bluuebunny/crossref_metadata_2025_split huggingface dataset:
Drilling in the Digital Age: Harnessing Intelligent Automation to Deliver Superior Well Construction Performance in Major Middle Eastern Gas Field
Authors: Goodkey Brennan , Hernandez Gerardo , Nunez Andres , Corona Mauricio , Atriby Kamal , Rayes Mohammed , Carvalho Rafael , Herrera Carlos 
Published: November 2020

Abstract
Over the past decade, breakthroughs in digital technology have rewarded a variety of industries with a step change in productivity and efficiency. Despite this, the drilling industry has yet to benefit on a large scale from these advances and a significant amount of value remains untapped. This paper details the effort of a service company to leverage modern technology by introducing a drilling automation system in pursuit of achieving a higher degree of consistency and efficiency.
The drilling automation system described in this case study was deployed in the Middle East on two onshore gas drilling rigs in 2019. The deployment was an opportunity to validate the potential of modern drilling automation technology and prove its ability to consistently deliver value. Since the company had extensive experience in the region, the Middle East was selected as the preferred location for field trials. This ensured that the value of automation could be precisely quantified as performance benchmarks were well documented and available for comparison. The automation strategy relied on an intelligent decision management system capable of dealing with constantly changing drilling conditions in order to implement efficient, consistent, and standardized well construction operations, while enhancing safety and reducing NPT. When given authority, the system would take control of the rig surface equipment to enable full automation of most drilling actions and engage optimization engines to monitor and adjust parameters to maximize performance. The system was leveraged to eliminate the variability innate to humans and deliver consistent results, while consolidating the improvements, performance gains, and lessons learned that otherwise would tend to disappear or erode over time or through personnel replacement.
Throughout this document, insight is provided into the technology itself, the deployment process, implementation challenges, the agile development model, and the results achieved. In addition, as the introduction of automation is a major departure from the traditional human operated drilling process, an emphasis will be placed on the results of the change management strategies utilized.
Optimizing and Digitizing Asset Reliability Strategies to Minimize Cost and Maximize Performance
Authors: Reeve Stephen 
Published: November 2020

Abstract
The principles and tools of Reliability Centered Maintenance (RCM) have been around for decades, and most organizations understand the key elements that underpin a sound Reliability program. They invest time and money into creating and implementing Asset Management and Reliability improvement initiatives, in the effort to avoid unexpected failures, outages, safety exposures and costs to meet business performance goals.
Why then, time after time, do we see organizations struggle to deliver performance improvements and realize value from their efforts?
Are you experiencing any of these problems? High maintenance costs and unplanned downtimeUncertainty in the quality of strategies across the asset baseNo line of sight from the tasks being executed in the field to reliability strategy decisionsCreating and deploying optimal maintenance strategies is an excessively time-consuming processChallenges in benchmark asset performance and measuring continuous improvementsInconsistent maintenance plans and Master Data
Implementing a Production Performance Management Platform for Better Production Data Management, Improved Surveillance and Forecasting for Mature Fields – A Case Study in Sharjah, UAE
Authors: Moustafa Mahmoud , Guillen Armando , Jany Abdulla , Jain Siddharth , Kumar Hemant , Kedia Rachit 
Published: November 2020

Abstract
In production engineering, managing a mature field, where cost and job complexity increases as the reservoirs further deplete, analyzing a vast amount of data from different sources is required to understand the well and reservoir performance on a timely basis. Analyzing the full picture of the production system behavior and planning timely interventions is the key in managing these fields. As the E&P organizations go through the production lifecycle, it is increasingly difficult to keep this data in a structured format and a place where it can be easily accessed by the end users for their day-to-day workflows. More and more production data end up in Excel spreadsheets and other repositories creating what is known as data entropy. Legacy corporate data archives become a bottleneck in efficient production data management as they cannot scale efficiently.
On the data analytics side, production and reservoir engineers often rely on Excel spreadsheets or inefficient home-grown systems which restrict their ability to derive more insights from the data. Then there is the collaboration aspect. Emails and shared-drives become the de-facto form of exchanging analysis and data, but these resources are time consuming and error-prone due to misplacement, duplication and different vintage of several data files.
It is contradictory that the E&P industry despite its steady advances with digitalization and adoption of the latest technologies such as Artificial Intelligence and Machine Learning (to mention a few) continues to struggle with these fundamental data management issues. The implementation of a robust production data management system and basic analysis workflows are key to enabling effective digital tools and must be a top priority for any oil and gas organization consolidating their digital journey.
This paper talks about the success case of implementing a production data management system for the mature assets of a National Oil Corporation, encompassing 37 years of production data from various scattered archives. The implementation of the system was customized to suit the requirement of the end-users to enable maximum utilization and faster adoption of the technology. The conceptualization, implementation, customization and development of this digital transformation initiative along with lessons learnt and future development possibilities is demonstrated in this case study.
A Proposed Operational Method for Safe and Quick Retrieval of Deepwater Drilling Riser During Extreme Hurricane
Authors: Abimbola Fatai Akorede , Yang Jin , Uche Onuoha Mac Darlington , Liu Shujie 
Published: November 2020

Abstract
An operational technique designed for safe and quick retrieval of deepwater drilling risers during an imminent threat from hurricane or typhoon is developed by proposing a new drilling riser model with disjointed sections for easier and faster disconnection as against the conventional practice of retrieving the whole length of the riser which is tedious, time-consuming and has a huge risk of untimely evacuation of personnel and facilities. A theoretical method is used to model the drilling riser in freestanding disconnected mode and the resulting differential equation is solved numerically. The mechanical behavior of the drilling riser is analyzed for its survivability in freestanding disconnected mode in order to ascertain its useability when the impending typhoon is over. Parametric studies are carried out on key influencing parameters such as buoyancy can upthrust ratio, buoyancy can (BC) position, and riser thickness to arrive at an optimum condition that will ensure the survivability of the drilling riser when disconnected in freestanding mode. Fifteen parametric case studies are presented. In the first five cases, with a varying buoyancy can upthrust ratio but a fixed BC position and riser thickness, it resulted to obtaining a suitable buoyancy can upthrust ratio which is used in subsequent cases. The next six case studies gave a suitable BC position, and finally the last four cases for riser thickness. Results obtained from the fifteen cases are used to get the optimum condition for survivability of the riser in freestanding disconnected mode. A deepwater drilling riser designed in conformity to the optimum configurational conditions as presented in the analysis can survive in the face of an emergency situation such as hurricane or typhoon.
Clean Fuel, Smart Enterprise & Economic — Blending Economic
Authors: Zhang Wei 
Published: November 2020

Abstract
With the increased awareness of pollutant emissions' effects on the environment, minimizing greenhouse gases and particulate matter from cleanup activities, whether they're fossil or alternative fuel based, is at the core of green remediation strategies.
Refiners today are increasingly focusing on environment protection by implementation of clean fuel oil campaigns. The product specifications are tighter, and the refinery margins decrease significantly with existing refinery setups. Hence, refiners must revamp units and expand their product portfolio; as well as, asses: Digitalization: converting the refinery to a "Smart Enterprise" to achieve operational excellence and utilization of existing plant setup to archive the regulation and market changes via smart discovery.Optimizing capital investments to efficiently meet future market demand.
Digital Blending NLP/MINLP blending application can lead to operation results with accurate property estimation and profit increase in the nonlinearization of blending model. The MILP blending model is developed to improve blending accuracy by reducing quality giveaway and increase profit by optimizing blend recipe. Combined with LP technology for planning and scheduling as well as automated reconciliation, this creates Digital Planning and Scheduling and enables informed decision making. Refinery mission is to timely supply the highest value products at a competitive cost through a competent and optimized work force, while maintaining world class safety, reliability and environmental protection. We are in the advanced stages of implementing a digitalization program that gradually incorporates process control, digital twin, planning, scheduling and blending. This paper focuses on refined product blending optimization.
Daily blending execution to reduce quality giveaway by considering blends in future long period with tight specification. Spot blending: evaluating opportunities to improve margins Evaluating new products based on existing setup. The results support clean fuel production initiatives.
Troubleshooting Gas Compression System Using Data Analysis
Authors: Al-Aiderous Abdullah 
Published: November 2020

Abstract
The objective of this paper is to showcase the successful and innovative troubleshooting data analysis techniques in one of the gas compression systems in upstream gas oil separation plants (GOSP-A). The gas compression system using gas compressors, dry gas seal systems and due point controls is used in almost all of upstream operation.
These proven data analysis techniques were used to tackle major and chronic issues associated with gas compression system operation that lead to excessive flaring, mechanical seal failures, and off-specification products. Dry Gas mechanical seals are an important key element in gas compression and its lifetime represents a concern to the operation personnel. Most gas compression systems have a mechanical seal lifetime of 2 years which in some cases limit production, increase the potential of unnecessary flaring and increase OPEX significantly.
In this paper, comprehensive data analysis of the potential root causes that aggravate undesired premature mechanical seal failures and off-specification gas products will be discussed along with solutions to minimize expected impact. For example, improper gas conditioning that feeds dry gas mechanical seals increase the risk of premature failures. Additionally, improper product specification in some applications have been found to promote seal failures and incur additional flaring which is both costly and environmentally undesirable. In addition, after extensive analysis improper operation practices during compressor startups, steady state operation and gas conditioning have been linked with premature compressor failures, product off spec and safety device failures.
The field trial proved the effectiveness of the proposed innovative troubleshooting data analysis techniques in reinstating the gas compression unit in GOSP-A to its recommended design conditions, eliminated compressors and mechanical seal failures and avoided the off-specification products at the lowest operating cost. This innovative technique was based on deep and extensive process data analysis, evaluating operating and design data, reviewing international standards, benchmarking against other facilities, process simulation using Hysys, and finally the actual field trial.
Fluid Properties on the Main Path of Establishing Accurate Flowrates Measurement with Multiphase Flowmeters
Authors: Pinguet Dr Bruno , Kuczynski Konrad 
Published: November 2020

Abstract
Any flow metering devices provide the information at the meter conditions (i.e. line pressure and temperature). Still, the value and the volumes of water, oil, and gas need to be estimated at standard conditions. To convert them from the meter conditions to standard conditions, this requires additional information. In other words, measuring the flowrates requires knowledge of fluid properties (PVT) and phase transfer following pressure and temperature, and this is irrespective of the type of meter or technology used.
Multiphase and Wet Gas Flowmeters (MFMs) are particularly affected by the use of the PVT information for two main reasons. First, they can be working at very high pressure and temperature, which requires a significant correction to convert the measured flowrates to standard conditions. Second, because no separation is made, a large amount of gas can be dissolved inside the oil, or some of the condensates could be in gas phases at meter conditions when liquid at standard conditions.
A lot of published documents are showing the flowmeter performances under well-controlled conditions, but little publications are available about the impact of the fluid properties. The paper describes, first, the solution to adopt to evaluate the overall meter performance which is generic to all MFMs; second, explain that this cannot be addressed by any reference facilities where MFMs are tested due to the versatility of the produced fluids, third that using such miscible fluids lead to evaluate the MFM performance improperly at line conditions and worst leading to weak conclusions
The information necessary for the statement of the overall uncertainty performance is derived from a model; it is a generic set of information based on fluids properties, shrinkage and dissolution factors⋯ This paper will show that using a combination of this information, the flowrates at line conditions can be converted to standard conditions for any fluid and flow conditions. The use of these data with the intrinsic performance of the MFM provides the overall uncertainty expected by the oil and gas operators to estimate the performance of the well or reservoir.
If the oil and gas companies more accept the MFMs and the level of performance of the latest flowmeters are improving drastically. The PVT or fluid behaviour package cannot anymore be "hidden". The benefit is, with the use of the PVT and MFM performance, to provide the global uncertainty statement with a proper quantification of these cumulative uncertainties with the possibility for the end-user to review the performance of each MFM and how to keep them in the best working conditions in the field operations
Real-Time Measurement of Drilling Fluid Rheology and Density using Acoustics
Authors: Ofoche Paul , Noynaert Samuel 
Published: November 2020

AbstractThis paper presents a novel approach of continuously measuring drilling fluid rheology and density by use of sound signals. A unique apparatus is built with a series of pipe sections designed to exact pre-calculated dimensions to achieve equivalent standard shear rates as stipulated in the American Petroleum Institute (API) Recommended Practice 13D for measuring the rheology of oil-well drilling fluids (from 3 to 600 RPM). Acoustics waves are passed through the fluids of interest and their interaction is recorded and analyzed to deduce the density and rheological properties of the fluids.The concept of resonance as demonstrated by the Barton's pendulums are the basis of the methodology. Sound signals are known to exhibit a damping effect when passing through various media. Pairs of sensors are employed in this set-up and their signal response are first characterized and calibrated with fluids of known properties. Electric current is converted into acoustic signals by piezoelectric sensors mounted of the flowline which are then emitted through the fluids desired to be measured without interrupting the flow. A matching sensor receives these damped signed and reconverts them back to electromotive potentials for recording by a data acquisition unit. The signals are then analyzed by applying statistical techniques to interpret and obtain the fluids physical properties.Owing to the nature of the task, the goal of accurately achieving simultaneous measurement of density and viscosity is attained by applying an ensemble machine learning algorithm, known as Multivariate Random Forest. Pure chemicals and fluids of known properties form the training group on which the predictive model is built for subsequent testing on new mud samples flowing through each section. The pipe sections generate shear rates covering the standard range adopted in oilfield reports. Results from each pair of sensors are analyzed and compared with dial readings from rotational viscometers; these have shown to be within a narrow band of error.As a result of this work, the voltage outputs are sent continuously and in real-time to a processing computer that converts the values to dial readings at standard shear rates, while not disrupting the flow. This can aid in the better monitoring and surveillance of the entire fluid system of the well, which is highly beneficial to well control. The system can also be arranged to acquire gel strengths or how the fluid behaves after a fixed period of rest. Improvements can be made on the current procedures for fluid characterization which have remained relatively static for many years. This work engages the disciplines of rheology, acoustics and machine learning, creating a mechanism for continuous and real-time drilling fluid surveillance critical to the enhancement of safe development of petroleum resources.
Visual Inertial System for ROV Positioning
Authors: Marques Filipe , Castro Filipa , Parente Manuel , Costa Pedro 
Published: November 2020

AbstractROV localization is essential for subsea operations and is commonly achieved by using acoustic sensors such as ultra-short baseline (USBL) and long-baseline (LBL). These systems are costly and prone to errors in specific scenarios, for example: operations in shallow waters or in proximity to subsea structures. In these scenarios, the operator may experience shadow areas where the accuracy will be compromised or position estimation is not possible at all.Since a significant number of subsea inspection operations are performed under these circumstances, we propose a solution to estimate the movement of the ROV from its live video feed, providing a real-time estimation for ROV position in any scenario. The neural network returns motion estimation and orientation, acting as an inertial navigation system that is able to combine with correct, or replace the acoustic sensors position estimation.The solution proposed in this paper was tested in a real subsea operation where acoustic sensors were not accurate. We describe the use case and how, with our solution and an in-house simulator, we were able to monitor the operation and create a replay of the entire mission in a simulated environment. We show that our solution is effective at estimating the ROV trajectory during the entire operation. Our method can be used to improve the accuracy of acoustic sensors and to replace them in situations where they are not able to operate, and, later on, we can see the entire operation in our digital twin.
Geomaterial Microfluidics for the Visualization of Alkaline Surfactant Polymer Flooding
Authors: Alzahid Yara , Alghamdi Fahad 
Published: November 2020

Abstract
To fully understand the flow mechanisms in oil reservoirs, analysis at pore-scale is required, as it is the length scale at which capillary-trapped oil is mobilized. In particular, analysis at this length scale would help in developing efficient recovery methods to achieve the best possible oil recovery. In this paper, we use a novel geomaterial microfluidics device that couples the surface chemistry and mimics a real sandstone rock to analyze Alkaline Surfactant Polymer (ASP) flooding at the pore scale. An experimental micro-scale approach was developed utilizing geomaterial micromodels for analyzing oil recovery. The geomaterial micromodel is fabricated using soft lithography and polydimethylsiloxane (PDMS) as the main material. Then, to functionalize the PDMS devices, the relevant minerals for sandstone (Quartz and Kaolinite) are added to create the geomaterial micromodel device. To study the effect of minerals presence on oil recovery during ASP flooding, oil recovery is measured using geomaterial microfluidics, as well as plain PDMS microfluidics device that has the same porous media pattern. The results show the different displacement patterns and oil recovery achieved by waterflooding and followed by ASP flooding, which is the typical practice in any enhanced oil recovery method. Results showed oil recovery of 65 % followed by 69 % for waterflood and ASP, respectively in a geomaterial device. In the plain PDMS microfluidics device, an oil recovery of 75 % followed by 80 % for waterflood and ASP, respectively. The geomaterial microfluidics chip provided a more realistic oil recovery values compared to the plain PDMS chip. Therefore, using this novel geomaterial microfluidics fabrication method creates an ideal visualization platform to study the pore scale of any given flood, as it captures the surface chemistry, roughness, and pore networks present in actual rocks. This would ultimately help develop and optimize oil recovery mechanisms.
Downhole AOP Measurements Applied to Mobile Oil in a Tar Mat: A Study on the Influence of Contamination
Authors: Tiwari Sanhita , Chen Chao , Al-Dhuwaihi Abdul-Aziz , Baroon Bodoor , Alabbas Reem , Ayyad Hazim , Sullivan Matthew , Smythe Elizabeth , Dumont Hadrien , Harrison Christopher 
Published: November 2020

Abstract
A downhole fluid analysis prototype module (MRPV) has been developed which measures both asphaltene onset pressure (AOP) and bubblepoint pressure (Pb). This manuscript describes one of many recent downhole jobs which were part of a study to characterize live crude oil in real time while sampling mobile oil in a permeable tar mat. These jobs support an enhanced oil recovery (EOR) study to understand the best way to extract bypassed oil (Fatma 2019). In this manuscript, downhole bubblepoint and AOP measurements made with the MRPV in well RA-0Y37 agree with those obtained in a laboratory with bottomhole samples. The effect of oil-based mud contamination on the AOP and Pb is examined during clean up while sampling. Water was present for all jobs, but the hydrophobic membrane blocked water from entering the MRPV, facilitating ease of interpretation of optical data. This manuscript comprises the first part of a two-part series (Tiwari 2020).