bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Top - Down View of Field Development Plans, Integrated Reservoir Performance and Production Sustainability Assurance
Authors: Tahir Sofiane , Al Kindi Salem , Ghorayeb Kassem , Haryanto Elin , Shah Abdur Rahman , Yersaiyn Saltanat , Su Shi , Ali Samad 
Published: November 2019

Abstract
Maturing giant and super giant fields have, typically, an extensive data set ranging from seismic data to time lapse surveillance data. The data set, associated studies and models together with driving values defined by ADNOC form the foundations of long-term plans, field development plans, business plans, reservoir management plans and production optimization plans. Ensuring the adequacy and optimality of the plans and their capability to meet their prescribed objectives is a very challenging task that requires unique assessment workflows. ADNOC is undertaking multiple fast-tracked Integrated Reservoir Performance and Production Sustainability Assurance (IPR) projects with the above objectives in mind. In this paper we will share the experience gained through the execution of such projects and the way this experience helped refining the workflows and the associated value.
We designed and applied unique workflows that combine "Bottom up" approaches by technical discipline with the "Top down" focusing on those factors that have the largest impact on the scope of the plans and the ability to deliver the expected outcomes. The identified issues and opportunities are presented in terms of their impact on volumes in place, reserves, facility, drilling plans, surveillance plans, modeling, etc. and are associated urgency indicators to help prioritizing actions.
The adopted integrated methodology and workflows helped in identifying and ranking various issues related to the reservoir models (static and dynamic) and many recommendations on how to tackle these issues in the new generation models were provided. Advanced reservoir management workflows were generated towards optimal production and injection balancing as well as to better manage the water flood and identify the most offending injectors. Many scenarios were explored to check the different elements of the full field development plan and ongoing projects, considering all the identified uncertainties. Many recommendations were provided, accordingly, concerning infill drilling and future gas lift program. Specific workflows were generated to optimize the performance of the existing gas lift wells and to identify and rank the future wells that will need gas lift according to their urgency, hence confirm the gas lift compression capacity that was subject of an ongoing project.
Key enabler to complete the project in the planed time frame was the use of cutting-edge modeling technology which has a drastic impact on the project and the team's capability to explore a comprehensive set of scenarios with associated sensitivities and uncertainty analysis providing unique insights towards more optimal decisions and clearer way forward.
Integrated Approach of Evolving Petrophysical and Formation Testing Measurements Improves Hydrocarbon Reserves of Laminated Clastic Formations in Kuwait
Authors: Chen Chao , Sassi Khaled , Ayyad Hazim 
Published: November 2019

Abstract
The characterization of the clastic Zubair reservoir is challenging because of the high lamination and the oil properties change making the conventional saturation technique uncertain. A new workflow has been recently established in the newly appraised wells which has involved advanced petrophysical measurements along with the fluid sampling. The new technique has led to identify new HC layers that were overlooked by the previous techniques, thus adding more reserves to the KOC asset.
Because of the high lamination of clastic Zubair formation and the change of the oil properties, the dielectric dispersion measurement was integrated along with the diffusion-based NMR to identify new oil zones that has been initially masked by the resistivity-based approach. The new approach has also provided details on the oil movability and the characterization of its property. As the newly identified layers were identified for the 1st time across the field, the fluid sampling was conducted to confirm the new findings.
The advent of a new logging technology from a multi-frequency dielectric technique deployed over the formation has independently pinned down the HC pays over the Zubair interval, including a new zone below the water column. The zone was initially identified as heavy Tar zone. The advanced diffusion-based NMR was thus conducted and integrated with Dielectrics which has demonstrated the movability of HC using the diffusion-based NMR approach over the newly identified zone. A fluid sampling was later performed which has confirmed the new finding. The new identified zone was initially overlooked by the previous interpretation and extensive modeling over the entire field. The seal mechanism was also explained by taking advantage of the high-resolution dielectric dispersion measurement (mainly the low frequency), which has been also supported by the Images interpretation. This new approach has added an incremental oil storage over the field.
A Generic Method For Acoustic Processing Using Deep Learning
Authors: Kisra Saad , Khadhraoui Bassem , Sable Sebastien , Lu Duc Duong Lam 
Published: November 2019

Abstract
A new method based on deep learning enables the extraction of formation compressional and shear slownesses from raw waveforms acquired by an acoustic tool regardless of its conveyance system or of its hardware configuration (number of axial receivers, waveform sampling rate, or number of time samples). The proposed approach is very fast, fully automated, and suitable for real-time processing workflows at the wellsite.
Over the years, a large collection of acoustic waveforms has been recorded and processed by experts in a variety of environments. In the proposed method, we apply a convolutional neural network (also known as ConvNet or CNN) to learn from previously processed data to estimate acoustic slownesses from raw waveforms. Because we use an algorithm that is originally designed for visual recognition, we transform the raw waveforms into images with enhanced characteristics that are directly associated with the acoustic slownesses that we are aiming to predict. For monopole waveforms, we were able to improve the prediction results using a short-term average/long-term average (STA/LTA) technique that enhances the main arrivals. We then train a CNN model with both the input images and the expected outputs (i.e., slowness values) on a large variety of data covering the main rock environments of interest. The CNN-trained model is subsequently used to estimate the slowness values from unprocessed waveforms never seen by the CNN model.
To test our method on real data, we gathered a collection of acoustic waveforms recorded by several acoustic tools in 20 wells, drilled in different fields across the world. The wells were drilled with different bit sizes (varying from 6 to 17.5 in.), and the compressional slownesses were ranging from 50 to 165 μs/ft and shear slowness ranging from 80 to 600 μs/ft. In total, we used 96,011 data points where each data point consists of a pair of a waveform array and the associated slowness value calculated by an acoustics expert. We then applied the CNN-trained model to a set of waveforms from validation wells, i.e. wells that were not part of the training dataset.
In these wells, previously unseen by the ConvNet model, the average absolute error between the slowness estimated using the CNN-trained model and the slowness calculated by an expert was less than 3 μs/ft, which is comparable to the error associated with state-of-the-art processing techniques for slowness estimation. We also discuss how our method can be extended to estimate shear slowness from dipole data using the same ConvNet. The deep-learning-based technique for slowness estimation that we describe can run extremely fast after the ConvNet training process is completed and provides good slowness results without prior information about the tool configuration or the environment in which waveforms are recorded. Because our technique is fully automated, it can also be used as an automatic quality control (QC) flag for wellsite processing and real-time operations.
Towards a Self-Serving Big Data Analytics Platform for Oil and Gas
Authors: Ocampo Alexis , Farah Gabriel , Gutiérrez Ernesto 
Published: November 2019

Abstract
Organizations today, especially in the oil and gas sector, are swimming in data [1] but most of them manage to analyze only a fraction of what they collect. To help build a stronger data-driven culture, many organizations, starting with technology companies, are adopting a new approach called self-service analytics. In this paper, we present our big data and analytics architecture that has enabled our in-house and outsourced engineering, and data science teams, to develop self-serving AI models and data pipelines. Our architecture is presented to the reader in such a way that he/she can apply it using the cloud provider of their choice by translating the concepts presented here, either to native cloud PaaS or using open source products.
Integration of Geophysics and Geology in a Complex Area, Gulf of Suez
Authors: de Vincenzi Luca , Ellen Helby , Khamis Anaam , van Dijk Johannes Petrus , Sabetian Raffaela 
Published: November 2019

Abstract
Dragon Oil (ENOC Group) is operator with 100 % of interest in the exploration East Zeit Bay (EZB) block, which covers a surface of 93 km2 in an area where producing oil fields are nearby; e.g.: East Zeit, Hilal, Ashrafi, Ashrafi SW, and Zeit Bay fields. The license commitment consists of a seismic acquisition and processing and the drilling of two exploration wells before September 18th, 2019, which corresponds to the end of the extension of the first exploration period. The EZB block is located in the Gulf of Suez (GoS) in an area characterized by very shallow waters, where two coral reef islands emerge during low tide and several obstacles are present, i.e.: an oil terminal, mooring buoys, a waiting area for tankers and fishing and touristic activity. In the period 1969-1984, some 240 km of 2D seismic were acquired within the block using different acquisition parameter sets and processed with inhomogeneous flow-charts generating a sparse 2D seismic grid of variable data quality. Some of these data were reprocessed even in PSTM and PSDM, but without any sensible improvement. Furthermore, 3D seismic surveys were also acquired in 1996/1997 and almost cover the entire surface of the EZB block: The Ashrafi Deep Water (DW) survey, obtained towing a streamer, has been recorded in the north-eastern part of the block and the central and western parts have been surveyed by the West Ashrafi campaign in Ocean Bottom Cable (OBC) mode. But this 3D is of very poor quality due to the screening effect of coral covering the sea bottom on the geophone component of the recording.
During the last four years, extensive multidisciplinary analyses integrating reprocessing of existing magnetic data with new acquired gravity data and new and reprocessed 2D and 3D seismic data, well log and dipmeter analyses were carried out by Dragon Oil to delineate drillable prospects. Key of the success in generating a valid interpretation was the idea to combine a well site survey, compulsory for rig location, with a 2D seismic survey in an area with restricted vessel maneuverability.
Ushering Operational Excellence Through Development and Operationalization of Closed Loop Action Tracking System
Authors: Choudhuri Amit , B. Mohd Jainal Mohd Saifunazim , B. Adenan Mustafa , Thanerajah Vivege Rajah 
Published: November 2019

Abstract
At all Field Operation Centers of PETRONAS Carigali Sdn Bhd (PCSB), multiple meetings are conducted on a daily basis and also on a periodic basis, based on the laid down standardized meeting Terms of Reference (TOR). Many short and medium term actions, opportunities and risks are identified during these meetings. Further, through continuous real time surveillance and routine reviews as well as detailed analytic exercises, multiple actions leading to opportunities for production gain and better managing risks are identified. These actions are assigned to various discipline focals with an understanding that they would be completed within an expected time duration. As has been the practice, logging and registering of these actions are being made through different means, using documentation process that are not standardized, and which leads to difficulty in effectively following up on these actions until their closure.
To ensure that all such actions are tracked and effectively closed within the target date as well as to enable a readily accessible "Lessons Learnt" data bank, a centralized and standardized closed loop action tracking web-based system was deployed by Operational Excellence department in collaboration with all Assets.
Waste Heat to Power System in Oil and Gas Industry Improves Plant Power Efficiency
Authors: Agahi Reza 
Published: November 2019

Abstract
Waste heat is the by-product of industrial energy usage. Approximately one-third of the energy consumed by the oil and gas industry is discharged as thermal losses into the environment or via cooling systems. And the main reasons for waste heat discharge are process inefficiencies and technology limitations in the conversion of thermal to mechanical energy. Nevertheless, because the oil and gas industry demands large amounts of thermal, electrical and mechanical energy, a huge amount of waste heat is subsequently available.
Organic Rankine Cycle (ORC) technology has made economical utilization of lower temperature heat sources possible. ORC's efficiency percentage for waste heat recovery varies between single digit to the mid-20s, depending on the waste heat source temperature and the cooling medium. Even the recovery of a few MW of thermal energy with a single-digit cycle efficiency for a plant consuming an average of 100 MW (134 102 hp) thermal energy is a considerable efficiency improvement. Studies by the Oakridge National Laboratory (USA) show that 75% of waste heat comes with sufficiently high temperatures (&gt; 150°C, or &gt; 302°F). This report projects a 2-5-year return of investment for ORC-based waste heat to power plant systems, which represents an attractive financial payback.
The recovery of waste heat from oil and gas operations remains mostly underutilized. Furthermore, economically feasible power generation from waste heat has been limited to medium- to high- temperature waste heat resources. This paper will explore technical solutions to these challenges facing the oil and gas industry
In this paper, three cases of waste heat from a gas turbine's exhaust flue gas are presented. The turbines have nominal output of 7.5, 15, and 25 MW (10 057, 20 115 and 33 525 hp) electrical power at an ambient air temperature of 15°C (59°F). A heat recovery unit (HRU) can recover thermal energy from exhaust flue gas. The heat recovery loop (HRL) could exchange thermal power with an ORC system, which in turn has the potential to produce electrical power. It will be demonstrated that this configuration has a HRL/ORC cycle efficiency of approximately 10% when the ambient air temperature is about 30°C (86°F).
Comparative Analysis of Gas Hydrate Inhibitors: Local and Conventional
Authors: Elechi Virtue Urunwo , Ikiensikimama Sunday Sunday , Ajienka Joseph Atubokiki , Akaranta Onyewuchi Emmanuel , Onyekonwu Mike Obi , Okon Okon Efiong 
Published: November 2019

Abstract
This paper takes a look at the performance of a Locally Sourced Material (LSM) in a laboratory mini flow loop of ½ -inch internal diameter, made from 316 stainless steel pipe sheathed in a 4-inch PVC pipe built on an external metal frame work. The performance of the LSM was measured with that of the conventional hydrate inhibitor 2-(Dimethylamino)ethylmethacrylate (2-DMAEM). The performance evaluation was based on Pressure versus time, change in pressure versus time and initial and final pressure versus time plots. These plots showed that LSM performed better than the conventional 2-DMAEM in all the weight percentages considered (0.01wt% −0.03wt %). The optimum weight percentage for inhibition was 0.02wt% with inhibition efficiency of 81.58% while that of 2-DMAEM was 73.68%. The inhibition efficiency for 0.01wt% and 0.03wt% of LSM wereboth 72.81% whereas that of 2-DMAEM were 51.75% and 76.32% respectively. The LSM is locally sourced, readily available in commercial quantity and also eco-friendly because it is plant based unlike the 2-DMAEM which is toxic and expensive. It is advised that the LSM be developed as an alternative to the conventional inhibitor for gas hydrate inhibition.
Protecting Assets, Protecting People: Exploring IoT Solutions to Drive Improved Asset Integrity and HSE Performance in Oil & Gas
Authors: Ventulett Thomas P. , Villegas Leigh M. 
Published: November 2019

Abstract
Under budget constraints, oil and gas organizations are turning to technology solutions to ensure greater asset integrity and improve operational safety. Purpose-built digital equipment, like mobile devices and IoT sensors that are certified intrinsically safe for hazardous environments, can do both.
Communication of precise/timely information in hazardous environments allows teams to make the best decisions possible, ensuring personnel follow safety procedures and processes meet regulatory requirements, while also driving improved performance. Accurate, real-time communication in combustible environments is only possible with intrinsically safe (IS) communications hardware, such as tablets and IoT sensors. These bring industrial applications directly into ATEX/IECEx Zone 1 hazardous areas to capture/share real-time information, making them more efficient, productive, and safe.
Performing inspection activities onsite with digital devices or IoT sensors reporting 24/7 helps organizations create live and dynamic workflows in real-time. Thus, maintenance, repairs, or further assessments can be optimally scheduled. Further, digital records validate compliance (i.e. location, date, time, pictures) while driving efficiency. Pervasive digital connectivity is expected to result in savings and improved asset integrity, securing healthier and safer operating environments.
We explore the potential benefits to asset integrity and HSE management from intrinsically safe technologies that help oil and gas teams communicate better. We look at use cases where personnel collect, share, and analyze IoT data in Zone 1 operations (industrial areas where ignitable concentrations of flammable gases, vapors or liquids are likely to exist under normal operating conditions), using devices that are certified to not ignite combustible environments. When such IS solutions are applied broadly across a hazardous area operation over a period of time, we expect them to help save time, improve accuracy, and monitor personnel's well-being so they can do their jobs better and more safely - and drive better results for their organizations.
The paper describes just some of the ways that IS-certified technologies have the potential for changing how oil and gas teams communicate, helping them drive better results through improved health, safety, compliance and efficiency. We suggest testing methodologies with IoT devices for organizations to establish their own HSE baselines that can be used for benchmarking improvements from the implementation of those devices. We also make predictions for future oil and gas operations based on ongoing development of IoT/machine learning technologies and call for further research and testing in realistic scenarios.
Geology Driven History Match of Eastern Siberian Halite Cemented Fluvial Reservoir
Authors: Ibragimov Ruslan , Ovchinnikov Aleksandr , Burdakov Dmitriy , Romantsov Aleksey , Sterlyagova Svetlana , Darmaev Bator , Zimin Sergey 
Published: November 2019

Abstract
History matching is a crucial step in process of gaining reliable model, which further will be used for production profile forecasting, well planning, choosing proper development plan of a field. Target reservoir for which it needed to construct and history match a model included several features that made this process quite challenging:
– high lithological heterogeneity due to alluvial genesis of rocks, – large influence of diagenetic alteration of sediments on reservoir quality, – impossibility of direct using of 3D seismic data because of low thickness of reservoir formation.
This work was successfully performed through complex application of following methods:
– building detailed facial maps based on conceptual depositional model, core and wireline log data from enormous number of wells, – using both well test and production data during adjustment of model for matching.
Concerning a key peculiarity of the reservoir – salt cementing – unique method of its modeling in both static and dynamic models was applied. This model also has the feature of changing salinization coefficient of reservoir during waterflooding. The basis for applying of salt cementing of reservoir is relationship found between wireline log and salinization coefficient from core data.
Assessing Structural Uncertainty Ranges in Static Models by Incorporating Structural Control Points from Horizontal Wells: A Case Study of a Middle East Carbonate Reservoir
Authors: Contreras Perez David Rafael , Al Zaabi Ruqaya Abdulla , Viratno Bernato , Sellar Christopher , Susanto Maria Indriaty 
Published: November 2019

Abstract
The rationale of structural uncertainty analysis in reservoir modeling is to quantify the range of probable Gross Rock Volume (GRV) s and searchfor the means to reduce this range as much as possible. This task considers running different scenarios and/or structural configurations based on the observed mismatch between structural depth estimation from seismic mapping and stratigraphic tops derived from well data. Integrated multi-disciplinary teams can collaboratively eliminate reservoir uncertainties at the well location, however uncertainty remains in the interwell area.
The challenge for any reservoir characterization team is to share expertise across disciplines in order to mitigate the lack of information with scientific reasoning. In this way the range of uncertainties impacting business decisions, development scenarios or data acquisition plans are minimised.
The workflow summarized here is an example of how to utilize structural elements from existing wells to quantify intrinsic GRV uncertainty while building static models. Offshore Field developments usually have a bigger horizontal well count than the ideal vertical penetrations and this case study is no exception in this case study. The ultimate goal of this publication is to generate the inputs required for a more realistic set of structural realizations that fulfil all of the current understanding from horizontal well placement and their intrinsic structural uncertainty.