bluuebunny/crossref_metadata_2025_split huggingface dataset:
Process Quick-Look Assessment – A Booster for Decarbonization
Authors: Guillaume Vaillant , Elder Mata-Freitas , Damien Roquet , Patrick Little , Laurent France , Matthieu Deleersnyder 
Published: October 2022

Abstract
This paper aims at describing how O&G companies can boost the decarbonization of their Upstream activities and target operational excellence by analyzing their assets with a well-structured and efficient methodology, called ‘Process Quick-Look Assessment for carbon footprint reduction’, or PQLA. This quasi-exhaustive review allows to identify and prioritize actions to reduce greenhouse gas (GHG) emissions, with a primary focus on quick wins / low CAPEX actions.
From the analysis of historical operational data, a detailed mapping of the asset GHG emissions by sources of energy (fuel gas, electricity, liquid fuels), flaring and venting is established. Then, a multidisciplinary task force with process, operations, maintenance and well performance representatives from Headquarter and Affiliate/Business Unit starts the investigations. By analyzing pressure profiles of gas, water and oil, by performing a gap analysis review with respect to (w.r.t.) best operational practices, by challenging methodically each system operation considering plant historical data, the team is able to identify areas for improvement and make impactful recommendations. Typical quick-win findings are the process control improvement of compressor anti-surge, reduction of discharge pressure of compressors and pumps, adjustment of valve setpoints, optimization of the cooling medium distribution… etc.
The structured methodology of the PQLA allows to establish a consolidated overview of the GHG saving possibilities, with strong Affiliates commitment that fully own quick-wins and low CAPEX initiatives to close the gaps and rapidly improve GHG emissions. It has been applied on multiple assets operated by the Company in Nigeria, Angola, Congo, Brazil, Denmark, Qatar, UK, Argentina… with effective or and realistic realizable GHG emissions reductions. In addition, the PQLA leads to a positive mindset change in Energy & GHG management culture within the Affiliates, contributing to onboard the operational teams to further accelerate the reduction of GHG emissions.
Video Analysis of Flare Stacks with an Autonomous Low-Cost Aerial System
Authors: Al Radi Muaz , Karki Hamad , Werghi Naoufel , Javed Sajid , Dias Jorge 
Published: October 2022

Abstract

Objectives/Scope
The inspection of flare stacks operation is a challenging task that requires time and human effort. Flare stack systems undergo various types of faults, including cracks in the flare stack's structure and incomplete combustion of the flared gas, which need to be monitored in a timely manner to avoid costly and dangerous accidents. Automating this inspection process via the application of autonomous robotic systems is a promising solution for minimizing the involved hazards and costs.

Methods, Procedures, Process
In this work, we present an autonomous low-cost aerial system to be used as a flare stack inspection system. The proposed UAV system uses the visual signal obtained from an on-board camera for analyzing the observed scene, guiding the UAV's movement, and obtaining spectral data measurements from the flare during operation of the inspected system. The UAV system uses a deep learning detection network for detecting the flare stack's structure and extracting visual features. These visual features are used simultaneously for guiding the UAV's movement along the structure inspection mission and computing combustion-related measures.

Results, Observations, Conclusions
The deep learning network was trained for inspecting the structure and monitoring the operation of the flare stack system. Simulations were conducted for evaluating the performance of the proposed structure and operation inspection technique and real images of flare stacks were used for testing the initial phases of the prototype. The developed system could autonomously collect an image database of the flare stack's structure for inspection purposes. Moreover, the trained deep learning detector could accurately detect combustion-related objects, such as flame and smoke, to give a conclusion about the current state of the flare stack system.

Novel/Additive Information
The current system introduces a novelty to combine 3D navigation using visual servoing and a deep learning detection network in an autonomous UAV system for automating the process of flare stacks inspection and monitoring. The implementation of such system is expected to lower the cost and minimize the human resource risks of flare stack inspection processes.
Using Autonomous Control to Maintain Gas Lift Well Flowing Bottomhole Pressure
Authors: Gao Zhuo 
Published: October 2022

Abstract
Optimizing a gas lift well can be challenging as it needs to optimize a complex interdependent system, where gas is compressed and injected to the bottom of the well to lift oil/water/gas to the surface. This system contains numerous facilities and equipment, from the compressor, injection valves, downhole gas-lift mandrel, valves, wellhead, and surface separator to the sales lines. Flowing bottomhole pressure plays a critical role in the system. In many cases, stabilizing the flowing bottomhole pressure within a desired range can be considered optimization of the system.
An edge internet of things (IoT) platform provides customized autonomous control logic to accommodate the customer's optimization challenge at the well level. Weatherford deployed the customized autonomous control logic to optimize the gas injection rate based on liquid production, well stability and to adjust an automated wellhead choke to maintain the flowing bottomhole pressure within the desired range.
The field results show that the methodology deployed has been able to maintain the flowing bottomhole pressure, optimize the gas injection rate, minimize the buy-back gas, and reduce the manual intervention, which, in return, largely reduced the optimization workflow's OPEX and boosted the production. In the eight-week trial, the flowing bottomhole pressure has been maintained within the target range 99.9% of the time, production was enhanced because of the optimized operating condition, and reduced the buy-back gas cost by 28%. A significant improvement in personnel efficiency was achieved by saving 8 trips/well which also reduced OPEX over the entire pilot.
The next-generation automation device incorporates the industry 4.0 technology to deliver customized autonomous control logic that leverages high-frequency, real-time data and well analysis to improve the optimization granularity. It maximizes the values that are delivered to the customer at the well site.
The Efficiency of High Power Laser Perforation in Shale Formations
Authors: Batarseh Sameeh I. , Mutairi Saad M. , SanRoman Damian P. , Al Harith Abdullah M. 
Published: October 2022

Abstract
This work introduces the high-power laser for perforation application in shale formations. Shale represents a significant role in unconventional reservoirs and production. Conventional perforation methods cause compaction and reduce permeability around the created tunnel. High-power laser perforation can enhance permeability and porosity while providing unmatched control of the perforation's shape and length. This paper presents the high-power laser perforation process on shale formation and lab-to-field transformation for the first industrial laser perforation.
A high-power laser system is used to perforate different types and sizes of shale samples. Laser perforation affects the formation and creates microcracks while interacting and perforating the shale. The rocks are characterized before and after the process using a combined multi-physics and multi-scale approach. Small core plugs and cuttings are used to study the effect at the micro-level using NMR, thin sections, and SEM. Larger samples are used to characterize the effect of the rock's overall structure, such as hardness, permeability, velocity, and micro-cracks mapping. The chemical transformations are captured using differential thermal analysis (DTA), X-ray diffraction and fluorescence (XRD/XRF), and geochemical analysis. The laser interaction is recorded in real-time using high-speed infra-red thermography and optical videos.
Experimental results showed that a near-infrared high-power laser could create long and wide perforation tunnels in shale formation with increased permeability due to the creation of micro-cracks, changes in interconnectivity between the pores, and the collapse of clays.
This work demonstrates that a high-power laser can be used to enhance the perforation processes in shale formations. The technology can controllably create tunnels of different sizes and geometry. It is a none damaging, safe and precise technology that enables the creation of enhanced tunnels with improved flow properties and the unlocking of unconventional reservoirs.
Energy Management Best Practices
Authors: Faisal Al Qurooni A , Ahmed Al Homam W , Mohammed Al Muhainy A 
Published: October 2022

Abstract
Objectives/Scope: Energy Efficiency is one of the major pillars that need to be considered seriously today more than ever to overcome potential environmental impact and mitigate climate change. Therefore, Saudi Aramco strives to improve the energy efficiency at its industrial and nonindustrial premises. These efforts are in line with the vision of the Kingdom of Saudi Arabia as several initiatives were launched to achieve net zero through decarbonization. Despite all the exerted efforts, strict policies, challenging targets and investments to achieve a continual improvement in energy efficiency, it would be hard to avoid a performance plateau without breakthrough solutions. This paper will shed light on some of best practices used in the southern area of Ghawar field to improve the energy performance of multiple gas and oil separation plants.
A Sweet Spots Prediction Method of Tight Carbonate Reservoir and Its Application in Horizontal Well Design
Authors: Niu Zhiwen , Wei Xiaodong , Yan Xiaohuan , Li Jiajia , Wang Yu , Chen Xin , Xiao Dengyi 
Published: October 2022

Abstract
Tight carbonate reservoirs are widely developed in Arabian Sea. The character of the reservoir indicates high heterogeneity, poor reservoir physical property and low productivity of which would lead to high Unit Technical Cost (UTC). Past drilling experiences show that thin sweet reservoirs identification is very important for field development. In this study, a new method of sweet spots prediction in tight carbonate reservoirs is carried out, and successfully used in horizontal wells design.
Two main aspects of sweet spot reservoir were summarized based on this study: 1) For reservoir property prediction, multidisciplinary comprehensive research is used in this study. Seismic attributes fusion between amplitude and frequency is a main technical progress to predict the favorable reservoir. Where after, the porosity model is built by the constraint of fusion attribute and logging interpretation. 2) In terms of fluid mobility, rock type based permeability model is done to classify the flow capacity of reservoir fluid. The high permeability belt can be determined. Finally, integrate research will provide the evaluation map of sweet spots, then by which the horizontal trajectory is carried out.
The study results show that sweet spots are those reservoirs with relative high porosity and high permeability compared to wall rock. Frequency and amplitude based seismic attributes fusion is a most effective way to characterize the favorable area in tight reservoir. In fluid mobility study, permeability model that constraint by lithological classification is very important to definite each flow unit of the sweet spots. Integrated analysis reveal that the sweet spots distribute in an overlap mode with poor connectivity between each reservoirs and the single reservoir thickness is only about 10~15m with an average porosity range 5%~15% and permeability range 0.1mD to 1mD. Based on the sweet spots characterization results, the horizontal well trajectory is designed to realize multi-reservoirs penetration. It is one of the best ways to maximum the well productivity, reduce the drilling risk and UTC.
This study provide a new manner to predict sweet spots for tight carbonate reservoir and improve the success of horizontal drilling. The study results had been used in pilot well design in B oilfield, Abu Dhabi Offshore. The successful drilling experience can be used in the similar reservoirs of Middle East.
Completion Design and Hydraulic Fracturing Evolution in a Tight Reservoir Having Low Poisson's Ratio and Low Young's Modulus: A Case Study in Aishwariya Barmer Hill (ABH) Oil Field, Rajasthan, India
Authors: Bohra Avinash , Vijayvargia Utkarsh , Tiwari Shobhit , Gupta Sumit , Kothiyal Manish Dutt 
Published: October 2022

Abstract
Aishwariya Barmer Hill (ABH) field is a moderate permeability (0.5 – 4 mD) oil bearing porcellanite with alternating sequences of tight shale. After successful appraisal campaign a full field development with multi-stage fracturing using cemented frac sleeves, field was brought online with Hydraulic Sucker Rod Pump (HSRP) as artificial lift and has been on production since 2019.
However, a need was felt to review the frac and completion design on account of challenges faced during fraccing and unplanned downtime during production operations. Critical observations that prompted a change in completion and frac technology are: Formation rock pebbles and proppant were observed in the wellbore during workovers.Reservoir's low Young's Modulus (YM) allows the generating high strains at low pressures, while low Poisons Ratio (PR) makes the rock brittle and shatter under high deformation. Consequently, shattered rock was not able to hold the proppant in place after fracture closure resulting into flowback of proppant and pebbles.Debris fill in wellbore resulted in production impairment and malfunctioning of HSRP.
To mitigate the identified risks, the design change incorporates measures to address post fracturing production problems related to high treating pressures as well as optimize number of frac stages and stage spacing. Uniform proppant distribution with lesser number of stages is targeted by utilizing limited entry technique to help in distributing treatment pressures and proppant in multiple clusters as well as limit net pressure build up in each frac. It will help prevent rock shattering and better retention of proppant after frac closure.
Completion design workflow includes log based zonal isolation between each stage and frac design for two to three cluster per stage. The revised design will predict the number of stages in each well for optimal utilization of wellbore for best economical production.
Revised frac design has been implemented in 5 infill wells wherein 66 stages have been pumped without TSO signature or premature screen out in any of the stages. Wells have been put on production and are performing better than rest of the wells in the field. There has been no evidence of debris accumulation in wellbore or proppant flowback in production fluid. Further drilling campaign for 15 wells has been planned with cluster frac strategy with revised frac design.
Using Hybrid Geosteering to Proactively Position a Well in Fractured Tight Carbonate Jurrasic Rock – A Middle East Deep-Gas Reservoir Case Study
Authors: Al-Mutawa Majdi , Bloushi Taha , Al-Ajmi Saad Abdullrahman Hassan , Al-Bader Fahad Khned , Pattnaik Chinmaya , Pasaribu Ihsan Taufik , Keot Chandan Jyoti , Osman Salih Noreldin , Siam Mahmoud , Basset Mohamed Abdel 
Published: October 2022

Abstract
Deep, unconventional tight carbonate reservoirs in northern part of the Middle-East present many challenges for field development. These challenges are due to the inherent very low porosity characteristics with an average 2 to 6% matrix porosity and 0.01 to 1.5 mD permeability. Natural fractures play a primary role in the producibility of these reservoirs. High angle well is being planned as a way to intersect as many fracture systems as possible for production improvement.
To better visualize the subsurface structure while drilling, Ultra Deep reservoir mapping while drilling was deployed to map the whole reservoir boundaries in real time and know the formation dip and precise trajectory position from reservoir roof and base at any point in the lateral section. The 3D reservoir mapping gives precise geological geometry of the reservoir and enhances the placement of the well. The information from Reservoir Mapping While Drilling integrated to other downhole measurements such as Near bit Gamma Ray, Resistivity and the surface logging of gas composition and geo-chemical data could help immensely to determine the productive fracture zones.
The foremost drilling challenge in deep Jurassic carbonates is exposure to high borehole temperature which can go up to 300 degF, due to high friction factor at depths of more than 12000 ft vertical depth from sub-sea (TVDSS), combined with high temperature gradient. This downhole high temperature environment almost reaching LWD tools maximum temperature thresholds, could potentially affect the LWD tools while drilling because of excessive fatigue to the electronics.
This paper will discuss the case study of well placement challenges in deep Jurassic tight carbonate reservoirs and solutions to ensure of the success of the well operation and achieve well objectives.
New Technological Possibilities to Perform On-Stream Monitoring of HTHA
Authors: Putintsev Sergey , Lens Herman 
Published: October 2022

Abstract
High Temperature Hydrogen Attack (HTHA) is a phenomenon that has become a center of attention for operators of chemical, petrochemical and fertilizer plants after several incidents in the recent years. These incidents have forced the industry to reconsider the scope of non-destructive testing and the inspection approach, and revealed a vital necessity for better prediction and monitoring. Recent technical developments are making it possible to perform HTHA inspections in-service (on-stream) during operation in addition to the traditional inspections during a shutdown. This creates a favorable opportunity to monitor early stage HTHA, which would allow asset operators to optimally utilize their assets without concessions to integrity and safety.
Agile Development of Machine Learning (ML) for Conventional Artificial Lift Systems in the Middle-East
Authors: Ajmi Ahmed , Andrade Antonio , Vargas Luis , Mackay Angus , Al-Busaidi Mahir , Al-Kharusi Faris , Azri Ahmed 
Published: October 2022

Abstract
The majority of Oman's southern fields are produced by beam-pumps which consist approximately 2,000 wells, globally beam pumps remain an extremely popular choice for secondary lift. Identification and diagnosis of beam pumps using dynocards is an expensive human visual interpretation process. It does not only require significant labor time but also requires deep expertise in the production technology domain. The development team had three goals: 1) use open-source analytics, 2) develop a Machine Learning application (ML) to solve business challenges and finally 3) foster solutions with significant value investment ratio (VIR). In this case, a proof-of-concept application was developed to automatically screen beam pump dynocards and identify abnormalities (e.g., electrical related failures) which cause improper operation of the well, leading to deferment, undetected by conventional monitoring systems, and/or mechanical damage.
To address the above challenges, an analytics minimum viable product (MVP) was developed for pattern recognition which significantly assisted in automating (analysis of a 100 hundred wells with real-time data in less than 1 second) the visual interpretation process, increasing efficiency, and reducing maintenance activities due to missed early diagnosis. It detects current and future abnormal conditions that cause improper operation of the artificial system to deferment and potentially to mechanical damage. This new app identifies and highlights these wells so that operations & maintenance staff can focus their attention where it is really needed, improving their workflows and decision making.
This paper outlines how applying Machine Learning (ML) along with the Scaled Agility methodology enabled the operator to develop an MVP and diagnose abnormalities on daily basis not raised by any other system. Of the 100 wells in the selected field around ~10% were identified with clear failures. This translated to approximate ~5 % improvement in lead indicator (prior to issues) detection projecting ~2.5 million USD in efficiencies and deferment reduction.
The cost of development in CAPEX was 0 USD as the team developed this purely on Open-Source platforms that were license free and on their own without the need of third-party application or resources.
Dynamic Fatigue Life and Integrity Monitoring in Novel Mobile Spiral Intelligent
Authors: Minto Chris D.G. , Stevenson Andrew 
Published: October 2022

Abstract
Evidence from initial deployments of the Hydrogen optimized Mobile Spiral Intelligent Pipeline (MASIP) with built in distributed fiber optic sensing is discussed to show the benefits of an integrated approach to pipeline integrity management. This offers not only conventional monitoring (leak detection, right of way protection) but also dynamic pipeline strain with a meter-by-meter residual life assessment that has the potential to improve the effectiveness and reduce the costs of physical inspection methods.
Vibration and Steam Leaks Monitoring Using a Batteryless IIoT Powered by Waste Heat Using LoRaWAN Wireless Protocol in Chemical Plants
Authors: Aragones Ortiz Raul , Oliver Parera Maria , Malet Munte Roger , Marquez Garda Maria Teresa , Comellas Vogel David 
Published: October 2022

AbstractThis work presents a novel waste heat-powered long-range wireless and battery-less industrial Internet of Things device (IIoT). It is conceived to measure and predict machinery vibrations and steam leaks. This self-powered and autonomous device will help energy demanding industries (chemical, petrochemical, oil refineries, paper, iron&steel, etc.) to become more environmentally friendly and profitable in their digitalization transition towards Industry 4.0 paradigm. Moreover, thanks to being continued powered by thermoelectric generators they are battery-less, enabling long-range wireless protocols adoption such as LoRaWAN, minimizing in-house wireless infrastructure, and incorporating edge computing. This capability allows to reduce up to 98% of the cloud computation effort and its GHG emissions contribution due to data can be computed inside the IoT device. These capabilities are tough to find in battery-powered IoT due to their battery lifetime limitations.