bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ground Robotics Enabler for Normally Unattended Installations
Authors: Munoz Jean-Michel , Bartoli Eric , Meyer Eric , Berson François , Mackinnon Dave , Carles Nicolas , Nourisson Guilhem , Kydd Kris , Murdoch Andrew 
Published: November 2019

Abstract
Normally Unattended Installations with presence of people on site only once per year (NUI-1Y) for maintenance campaigns is the ultimate aim of unmanned concepts and a new frontier for cost reduction. Ground robotics is one enabler of this goal. This new technology is in its infancy and still requires technological and organizational advancements to be readily accepted and supported by the industry.
Total fully supports the unmanned concept and has been involved for several years in the development of autonomous ground robots capable of working in potentially hazardous environments. Adopting ground robots in our operations is not only a matter of Technology Readiness Level but also requires expertise on how to use this new technology. Accordingly, the Total roadmap for robotics development focuses on three key areas:–Technology development: Starting from the ARGOS Challenge back in 2014 - Total has been engaged in the development of a new series of robots for anomaly detection / emergency response, and started a Joint Industry Project in 2019 on work class operating robots with simple and complex manipulation capabilities. The anomaly detection robot is now ready to be deployed on operational sites with the first deployment in the UK planned for September 2019.–Operating Experience: Identifying limitations and capabilities of robots is essential to integrating them into our operating concepts. Additionally, Total has set up a robotics development platform where robots are regularly tested and challenged by Field Operations personnel to find new use cases and to share this new technology within the organization.–Reliability: Unmanned design concepts using robots will not be accepted if the robots are not reliable. Therefore, to give confidence to decision makers, long term pilots are planned on several operational sites to gain the thousands of hours of operating experience needed before approving the development of new assets employing this technology.
This presentation gives an overview of the Total roadmap on robotics, sharing results of the latest developments and how robots are being integrated in our operating philosophies.
Identifying Facies Control on Diagenesis and Reservoir Quality of an Offshore Mid-jurassic Reservoir
Authors: Zakaria Hasan , Al-Hammadi Mariam , Lehmann Christoph , Al-Hassani Sultan , Ahmed Shafiq , Khan Owais , Al-Ali Ahmed 
Published: November 2019

Abstract
A sedimentological study of an Offshore, mid-Jurassic reservoir was carried out in order to understand reservoir quality and reservoir architecture. In-house core description, and analysis of open-hole petrophysical log interpretation and conventional core analysis, enabled the authors to interpret the depositional environment and produce hand drawn facies maps to understand the facies architecture; by developing facies scheme, depending of depositional environment comingled with diagenetic process.
About 2,285 ft of core and 1,799 thinsections were described from 18 wells, distributed throughout the field. Description of core was done at 1:50 scale and about 495 thinsections were pictured throughout the reservoir section. Detailed description of textural and faunal content, along with sedimentary features was carried out to determine the depositional model and facies architecture. By analyzing the diagenetic features and porosity type, Reservoir Quality controls were derived. All these elements were taken into consideration in order to build facies scheme that not only justifies the depositional environment but also incorporates the impact of diagenesis.
A low relief, very active shoal system on a giant carbonate platform was interpreted. Various Lithofacies were grouped into 5 Facies associations keeping in mind depositional environment and diagenetic impact. This model was used to make 4 hand drawn facies maps representing reservoir X and High permeable layer, X7 with general tendency of better facies in the northern part of the field.
The novelty of this work is the differential degree of cementation due to the abundance of echinoderms degrading the reservoir quality, and identifying these dominant echinoderm facies zones allows us better well placement by avoiding them.
A Business-Oriented Framework to Evaluate Advanced Analytics for Predictive Maintenance: Measuring Benefits-Effort Tradeoff
Authors: Cadei Luca , Corneo Andrea , Milana Diletta , Loffreno Danilo , Lancia Lorenzo , Montini Marco , Rossi Gianmarco , Purlalli Elisabetta , Fier Piero , Carducci Francesco , Nizzolo Riccardo 
Published: November 2019

Abstract
The use of advanced analytics techniques has become pivotal for the Digital Transformation of the Oil and Gas Industry. Most of these models are used to predict and avoid the off-spec behaviors of both equipment and functional units of the plant and also for predicting overshooting events in advance allows plant’s operators to avoid production down-time.
From a Machine Learning perspective, predicting off-specs situation and peaks in time signal is a complex task, due to the great rarity of events. For the very same reason, using standard data science measures – like Area Under the Curve (AUC), Recall and Precision – can lead to misleading performance indicators. In fact, a model that predicts no off-spec would have a high AUC just because of the unbalanced classes, leading to many false alarms. In this paper we present a business-oriented validation framework for big data analytics and machine learning models applied to a upstream production plant. This allow to evaluate both the effort required to operators and the expected benefit that could be achieved.
The validation metrics defined take the classical Data Science measures to the business domain. This allow to adapt the model to the very specific use case and end user addressing the specific upstream plants constraints. This framework allows to define the optimal tradeoff between effort required and preventable events, providing statistics and KPIs to evaluate it. Normalized Recall (NR) takes into account both the percentage of events intercepted and the effort required, in terms of Attention Time (AT), when the operator should pay attention to the equipment involved. Plant operators can now have an idea of the results they can achieve with respect to the maximum effort required. Moreover, to prove the goodness of the model, we defined the lift in the NR as the ratio of the model NR and the NR that would be obtained by randomly distributing the same number of alarms.
We applied this framework to specific use cases obtaining an expected recall of 40-50% with an expected effort of 5-10% of the time (considering more than 6 months). The effort is actually lower, since the operator is not requested to be fully committed to the alarm. The innovative framework developed is able to demonstrate the real operating capability of the analytics implemented on field, highlighting both the effort required to operators and the accuracy of machine learning tools.
Setting Up of Project Control Learning Academy in ADNOC
Authors: Balasubramanian Nagarajan , Albreiki Ali , Basioni Ahmed 
Published: November 2019

Abstract
The Project portfolio in the Oil & Gas industry across the value chain is quite large. Significant investments are done in complex environments to manage these Portfolio with annual budgets in International Oil Companies and National Oil Companies running into several billions of dollars. There are many challenges managing Projects and one of the key functions that helps in managing Projects is "Project Controls".
A Unified Value Assurance Process for Projects in the Upstream Business unit was rolled out in July 2015. Based on the learnings from previous project execution and challenges to manage Project schedules, cost, budgets and risks, it was felt that the knowledge level of the Project Management and Project controls function needs to be raised. It was in this context that an idea to set up a Projects Academy came about. Preliminary evaluation for this academy showed extensive efforts, coordination, involvement of external parties and extended time to get the Project Academy set-up. As a quick win, the upstream business line supported the establishment of a "Project Control Learning Academy" with in-house resources.
We will present in this paper how the Project Control Learning Academy was set-up and training being imparted through Human Capital function. We will also address the lessons learnt and the future course of action for enhancing the Academy.
Preparations for Foam Gas Shut off in Carbonate Reservoirs
Authors: Skauge Arne , Aarra Morten Gunnar , Ormehaug Per Arne , Solbakken Jonas , Mogensen Kristian , Masalmeh Shehadeh 
Published: November 2019

Abstract
High GOR is a constraint for many production wells. Foam has been proved able to reduce GOR in several field production well treatments. The primary goal of this study is to qualify foams for use at reservoir conditions (280 bars, 103 °C). Foam will block gas and thereby reduce Gas-Oil Ratio (GOR) and improve field oil production.
Bulk foam experiments and dynamic foam core flooding experiments have been performed to qualify foam for gas shut off at reservoir condition. Foam has been studied in static foam tests using different surfactants, and in limestone core flood experiments at reduced conditions 30 bars and 50 °C. Further foam generation and gas blocking test at reservoir conditions have been conducted both in outcrop and in reservoir material.
Dynamic core flooding experiments at 30 barg and 50 °C have shown generation of moderate to strong foams in carbonate limestone core material both with Nitrogen and Methane as the gas phase. Strong foam with mobility reduction factors around 65-70 have been generated in reservoir core material at reservoir conditions. The presence of oil in the carbonate material is a challenge for in-situ foam generation. One solution to the problem has been identified by use of a pre-flush with FS-500 (fluorinated surfactant), prior to alpha olefin sulfonate (AOS). CO2-foamers such as Duomeen has been tested, but gave no foam with Nitrogen as gas phase. Higher concentration of AOS surfactant was able to generate in-situ foam without assistance of fluorinated surfactant. The generated foam is stable also with lower surfactant concentration after first generation. The mobility reduction factor is constant for a range of surfactant concentrations.
Strong foam has been generated in carbonate reservoir core material at reservoir conditions. The presence of oil in carbonate material prevents in-situ foam generation at low surfactant concentration, but gives strong foam at higher surfactant concentration.
Rock Classification Based on Micro-CT Images using Machine Learning Techniques
Authors: Shaik Abdul Ravoof , Al-Ratrout Ahmed Ahed , AlSumaiti Ali M , Jilani Abdul Khader 
Published: November 2019

Abstract
Rock classification plays significant role in determining the fluid flow movement inside the reservoir. With recent developments in computer vision of porous medium and artificial intelligence techniques, it is now possible to visualize unprecedented detail at the scale of individual grains, understand the patterns of contact angles and its direct connection to multiphase fluid movements within the porous media. The outcome of this work is a probabilistic rock classification model that provides a reliable and realistic description of the reservoir.
As part of this work, 400 fully brine saturated 3D micro-CT images of Bentheimer and Clashach micro core plugs are utilized. Various three-dimension image analysis techniques are applied to quantify the rock properties (e.g. porosity, absolute permeability) and to extract pore structure information, such as pore throat distribution, pore connectivity and pore roughness from these images. The rock surface roughness is quantified as the local deviation from the plane (AlRatrout et al. 2018). The whole image dataset is divided into two separate subsets, 80% for training purpose and 20% for testing purpose. Both subsets are fed to an artificial intelligence-based model to verify and validate the results. To improve the accuracy of the model, k-fold validation technique is implemented.
The accuracy of the developed model is validated using Root-Mean-Square Error (RMSE), coefficient of determination (R2) and relative error (RE). Blind test of comparing predicted results with second subset of experimental data have shown that the developed model is capable to predict rock type with a maximum error of 3.5%. The results of this study indicate that for the given dataset, pore surface roughness has dominant effect on rock classification.
The accuracy of the developed model can be improved by incorporating additional information, for example rock mineralogy. However, the developed model is limited only aforementioned rock types, can be easily extended to other rock types provided enough micro CT images are available.
Seismic Diffraction Imaging in Laterally Varying Velocity Media for Frequency Bandwidth Expansion - Application in Carbonate Field Sarawak, Malaysia
Authors: Bashir Yasir , Abdul Latif Abdul Halim , Rezaei Shiba , Mahgoub Mohamed , Moussavi Alashloo Syed Yaser , Hermana Maman , Prasad Ghosh Deva , Weng Sum Chow 
Published: November 2019

Abstract
Seismic Imaging for the small-scale feature in complex subsurface geology such as Carbonate is not easy to capture because of propagated wave affected by heterogeneous properties of objects in the subsurface. The principal goal of anisotropic seismic diffraction & reflection imaging is to get a subsurface image of structural features with the greatest sharpness or resolution. In this paper, we have presented a new approach for anisotropic diffraction preservation using offset and angle domain data during the initial data processing. Which leads to the better preservation of diffractions amplitude in laterally varying velocity condition. The plane-wave destruction filter is used with a modified approximation for Diffraction separation as the conventional filtering techniques mixed the diffraction amplitudes when there are a series of diffraction hyperbola. Further, the implementation of the proposed method has proven on carbonate field data from Sarawak Basin for steeply dipping Carbonate Build-up.
Computational Fluid Dynamics of HCl Single and Two-Phase Oil Flow in a Multi-Stage Vertical and Horizontal Acid Fractured Well
Authors: Al Hajeri Talal , Rahman Md Motiur 
Published: November 2019

Abstract
Currently, the oil industry is using hydraulic fracture as a tool to exploit tight and ultra-tight oil formations. In carbonates, acid fracturing is common, unlike proppant fracturing in sandsones. The main objective of this paper is to study the behaviour of HCl injected and oil flow back from a horizontal well with multi-stage acid fractures (fractured hydraulically). For a vertical well, a single acid fracture is common.
The 2D fracture model and psedo-3D fracture model are incorporated in this integrated program for acid fracturing with all geomechanics and operational constraints. With five stages of fractures, post-fracture oil production from an acid fractured horizontal/vertical well is generated from this integrated model. Program is written in MATHCAD to observe the volumetric flow rate in steady-state, transient, and pseudosteady regime. ANSYS Fluent is used to carry out a computational fluid dynamics (CFD) for oil flow back along the fractures. CFD is applied to observe production rates where sequential pad fluid and acid injection is performed until the desired fracture dimensions are reached.
Results from production model shows, for steady-state, production increased from 44 to 60 STB/D and from 113 to 124 STB/D with P-3D-C and 2D-PKN-C fracture model respectively. CFD simulation is performed using a viscous model with gravitational and turbulent effects and the results show an increase in radial turbulence at the outlet of the fracture. The absolute pressure exerted on the walls is 1700 psi and the flow velocity increased from the tip at 39.4 ft/min covering a fracture length of 500 ft in both steady-state and transient flow.
This paper investigates the effect of acid fracturing on oil production using a predetermined fracture model and dimensions. The flow characteristics are challenged in multi-stage fractures in horizontal and vertical well. The outcome of CFD will assist in upscaling the simulation to a 3D model with field values from existing wells for validity. A further development with fracture simulation are carried out for vertical and horizontal fracture to understand the deformation behavior on the predetermined zone. This paper will contribute to advanced well stimulation techniques of acid fracturing that are representative of actual field applications.
A Comprehensive Analytical Approach to Evaluate Well Performance in a Giant Offshore Brownfield
Authors: Zhao Wenyang , Al-Neaimi Ahmed Khaleefa , Saif Omar Yousef , Al-Feky Mohamed Helmy , Abed Abdalla Abdel 
Published: November 2019

Abstract
Reservoir management is a data driven process with an objective to achieve an optimum ultimate oil recovery. It is fundamental to obtain a proper understanding of well and reservoir performance, which can only be built based on acquired data. Data acquisition in brownfield has been a significant obstacle hindering the decision making process. Comprehensive utilization of available data becomes more critical.
This paper presents an analytical approach of evaluating well performance based on an exploration of the conventional lift curve method. Lift curve is basically a correlation between oil production rate and wellhead flowing pressure, which is generated based on flow testing results. In addition to the conventional way of observing a single well's performance, the analytical approach integrates all the wells’ historical and current lift curves as well as GOR and water cut performance. The analysis is performed in two dimensions. The first one is the well historical performance evolvement. And the second one is the offset wells’ performance comparison. The data driven analytical approach has been improving the understanding of well performance and efficiency of reservoir management based on an automated analytical model. The analysis has revealed a pattern of well performance changes, which could be utilized to alert the non-preferable changes. These changes could be avoided or delayed by taking proactive actions. For instance, wells with potential gas encroachment risks have been observed before GOR performance has enough evidence to raise the alert. Actions has been proposed and taken to mitigate the reservoir issue, which has been confirmed by further flow tests. The nearby wells’ cross comparison has shown great value in both reservoir analysis and field development. Improvement of horizontal wells has been evaluated to address the necessity of horizontalization in certain areas. Well intervention candidates could be identified. The observations and findings will affect the future completion strategies. Uncertainty in flow testing results is always a governing challenge in reservoir management process. The reliability of reservoir analysis is strongly dependent on the accuracy of reservoir surveillance data. The proposed approach can also further validate the input data through cross-checking with offset wells’ performance.
Stuck Pipe Early Warning System Utilizing Moving Window Machine Learning Approach
Authors: Ahmed Omogbolahan S , Aman Beshir M , Zahrani Majed A. , Ajikobi Folorunsho I. 
Published: November 2019

Abstract
As stuck pipe continues to be a major contributor to non-productive time (NPT) in drilling for oil and gas operations, efforts to mitigate its incidence cannot be over emphasized. A machine learning approach is presented in this paper to identify warning signals and give early indications for an impending stuck pipe possibility during drilling activities so as to take proactive measures to mitigate its occurrence.
The model uses a moving window-based approach to capture key drilling parameters trends and apply an unsupervised machine learning algorithm to predict abnormalities in the parameters’ rate of change. It utilizes most commonly available drilling real-time data and is therefore deployable in all type of wells. No pre-drill model is essentially required as the model utilizes a self-learning and self-adjusting model.
The methodology involves the use of change point detection in identifying rig activity and the associated drilling parameters so as to capture relevant parametric trend for analysis. Inherent in the parameter trend are the different factors that affects their readings; such as wellbore geometry, bottom-hole assembly (BHA), dogleg severity (DLS), formation characteristics, pump flow rate and pipe rotations.
The algorithm has been tested on historical wells data in which stuck pipe incidence, near-miss stuck pipe occurred, and incidence-free wells to prove the concept. The results of the model performance is hereby presented along with an accuracy measure.
Scratch Test for Strength and Toughness of Oil Well Cement with Nanoclay as an Additive
Authors: Murtaza Mobeen , Rahman Mohammad Kalimur , Al Majed Abdulaziz Abdulla , Tariq Zeeshan , Mahmoud Mohamed 
Published: November 2019

Abstract
The mechanical properties are determined to measure the sustainability and long-lasting behavior of cement slurry under wellbore conditions. Different measurement methods were adopted in the past to study the mechanical behavior of a cement slurry. The most commonly used methods applied in oil and gas sector are cement crushing and acoustic velocities measurements. Both techniques have some limitations and additional techniques are warranted. Scratch test technique is commonly used for characterization of mechanical properties of metals, coatings and other materials. Advances in scratch testing of materials has resulted in its application to cohesive material such as rocks and cement. Recently, scratch test has been successfully applied for the strength evaluation of oil well cement. In this paper, we present the results of scratch tests carried out on oil well cement using type G cement and the specimens modified using nanoclay as an additive. The compressive strength test results from scratch test was compared to the macro level testing of cement cores loaded in compression up to failure. The dynamic elastic parameters of cement mix, elastic modulus and Poisson's ratio, were also determined using the scratch test. The scratch test based strength measurement technique will serve as a very handy tool for drilling and geomechanics engineers to study the mechanical properties of the cement slurry aged under different wellbore conditions with high level of certainty.