired on exploration and appraisal wells, we show how the results of geomechanical characterization together with the analysis of reservoirs quality/geological data allow us to suggest a vertical sub-division for Diyab formation. We explain further how the reservoir/geology, geomechanical parameters and natural fractures change laterally between wells.
Reservoir characterization work concluded that there are some lateral variabilities in Diyab formation such as the change in the thickness/mineralogy of the carbonate bench and thickness of the porous wackestone. Some lateral variations in geomechanical/SRV parameters are observed within the block, resulted mainly from change in natural fractures density and properties of the carbonate bench and porous wackestone. This work is the first result of the integration of the current available data and the knowledge on Diyab formation, which could potentially evolve with the acquisition of new data and analyses.
The combination of a full geomechanical characterization with a reservoir quality and structural geology study allows to propose a detailed reservoir and geomechanical sub-division for the Diyab formation. This approach will aid to better understand the lateral variability of facies, reservoir quality and geomechanical properties within the block which are crucial for successful development of this unconventional play.
A Novel Multiphase Workflow for Tight Unconventionals, with Eagle Ford Well Example
Authors: Majzoub Dahouk Mohamad , Lia Carlsen Mathias , Yusra Ilina , Hays Whitson Curtis 
Published: October 2022

Abstract
The goal of unconventional reservoir characterization is to forecast production based on historical well performance. This has been historically achieved using three main categories of modeling (1) decline curve analysis (DCA), (2) rate transient analysis (RTA), and (3) finite-difference numerical modeling. In the past 4 years, new technologies have emerged that, when used together, allow maximum consistency in production forecasting between these modeling approaches. The objective of this paper is to present a novel multiphase workflow integrating the most recent developments in unconventional reservoir characterization, and applying the workflow to an Eagle Ford well example provided in the SPE Data Repository.
The unconventional reservoir characterization workflow presented in this paper can be divided into 6 main steps. (1) PVT to correctly describe the fluid behavior and fluid properties using flowback data (Younus et al. 2019, Carlsen et al. 2020); (2) bottomhole pressures, to measure or estimate the flowing bottomhole pressure of each well; (3) multiphase flowing material balance, to estimate the contacted pore volume and contacted OOIP / OGIP (Thompson & Ruddick 2022); (4) numerical RTA, to quantify early time well performance using the linear flow parameter (LFP) (Bowie & Ewert 2020, Carlsen et al. 2021); (5) numerical modeling & forecasting; to estimate the EUR with different bottomhole pressure forecasts; and (6) decline curve analysis; to input DCA parameters into economic evaluation workflows. Common challenges and best practices are provided for each of the 6 steps of the workflow, resulting in a comprehensive workflow that can be readily applied consistently by any reservoir engineer working with unconventional reservoirs.
The proposed workflow has been used on many hundreds of wells covering all the ultra-tight unconventional / shale basins in North America. In this paper, the workflow is specifically applied to a well in the Eagle Ford, yielding (but not limited to) initial fluids in place, reservoir rock properties, PVT, well completion factors, fracture area and conductivity, and definition of infinite-acting and boundary-dominated flow regimes. The most relevant characteristics of the proposed workflow are (1) it can be automated and would require less than 30 minutes of engineering analysis per well; (2) it results in a full physics numerical model (finite difference) that can readily be used for any further studies. The proposed workflow is rigorous, complete, and practical, the first of its kind – (1) rigorous, leveraging existing and proven technologies, (2) complete, including every key engineeringstep from the PVT to production forecast and (3) practical, being used currently for more than a thousand wells in several North American basins.
Digital Transformation Drives Reduction of the Inactive Strings Count. A Success Story in a Major National Oil Company.
Authors: Al Braiki Warda Awadh , Davila Rayner Samuel , Molina Adriana Del Carmen , Azhar Mohammed , Shaik Zayed Iqbal , Alyaqoubi Afra Saeed , Eldin Haitham Alaa 
Published: October 2022

Abstract
This paper demonstrates a successful case story of how harnessing digitalization has significantly improved the Management of Inactive wells. An integrated web-based application was developed in house and fully deployed across the Company. It has demonstrated an immense added value by bringing key stakeholders in an automated and collaborative environment to standardize and improve the existing well reactivation cycle and mitigating well & facilities integrity threats.
Inactive Strings Management System (ISMS) is tailored made for multiple disciplines such as production, petroleum, well integrity, facilities and reservoir engineers, as well as Corporate Units and Management, all connected through standards automated workflows.
The new solution is composed by 13 main modules that covers the whole well reactivation cycle: a) Data Entry b)Validation, c) Endorsement, d) Live Well Inventory, e) Tracking and Monitoring, f) Well Problem & Status, g) Dashboards, h) Well history, i) Well Prioritization, j) Manager Approval, and k) Level 2 KPI's. 1) Managing Problematics Wells, and m) Project Related.
The Inactive String Management Tool has proved to be vastly beneficial. Since its implementation in December 2021, it has successfully supported the reactivation of around 810 strings, and monitoring over 4800 operating strings, with an associated cumulative oil rate gain of 490,000 barrels, translated into a profit of around $23 MM (USD), and shortened the well reactivation cycle by 20%.
The solution had led to major improvements on the current business and system processes by addressing major challenges including data integrity issues, lack of automation, systems integration, poor tracking of well reactivation actions, and most importantly, absence of a systematic approach to prompt multidisciplinary well revision by having a unique planform to manage the inactive strings, thru standardization of best practices and lessons learned across Assets.
It has increased the well problem visibility and accountability of key stakeholders to firm, maturate and accelerate well reactivation actions in an automated and collaborative environment, reducing silos, ensuring alignment, sharing knowledge and information more effectively. Improved resources planning and production gain without jeopardizing well and facilities integrity.
This solution is part of the Oil and Gas 4.0 company digital transformation strategy, encourages collaboration across departments, fostering a digital culture to rapidly react to business changes. It has laid down the foundation to sustain growth and a competitive edge in the digital age by applying deeper and more targeted analytics that enable better business decision-making, such as predictive analytics, artificial intelligence, and business process management.
Digital Control Tower – Instantaneous Visibility, Granularity and Decision Support for an LNG Mega Project
Authors: Srivastava Yogesh Chandra , Srivastava Abhishek , Granata Consuelo , Garg Tanvi 
Published: October 2022

Abstract

Objectives/Scope
Cloud based end to end digital project management platform with digital control tower, as fundamental technologies of the fourth industrial revolution, have enormous potential to improve real time visibility, productivity, collaboration, data sharing, efficiency, predictability, decision power and sustainability in the construction industry.
Throughout the lifecycle of capital / mega projects, the engineering, construction, operations, and maintenance stakeholders face numerous issues, including the lack of trust and visibility, inefficiencies, and the fragmentation of the information value chain into fragile data silos. Digital control tower aimed to narrow down the time, budget and quality & safety challenges of a capital project by having overall / end to end control of project from initiation phase to handover and operations phase of the project.

Methods, Procedures, Process
Digital control tower provides systematic, 360-degree view of project details along with real-time analytics spanning from design engineering, pre-construction planning, onsite execution, action tracker, construction services, equipment's, quality, budget and safety (leading and lagging indicators).
Stakeholders involved throughout the project lifecycle (i.e.: owner, engineering team, pre-construction and planning team, construction management and site team), enables instantaneous connectivity from the top floor to the shop floor for seamless communication by reducing waste time.
Digital control tower also enables bird's eye view for real time project performance monitoring and progress, it also enables week wise look ahead task for better monitoring and control, also narrows down the issues or concerns to an activity, area or person. The control tower can create the customizable necessary discipline/function dashboards available to all relevant roles and stakeholders without limitation.

Results, Observations, Conclusions
Digital Control Tower leverages Artificial intelligence, Machine learning and Blockchain technologies to seamlessly capture, maintain and process fragmented data set into intelligent predictive analytics which helps the project stakeholders to take right decision on right time, so as to avoid any kind of delays in project.

Novel/Additive Information
Digital Control Tower is not a standard dashboard, it's an integrated digital ecosystem where stakeholders can drive projects with real time data for decision making.
Deep-Learning-Based Surrogate Reservoir Model for History-Matching Optimization
Authors: Maarouf Alaa , Tahir Sofiane , Su Shi , Ramatullayev Samat , Rat Coriolan , Kada Kloucha Chakib , Mustapha Hussein 
Published: October 2022

Abstract
Achieving a high-quality history match is critical to understand reservoir uncertainties and perform reliable field-development planning. Classical approaches require large uncertainty studies to be conducted with reservoir-simulation models, and optimization techniques would be applied to reach a configuration where a minimum error is achieved for the history match. Such techniques are computationally heavy, because all reservoir simulations are run in both uncertainty studies and optimization processes.
To reduce the computing requirements during the optimization process, we propose to create a robust deep-learning model based on the hidden relationships between the uncertainty parameters and the reservoir-simulation results that can operate as a surrogate model for computationally intensive reservoir-simulation models.
In this paper, we present a workflow that combines a deep-learning, machine-learning (ML) model with an optimizer to automate the history-matching process. Initially, the reservoir simulator is run to generate an ensemble of realizations to provide a comprehensive set of data relating the history-matching uncertainty parameters and the associated reservoir-simulation results. This data is used to train a deep-learning model to predict reservoir-simulation results for all wells and relevant properties for history matching from a set of the selected history-matching uncertainty parameters. This deep-learning model is used as a proxy to replace the reservoir-simulation model and to reduce the computational overhead caused by running the reservoir simulator. The optimization solution embeds the trained ML model and aims to deliver a set of uncertainty parameters that minimizes the mismatch between simulation results and historical data. At each optimization iteration, the ML model is used to predict the well-level reservoir-simulation results.
At the end of the optimization process, the optimal parameters suggested by the optimizer are then validated by running the reservoir simulator.
The proposed work achieves high-quality results by leveraging advanced artificial-intelligence techniques, thus automating and significantly accelerating the history-matching process.
The use of uncertainty parameters as input to the deep-learning model, and the model's ability to predict production/injection/pressure profiles for all wells is a unique methodology. Furthermore, the combination of the deep-learning surrogate reservoir model with optimization methods to resolve history-matching problems is advancing the industry's practices on the subject.
Foundational Study of Artificial Intelligence Reservoir Simulation by Integrating Digital Core Technology and Logging Data to Optimise Recovery
Authors: Lener Alberto 
Published: October 2022

Abstract
In strategising development of hydrocarbon reservoirs, substantial uncertainty in recovery potential is often attributed to subsurface heterogeneity. Challenged reservoir characterisation is proposed to be directly due to the inability of correlating spatial scales: core analyses to well logging data. This study’s central goal is to propose a ‘Multiscale link’ by challenging empirical correlations of multiphase displacement and ‘upscaling’ processes of reservoir characterisation by exploiting Artificial Intelligence and ‘Digital Rock Technology’, aiming at minimising geological risk.
By exploiting 40 years of a North Sea field's appraisal and production and formulating an AI-compatible ‘multiscale’ data set, petrophysical correlations have integrated a further innovative concept: borehole image processing to characterise geological features and oil potential. In binding the ‘Multiscale’, fundamental multiphase dynamics at pore-scale have been critically associated to most affine reservoir modelling ‘deep learning’ frameworks, leading to ideating an AI workflow linking field-scale rates, well logs and core analyses to the continuously-reconstructed pore network, whilst extracting invaluable multiphase dependencies.
The preliminary results implementing selected Machine Learning algorithms, coupled with advanced digital technologies in reservoir simulation, have been showcased in proposing a solution to the ‘Multiscale link’ in reservoir characterisation, providing the groundworks for its programming realisation. Importantly, it was concluded that the layers of complexity within learning algorithms, which constrained its execution within this project, undoubtedly require multidisciplinary approach. By conceiving a physically and coding-robust workflow for advanced reservoir characterisation and modelling permitting ‘multiscale’ representative multiphase simulations, identification of optimal EOR becomes attainable.
This leading edge represents potential to minimise geological risk, thus de-risking reservoir management (in turn FDP) of mature and live fields; but also expected to set a starting point for further developments of Artificial Intelligence in the oil and gas industry.
How Digital Twins Will Help to Unlock United Nations Sustainable Development Goals
Authors: AlAmir Noora 
Published: October 2022

Abstract
This study analyses the need to overcome barriers in the path to adoption of digital twin technology by oil and gas companies thereby achieving value maximization for investments. Case studies provide evidence for demonstrated achievements in terms of corporate sustainability, while pursuing the adopted United Nations Sustainable Development Goals, thus highlighting the existing and future possibilities made available by Digital Twin technology – building the case for digital twin technology as a breakthrough technology. In order to enhance efficiency, and safety, the hydrocarbon industry is looking at a wide range of digital technologies, including machine learning, artificial intelligence (AI), Internet of Things (IoT). It is becoming increasingly common for the hydrocarbon industry to use data-oriented solutions as a means to improve performance, efficiency, and cost-effectiveness. Understanding such complicated systems necessitates the simultaneous examination of data gathered from numerous sources. The next era of large production monitoring and efficiency systems will be based on Digital Twin (DT) modelling. From underground equipment to central production plants, it is part of a productivity-boosting solution that integrates data, simulation, and visualization. Hydrocarbon Exploration can greatly benefit oil and gas companies if these cutting-edge technologies are properly utilized in addition to pursuing the adoption of UN SDG vision. There are many ways in which this study aims to incorporate digital technology into hydrocarbon exploration, drilling and production. This study investigates the necessity of oil and gas companies overcoming obstacles and create an ecosystem that stand in the way of their adoption of digital twin technology in order to maximize the value of their investments and achieve value maximization.
Advantages of a Compositional Framework for Well and Surface Network Modeling
Authors: Mogensen Kristian , Samajpati Swarjit 
Published: October 2022

Abstract
Integrated asset models are being constructed for all ADNOC assets as part of a production optimization initiative supported by a significant digitization effort. Contrary to the standard industry practice of utilizing black-oil formulations to capture fluid behavior, a compositional modeling framework was selected to address some key challenges: Compositional variation at reservoir-level, either lateral or verticalInjection of gas (immiscible as well as near-miscible) causing mass transferBlending of different fluids in the surface network at line conditionsOperational requirement to maintain the bottom-hole pressure above saturation pressureValidation of raw well test data before shrinkage correction (line conditions)Investigation of impact of changing separator settings (affecting shrinkage correction)Tracking of fluid composition in produced streams
The compositional framework is very comprehensive. For each of the 100+ producing reservoirs, one or several equation of state (EOS) models was developed. In every well, an initial fluid composition estimate was provided as an anchoring point, which was subsequently adjusted in a tailor-made workflow to match the solution gas-oil ratio measured in the field by performing an isothermal flash of the original composition and then recombining the flashed oil and gas streams to the field gas-oil ratio.
The workflow offers a number of advantages, one of which is that the path-to-surface correction can be imposed directly on field measurements. This has resulted in much improved allocation factors for oil, gas, and water. Fit-for-purpose algorithms have been developed to perform well test validation for different well types based on raw data at line conditions.