bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automated Real Time Anomaly Detection Model for Operation and Production Data at Scale
Authors: Mohammad Fuad Iqmal Irsyad , Ngah Demon Mohd Fuad , Husni Husiyandi 
Published: November 2020

Abstract
Oil and gas industry have evolved towards digitalization and data are fully utilized for decision making, cost optimization, improve in efficiency, and increase productivity. Upstream sector in oil & gas produce a huge number of operation and production data in a real-time platform. It is tedious process that somehow impractical and inefficient to quality check and analyze all available data manually (Subrahmanya et al., 2014). By using machine learning algorithm, this can be improved to automate data quality check at scale. On top of that, imputation can also be implemented to substitute on missing data and future forecast in real-time.
In a case of this study, a huge data was collected from more than 30,000 tags/sensors in real-time. The real-time data were collected up to seconds and quality check need to be done up to each data collected. Firstly, each equipment tags/sensors had been checked and arranged with P&ID drawing. Then, API was developed with the real-time platform. In this project, percentile of machine learning was applied and developed to quality checked the operation and production time-series data at scale. Lastly, the process was customized to other offshore platforms in the field. In addition to automated data quality checking, machine learning algorithms were also used to calculate missing information based on the underlying relationship between data points. These approaches would reduce time needed to maintain quality and reliable data for further analysis and usage.
As a result, percentile in machine learning successfully automate the process of data quality check for more productivity and efficiency. The percentile was applied to understand, validate, and monitor data at scale. Anomalies were detected in real time that allows operators to analyze further on any possibility in faulty, damage, or loss. All the outliers, missing or wrong data were also recorded and visualized in a dashboard. The model also provides additional statistic to define stale and bad data on top of automated define parameters. These features have improved efficiency of data acquisition and preparation.
As conclusion, the model assists operator in monitoring daily operation and production data efficiently. Data quality and reliability is the key factor in asset management to ensure operator trust on produced data. The quality checked data could be utilized for further analysis, troubleshooting, and decision making.
Leveraging the Latest Advancements in Automation and Digital Technology to Improve Efficiency and Safety in a Production Field: A Journey Towards Unmanned Operations
Authors: Kamin Lisa 
Published: November 2020

Abstract
This paper describes a holistic approach/strategy for effectively "de-manning" onshore oil and gas field development projects. In recent years, the concept of normally unmanned operations has garnered significant interest across the industry as operators have sought to improve efficiency, reduce costs, and extend the lifetime of existing fields. However, to date, most efforts toward de-manning of onshore fields have been narrowly focused and limited in scope.
This paper discusses how the latest advancements in digitalization can be leveraged to reduce manpower requirements in upstream operations. For fields to transform holistically, multiple solutions and technologies need to be implemented to address core functions, including production, maintenance, and health, safety, and the environment (HSE).
The paper will present the results of a conceptual de-manning study which Siemens conducted for a major national oil company (NOC) in the Middle East. As part of the study, the "as-is" state of one production field was captured during a three-day visit, which included 20 interviews with site operations and HQ teams, covering all core functions at the site. The major outcome of the study was a phased implementation roadmap, including a staged operational manpower reduction plan and production increases. It is projected that over the next five years, the NOC will be able to reduce headcount in the field from 534 down to 201. The study also quantified other potential benefits that could be captured through digital transformation, including a 5-7% increase in production efficiency, 2-5% increase in reliability, 15-20% increase in the mean time between overhauls (MTBO), and an 85% reduction in unsafe acts.
Sour Gas Production Challenges-CASE Study
Authors: Mubashir Ahmad Mubashir , Zain Yousfi Fawad , Albadi Mohamed , Baslaib Mohamed , Alhouqani Shamsa , Olatunbosun Ibukun , Agarwal Anubhav , Ahmad Zeeshan , Al Hosani Abdulla , Pendyala Viswasri , Mandal Chandra , Gadelhak Abdelrahman , Shaker Ashraf , Alsaeedi Ayesha , Elabrashy Manar , Alzeyoudi Mohamed , Alsenaidi Shemaisa , Al Muhairi Bakheeta , Al Bairaq Ahmed , Yugay Andrey , Pimenta Gervasio , Al Jeelani Omar , A Basioni Mahmoud , Sayed Sohdy , Yahya Al Blooshi Ahmed , Mahmoud Elmahdi Ahmed , Edouard Maktouf Soufiene , Al Mansoori Ali , Ali Alloghani Jasim 
Published: November 2020

Abstract
ADNOC onshore recently tested HPHT sour gas reservoirs with +30% H2S, +10% CO2 to evaluate the reservoirs and well potential as part of the efforts in supplying additional gas for meeting country's growing energy needs. Developing these massive HPHT sour gas reservoirs is essential for providing a sustainable source of energy for years to come.
Using Machine Learning Methods to Conquer One of the Last Traditional Bastion: The Human Resource Department
Authors: Lazar Raffik 
Published: November 2020

Abstract
"Having the right person at the right time at the right place" makes even more sense today under the low oil price environment. Understanding capabilities available in an organization can be hindered by the size of the talent pool and a lack of structured and systematic approach to quantify, update and manage competencies.
Using machine learning methods to maintain an evergreen competency database can help reducing discrepancies between organizational requirements and actual talent deployment.
A digital database for each talent has been created and populated with various data such as CV, corporate informations (compensation and current based location), assignment history, technical reports generated, articles / conference papers, training attended and periodical internal competency assessment / promotion history. A master database is then created including the database of each talent and ingested by a proprietary application. Machine learnings methods based on OCR (Optical Characters Recognition) and NLP (Natural language Processing) are then applied to this master database to extract synthetic and key informations needed for a more efficient resource management and capability deployment.
For each human capital, a competency matrix is created following a multi-tiered proficiency system ladder ranging from awareness to mastery levels. The machine learning methods resulted in a cloud of keywords attached to each talent and can be "browsed" similar to a Google search. Talent managers are now capable of having a snapshot of their current workforce pool skills and capability according to competency level. External workforce such as ex-employee or prospective candidate can also be included in the capability pool. In addition, a focused search, based on specific project needs and niche capability requirement, can be conducted. In a few clicks, a talent manager can lookup critical resources that can be deployed when and where needed. For entry level resources, personal development plan can be drafted with more granularity and be tailored by the capabilities available in a given location or operating units at a given time.
Mitigation Procedures and Best Practices to Conduct Slickline Memory Mode Production Logging In High-Pressure High-Temperature Sour Gas Wells
Authors: Ghamdi Ahmed , Hussain Baleegh , Qahtani Sultan , Ghanim Mohammed 
Published: November 2020

Abstract

Objectives/Scope
This paper summarizes mitigation practices to safely conduct slick line memory production logging (PLT) in high pressure sour gas wells. The paper addresses the enhancements that were made to overcome the lifting force on the tool due to high flow rates and prolonged exposure time.

Methods, Procedures, Process
Well intervention in a live well poses several safety and operational considerations. These considerations are more critical when operating at or near high-pressure, high-temperature (HPHT) conditions in sour gas reservoirs. Well intervention in these types of reservoirs is associated with specific risks and, consequently, require a modified tool string designed specifically to withstand such a demanding environment. The paper describes all the problems that can occur in performing such jobs, and their mitigation practices that will lead to a successful job.

Results, Observations, Conclusions
There are several conveyance methods to run PLT in gas wells, such as: slickline, wireline, and coil tubing. In this job, the wireline could not be utilized due to its material which is not compatible with sour gas. Coil tubing is generally run in horizontal wells, but for a vertical well, CT is not a cost-effective option. Hence, slickline in memory mode was chosen. The slickline material is of utmost importance, and must be verified to withstand sour environment. Moreover, the pressure control equipment (PCE), H2S partial pressure, job safety analysis (JSA), job schedule, and job time, are all important factors that were carefully analyzed to successfully perform this job.

Novel/Additive Information
This paper might be the first in the literature describing how to safely perform slick line memory mode PLT in high-pressure high temperature sour gas environment.
Putting a Value Tag on Well Data Management When Designing a Plug & Abandonment Operation
Authors: Caramanico Livio , Déliac Eric , Mirza Ali , D'Alesio Paolo 
Published: November 2020

Abstract
Proper well Plug & Abandonment (P&A) design requires an accurate picture of the current well status, since it represents the starting point to ensure the permanent isolation of zones with potential flow.
The greater the amount and quality of data available, the clearer the picture of the well, the better the P&A design. Then, the selection of the most suitable technologies for the specific case allows optimizing the operational sequence thus minimizing the related costs, typically around 50% of the global asset decommissioning cost.
Missing or inaccurate data at the P&A design phase results in a conservative approach, due to the many uncertainties regarding the well.
Data quantity and, mainly, quality is often dependent upon the age of assets and the existence of a proper Well Data Management System. This paper brings the P&A experience of a platform-based six-well asset in Africa, with no available well data repository resulting in a thorough "hunt for information" to generate a small database and identify the main data gaps relevant to the P&A operation being designed.
The collected data, ranging from 10 to 45 years of age, was highly fragmented and heterogeneous. For some wells, the missing information even included well construction files, cement logs, some overburden lithology parameters and, for one of the wells, the characteristics of wellhead and annulus fluid.
The execution of an uncertainty analysis based on the good practices applied in the North Sea and the Gulf of Mexico, showed that P&A cost could have varied between M$ 3.7 and 7.5 per well. The latter value was due to additional well barriers resulting from the uncertainty of well status.
Analysis of the initial BoD and potential optimization of the P&A sequence resulted in the definition of three scenarios: Best Case (full optimization), Worst Case (no optimization) and Base Case (limited optimization based on gathered information).
Two wells are discussed in this paper: Well A, with a moderate level of information, translated into an increase from the Best Case of 18%, representing around M$ 0.7; Well B, with very poor data available, translated into an increase from the Best Case of 75%, of which around 36% or M$ 1.3 related to missing information. As a result, the final P&A scenario led, for the entire asset, to an estimated increase of M$ 6 to 7 vs. the Best-Case scenario.
A simple and adequate Well Data Management System, also described in this paper, would have allowed to provide a cost-effective P&A design. The above "M$ 6 to 7" value (around 1 M$ per well) can be considered as a good indication of the value of such a system, although it is conservative in nature, since further benefits could have been also achieved during the production life of the asset.
Project Execution Success in an Epcm Environment by Ensuring Project Management & Project Controls Rigor During Pre Construction Phases
Authors: Pratap Chandran Sujith , K. Purayil Prasannakumar 
Published: November 2020

Abstract
Typically, Owners beef up their Project Management/Controls organization and develop Project Execution concepts once a development concept has been selected and Basic Engineering gets well under-way. It is observed that Owner's Schedule & Cost influence is high, while expenditure of either is low during Concept & Planning phases as against during the Execution phase.
This work proposes elaboration of a number of key project dimensions, collectively called integrated Front-End Loading (iFEL), enabling PM diligence during Conceptual and Scope development phases, overcoming the traditionally limited visibility to PM added value in these phases by not limiting PM resource investments, while keeping the ever-evolving scope, risk, complexity challenges in perspective.
A purpose-built Project Management Organization (PMO) implementing the above methods and administering the various procedures during the Feasibility/Concept, pre-FEED (Front End Engineering Design) and FEED phases can assume control of the value levers, improve predictability of the desired project outcomes and set the project on course for EPC (Engineering, Procurement & Construction) execution success as demonstrated by reference project(s) that adopted this approach.
This work proposes establishment of a dedicated, technically competent and scalable PMO during Pre-EPC phases to oversee front-end scope definition, maintenance of a list of big-ticket decisionsto be landed during each phase, securing of key resources, contracting strategies, formulation of a proactive management response system and overall value assurance. The PMO can then suitably scale-up and assume back stopper duties during EPC(M) (Engineering, Procurement, Construction Management) follow-up, delivering on project promises that led to the investment decision.
Cost-Effective Adaptive Inflow Technology for Sand Prevention in an Injection Well
Authors: Vimolsubsin Pojana , Wasanapradit Tawan , Khunmek Thanudcha 
Published: November 2020

Abstract
This paper describes the successful application of Autonomous Inflow Control Device (AICD) technology in the Nong Yao oil field located in Block G11/48 within the Gulf of Thailand. Water injection in the Nong Yao field, is often into unconsolidated sands drained at different depletion levels. This can lead to formation failure and the transportation of sand back through the annulus and tubing. This AICD bypass valve technology provides a solution by only allowing injected water into the formation but not in the opposite direction hence preventing downhole crossflow.
An AICD Bypass Valve was installed into two injection wells that were completed as Multi Zone Completions (MZC). It was planned that multiple zones would be opened simultaneously to provide ongoing waterflood support. The expectation was that the AICD technology would prevent crossflow between zones, due to pressure difference during any injection shutdowns, thereby eliminating the chance of formation failure and transportation of sand from the reservoir into the annulus and tubing.
At first, these reservoirs were produced through depletion, and waterflood of these reservoirs did not commence until reservoir pressures had reached between 5.5–7.0 ppg. This led to differential depletion across these reservoirs, hence increasing the risk of downhole crossflow immediately upon any injection shut down, which may come with sand production resulting injectivity impairment. This has been observed across other wells in the field not fitted with AICD Bypass Valves.
The concept of the AICD Bypass Valve is that the device requires a positive injection pressure differential to activate and open and allow fluid to pass through and it will close if the well is shut in due to a drop in the required pressure for valve activation. Since the installation of AICD Bypass Valves, these wells have been shut in multiple times due to platform shutdowns for rig mobilization and each time no drop in injectivity has been observed on restart, and sand production has been observed as predicted.
AICD Bypass Valves were installed into two new injection wells in the Nong Yao field, providing a low cost alternative to recompletion or redrill that may be required, if screens become plugged due to sand production triggered by crossflow between reservoirs during well shut-in. This has provided more reliable production for the field and the success in this project means that more AICD Bypass Valves will be planned for future MZC, for injection wells in unconsolidated sand.
HPHT Subsurface Barrier Valve Technology: Enhancing Reliability in Harsh Well Condition
Authors: Zhao Geng , Zuniga Jurgensen Camilo Eduardo , Batita Nabil , Arauz Grigory L , Battula Venkata Siva , Sharma Ashish , Perrotti Nicholas , Zhao Ming 
Published: November 2020

Abstract
Deeper reservoirs, higher pressures, higher temperatures, specific well fluids, uncertain debris-laden environments and demanding completion applications have presented very challenging well completion requirements. The reservoir subsurface barrier valve (SBV), which was developed from a fluid loss control concept, has become the key player to assist the operator to achieve safety and cost targets. In recent years, operational efficiency and overall project cost control have triggered a rapid field development approach, such as "fast-track" that includes batch drilling and batch completion. For this type of approach, the SBV is the core safety barrier for a long suspension while waiting for the production system to be ready. More and more stringent requirements on well operation safety continue to push the evolution of SBV development technology. The reliability and surveillance of the SBV, as one of the mandatory components of lower completions for subsea deepwater wells, is becoming more critical than ever for achieving a successful project.
This paper will focus on the innovation of SBV technology and will discuss in-depth the SBV design philosophy, special features and robust qualification process that ensures successful SBV performance in harsh well conditions.
Fiber Optic Liquid Leakage Detection
Authors: Baque Marc 
Published: November 2020

Abstract
Fiber Optic Liquid Leakage Detection (FOLLD) project has permitted of assessing on the DTS (Distributed Temperature Sensing), the DSTS (Distributed Strain and temperature Sensing) and the DAS (Distributed Acoustic Sensing) capabilities, combined with a Standard Telecom Fiber Optic Cable (FOC), for detecting small pressurized (20 barg) and pre-heated (50 °C) liquid (water) leakages on a buried dummy pipeline.
DTS/DSTS real time results demonstrated that the soil disruption, the soil and external environmental temperature variation had an influence on leakage detection results robustness. Consequently, only the first three (3) trials out of the ten (10) trials provide some comprehensive results.
Real time results shown that two (2) out of three (3) DSTS devices were able to detect a leakage up to twenty (20) km with a detection success rate of 67% in the position P1 or P2 or P3. One (1) out of two (2) DTS device detects up to twenty (20) km with a detection success rate that depends on the fiber optic position (67% for P2 or P3 and 33% for P1). Only one (1) DSTS shows a detection success rate up to fourty (40) km with a detection success rate of 67% in the position P1 or P2 or P3. DTS/DSTS real time results highlight that 100% successful detection rate can be reached up to twenty (20) km with several fiber optic cables in the position: P1/P2 or P1/P3 or P1/P2/P3. For DAS systems, the real time results shown that it was not possible to distinguish a leakage "noise" from other surrounding noise. This was especially due to the water pump noise, which was always occurring simultaneously with the leakage. Only a post processing treatment provide the leakage detection using a DAS technology.
Post treatment DTS/DSTS and DAS detection success rate are better than the real time results. This highlight the potential of both technologies.
All DTS/DSTS and DAS testing results demonstrated that the detection success rate are Supplier's dependent.
Successful Rigless Remedial Job Restores Production in a Mature Oil Field
Authors: Pelusi Luca , Lerose Marco , Pollio Pasquale , Spagnolo Salvatore , Scarso Marco , Mittiga Vincenzo , Appicciutoli Danilo , Caldarelli Riccardo , Vasquez Julio , Vitale Dario 
Published: November 2020

Abstract
The paper describes the execution of a successful rigless intervention solution deployed to establish well integrity and restore well production in a field in Europe. The paper will go through the planning and execution of the job highlighting the criticalities encountered and the overall benefit of the activity.
The approach used was a deep analysis of the well integrity issue from several prospectives. A new opportunity in the production optimization fluid has been identified. After an open discussion between Customer and contractor, a customized blend and detailed pumping procedure was agreed upon to reach the final scope. An injectivity test was executed to align the procedure to the reservoir and well behavior. Several pressure tests were performed to confirm the remedial job in place, and, after that, the well was re-perforated and opened to the production.
The horizontal well described in the paper, shut in during 2016 due to formation damage and depletion was selected for a new through tubing perforations interval by coiled tubing in the horizontal section. An unexpected firing head activation occurred at an unplanned depth, just above production packer. This event produced communication across the production tubing, liner, casing, and formation, which immediately induced total losses into the naturally fractured carbonate reservoir causing well control issues. The accidental perforations could have jeopardized the final scope of the project with no oil production and premature well abandonment. A successful rigless remedial intervention was immediately executed to restore well integrity by using a real time fiber optic coiled tubing acquisition and a new conformance fluid combined with a customized cement packer technique. The remedial intervention allowed the operator to resume the initial project's scope of opening the new perforation intervals, restoring an important oil production of about 150 [m3/d].
The final solution utilized a product with unique thixotropic properties which was then engineered for production optimization using a detailed pumping strategy. This novel approach added significant value, as the operator was able to restore the integrity of the well in rigless mode, avoiding the associated rig cost.