bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improved Reservoir Characterization Through Rapid Visualization and Analysis of Multiscale Image Data Using A Digital Core Analysis Ecosystem
Authors: Chandra Viswasanthi , Tallec Gwenole , Vahrenkamp Volker 
Published: November 2019

Abstract
Efficient integration of multiscale image and petrophysical data is becoming increasingly important to tackle emerging reservoir characterization challenges associated with complex carbonate and unconventional reservoirs. In this paper we illustrate an integrated digital rock physics and petrophysical data analysis methodology empowered by a digital core analysis ecosystem, for defining reservoir rock types and flow units in a micritic carbonate formation. We apply the methodology to 35 meters of cored well data acquired from the Late Jurassic Upper Jubayla Formation, equivalent to the lower Arab-D reservoir in Saudi Arabia. Pre-processing, segmentation and digital rock physics calculations are performed using whole core computed tomography (CT), plug micro-CT, thin-section micrographs and scanning electron microscopy data. Further whole core CT data analysis includes generation of mean intensity and heterogeneity logs. The digital rock ecosystem is applied to these multiscale image data and to spatially correlate with petrophysical well logs. The unique whole core CT processing step in the workflow allows the core barrels to be intelligently removed, and all the cores to be stitched together regardless of the total size of data. We thus access the full advantage of 3D whole core CT data that provides significantly high vertical resolution of rock properties in the well interval. Furthermore, the live ecosystem enables the continuous integration of image and petrophysical data as they become available over the duration of this study. Results from digital image analysis reveal the micro- and macro-pore types and their connectivity across multiple scales. Combined with plug and thin section data, log interpretation and digital image analysis, these pore types are upscaled into well log scale through texture-based rock-typing. The digital core analysis ecosystem we employ in this study has a unique capability of visualizing and analyzing large volumes of image and petrophysical data, allowing a novel method for rock-typing. The proposed methodology is scalable to data sets consisting of many wells, thus making it a valuable tool for accurate characterization of complex carbonate and shale reservoirs, which are becoming increasingly reliant on high resolution imaging techniques for pore space characterization.
Unsupervised, Deep Learning-Based Detection of Failures in Industrial Equipments: The Future of Predictive Maintenance
Authors: Lygren Sture , Piantanida Marco , Amendola Alfonso 
Published: November 2019

Abstract

Objective/Scope
Despite tremendous efforts, AI-based predictive maintenance is still not fully exploited in Oil&Gas plants worldwide. The reason mainly relies on the fact that predictive maintenance algorithms need many examples of failures to be trained on, and this is not always the case. For this reason, we developed an efficient unsupervised approach for predictive maintenance, based on deep learning algorithms and applied successfully to predict and anticipate the failures of a coalescer of an Eni's offshore plant.

Methods/Procedures/Process
Our method is based on a Recurrent Neural Network (RNN) autoencoder architecture, coupled with clustering algorithms. The RNN is based on a combination of two algorithmic steps, respectively called encoder and decoder. The encoder reads multivariate chunks of data and summarizes them in a vector of fixed length, named context vector. Then, the decoder brings this context vector and reconstructs the input signals. Once the reconstruction error is minimized, we cluster context vector by choosing an optimal number of clusters and associating them to the operating conditions of the equipment, in particular by distinguishing ‘healthy’ from ‘faulty’ states.

Results/Observations/Conclusions
We applied the aforementioned workflow to distinguish the operating conditions of a small equipment in an Eni's offshore plant. This equipment, an electroastic coalescer, suffered repeated troubles during the first phases of plant start-up. We picked up all the sensor measurements available for the coalescer (pressures, levels, temperatures) with very tight sampling (10 seconds resolution) and trained the RNN architecture on 9 months of data. After the application of a suitable clustering method on the context vector minimizing reconstruction error, we were then able to detect up to 5 different operating conditions of the coalescer, associating them to healthy and faulty states of it. In particular, the method was able to authomatically cluster the failures periods of the coalescer, with an advance of around 4 hours before the failures occurred.

Novel/Additive Information
‘Effective unsupervised learning – learning without labelled data – remains a holy grail of AI’ (Andrew Ng, WIPO Technology Trends 2019, Artificial Intelligence). We tried to do a step forward in the application of unsupervised approaches to predictive maintenance of industrial equipments by developing an innovative deep learning based method and applying it to a coalescer of an Oil&Gas plant, getting results that are very promising for massive, large scale application in real production settings.
Innovations in 3D Finite Element Assessments of Well Integrity — A Case Study for an ADNOC Onshore Field
Authors: Al Hosani Mariam Ahmed , Masoud Rashad Mohamed , Al Beshr Huda Abdullatif , Latif Mohd Anwar , Al Hammadi Shamma Jasem , Khalil Ihab Nabil , Al Bairaq Ahmed Mohamed , Al-Ameri Ammar Faqqas , Nasreldin Gaisoni , Ni Qinglai , Rodriguez-Herrera Adrian , El Mubasher Husham Kamal , Corona Mauricio , Sinha Ravi Kumar , Subbiah Surej Kumar , Hussein Assef Mohamad , Karamalla Babikir Mubarak 
Published: November 2019

Abstract
The growing appreciation of the effects of production-induced stress changes on reservoir performance has concentrated the minds of many people on the potential value of using geomechanical modelling to predict and quantify these effects for making life-of-reservoir decisions—relating to compaction mitigation and completing new wells.
This paper is concerned with well integrity analyses for compacting reservoirs—focusing specifically on a new area of predictive geomechanical modelling realised using the finite element method. The innovative workflow presented offers significant improvements and, for the first time, captures some of the realities of the construction process. It takes into consideration both the formation and completions by integrating 3D near-wellbore geomechanical modelling with cementing simulations and casing integrity analyses. Specifically, casing eccentricity and cement contamination data are taken from numerical cementing simulations carried out to re-create the conditions in wells with different trajectories. Moreover, formation mechanical properties, pore pressure and stress states from the time of drilling till the end of a simulated production schedule are taken from a calibrated field-scale geomechanical model and subsequently used to create high-resolution 3D near-wellbore geomechanical models.
The case study presented in this paper concerns a giant onshore field with multiple stacked reservoirs—containing a variety of hydrocarbons and experiencing different levels of depletion. The main interest is in conducting comprehensive casing and cement integrity assessments—particularly for wells located in compacting reservoir zones. A persistent challenge for geomechanical modelling and prediction is the availability of calibration data. This paper reduces uncertainty by presenting results concerning sensitivity analyses for a variety of completion conditions—including different levels of casing eccentricity, different degrees of cement contamination and different extents of casing corrosion.
Advanced Reservoir Characterisation of Meandering Fluvial Environment, 3D Modelling Study Offshore Malaysia
Authors: Mohamad M Naim , Rahman M Zahari , Nourreddine Benayad , Yakup M Hamzi , Sedaralit M Faizal 
Published: November 2019

Abstract
Thorough reservoir modeling studies have been performed for field ABC, however there are still challenges to be addressed in modelling of some specific sand reservoir depositional systems i.e. meandering fluvial reservoirs (point bars and crevasse splays). The current modelling approaches especially for fluvial reservoirs are mainly controlled by wells and have contributed to uncertainties in lateral variation based on geostatistic (variograms etc) between and away from well control. Moreover, the existing modelling approach is using sixth to fifth order (lower order) hierarchical architecture elements and this project further refines the model up to third order (higher order) which enables capturing lateral accretion of point bars.
Advanced fluvial workflow (AFW) have been developed to improve the understanding of the reservoir architecture of fluvial reservoirs. It comprises of three main steps which are, first, details study on fluvial reservoir sedimentology characteristics derived from core analysis and literature. Second, qualitative geophysical study and interpretation derived from seismic dataset. Third, integration between the first and second steps into a three dimensional (3-D) reservoir model.
As a result of AFW implementation in field ABC, this has led to better representation of the reservoir heterogeneities, more accurate STOIIP assessment, improved history matching quality index (HMQI) and enhanced subsurface risks and uncertainties understanding. This enable optimization of future field development plan such as infill well reactivation, water flood and chemical enhanced oil recovery (EOR). The AFW is a robust modelling method that can be used in any reservoir modelling platform (PETREL, CMG, RMS, TNAV) with multiple realizations capability using automated workflows.
Natural Language Understanding for Safety and Risk Management in Oil and Gas Plants
Authors: Milana Diletta , Darena Maria Stella , Bettio Nico , Cerruti Chiara , Siliprandi Guido , Fidanzi Alessandra , Cerioli Paolo , Silvestri Giacomo , Tarasconi Francesco , Caserio Matteo , Botros Milad , Gabrielli Maria Luisa 
Published: November 2019

Abstract
In Oil and Gas, technical staff is daily involved in critical activities. Safety is therefore a key priority, even more so with frontier and continuously-updating technologies acting as a fundamental part of the transformation of the traditional industrial processes. While safety reports and investigations have long been adequately stored and continuously monitored by expert professionals, Artificial Intelligence applications to natural language now provide the opportunity to develop a decision support system capable of extracting insights, predicting the risk of future operations, performing scenario analysis and prescribing risk mitigation actions on massive amounts of data. In this work, we used an Open Innovation approach to develop a Safety Pre-Sense system, leveraging Machine Learning and Natural Language Processing techniques as well as incorporating multiple different (and often unexpected) sources of information.
Starting from standard Natural Language Processing tasks, we leverage linguistic patterns to build binary Document-Term Matrices. Operating on these Matrices, we implemented a Domain Keyword Extraction algorithm to extract words (or multi-words) that have high specificity.
Our pipeline also provides a language-agnostic method to detect similarities between documents written in different languages and cluster them accordingly, in order to obtain clear descriptors that can be used to understand their meaning. To do so, we map our text in a high-dimension vector space where we apply cluster analysis to group documents that are semantically close into consistent and multilingual groups. We then extract, for each language, a list of domain keywords that characterize every cluster.
Next, we identify similarities in the data in a completely data-driven manner, with the objective of extracting correlations between event features (such as geographical location and cause or type of event). As a result, we extract new aggregations of complex items such as severe Accidents or Work Processes. We also demonstrate how Correspondence Analysis and Pattern Mining algorithms are able to extract and visualize correlations between topics and events, leveraging a dynamic Qlik dashboard.
Finally, we point at additional sources of information, both internal and external to our company, that can be used to enhance our analysis.
Application of Machine Learning Tool to Separate Overlapping Fluid Components on NMR T2 Distributions: Case Studies from Laboratory Displacement Experiment and Well Logs
Authors: Rojas Pedro A. , Rincón Manuel , Netto Paulo , Coutinho Bernardo 
Published: November 2019

Abstract
Fluid typing, meaning fluid identification and quantification of each phase, is a significant challenge in NMR data postprocessing, particularly when the fluid spectral distributions overlap in one-dimension (T1, T2 spectra) or two dimensions (T1T2 or DT2 maps). Typical examples are extra-heavy oil and clay-bound water (CBW), heavy oil and capillary-bound water (BVI), free water and light oil or light oil-water and oil-base mud filtrate (OBMF). In these cases, technical limitations in data acquisitions and constraints in the inversion algorithms result in poor spectral resolution for those fluids with very similar physical-chemical properties. This makes very difficult the interpretation of NMR measurements from the laboratory as well downhole.
We present two case studies: one focused on determining water saturation (Sw) in core samples in a water-oil displacement experiment in the laboratory; the second is about determining the permeability by identifying OBMF from an NMR well log in a medium to light oil-bearing formation. In both cases the targeted fluid component was determined using blind source separation based on independent component analysis (BSS-ICA), which is a machine learning tool capable of separating spectral T2 components (sources) given their statistical independency.
The results from the displacement experimental show a high correlation (R2 higher than .85) between saturation from the BSS-ICA derived water component and the estimated value from known injected water volumes. In the well log case, the results show that the presence of OBMF and its volume are a good indicator of rock quality of the reservoir layers, as confirmed from several core measurements. Beyond this, the OBMF obtained from BSS-ICA decomposition is used as a key variable in a newly proposed permeability equation. After core calibration the OBMF-based permeability was found to be more representative than the permeability derived from the widely used Timur-Coates equation.
Assessment of Biodegradable Diverter for Acid Fracturing of Highly Fractured Formations
Authors: Al-Hulail Ibrahim , Eoff Larry , Anazi Mashhoor 
Published: November 2019

Abstract
Many application and operational methods have been developed for applying carbonate matrix acidizing to successfully stimulate heterogeneous and long horizontal openhole zones. These methods have also been implemented during acid fracturing to various degrees of success. This paper discusses in detail the laboratory assessment of a biodegradable material for acid diversion in highly fractured formations.
Diversion in fracture acidizing is extremely challenging because of the high pumping rate, extreme pressures, and larger volumes of acid compared to matrix acidizing. To effectively stimulate natural or pre-existing fractured formations, the diverting agent should be able to bridge not only at the perforations, but inside the fracture system, too. Historically, several methods have been implemented for acid-fracturing diversion, such as ball sealers, viscous fluids, packers, etc., resulting in limited success in formations with natural or pre-existing fractures. This paper discusses the use of an acid diverter that consists of biodegradable particles with different sizes and hardness. The particle size ratios are specifically designed where large particles will bridge in the fractures while the smaller particles "nest" in the pore throat of the bridged larger particles. This leads to quick, efficient blockage of fractures and acid diversion.
The laboratory assessment of this biodegradable material was conducted at various temperatures up to 300°F and consists of (1) degradation in 3% KCl, live 15 wt% HCl, and spent 15% HCl, and (2) fluid loss using slotted disks at different diverter concentrations. The fractures were mimicked in the laboratory using a stainless steel slotted disk in a high-pressure/high-temperature (HP/HT) cell.
The dissolution rate of the particles was observed to be a function of time and temperature. The dissolution rate of the diverter was higher in water as compared to 15 wt% HCl acid. The stability of the biodegradable diverter was conducted at 300°F. The filter cake was stable up to 30 minutes when 1.0 ppt of the biodegradable diverter was used.
The results of this study indicate that the biodegradable diversion material can be used as an effective alternative diversion method to seal natural or pre-existing fractures.
Alternative Environmentally Friendly Solvents for Asphaltenes/Paraffins Removal from Oil Producing Wells
Authors: Al-Taq Ali , Alfakher Basil , Alrustum Abdullah , Aldarweesh Sajjad 
Published: November 2019

Abstract
Aromatic-based solvents, including benzene, toluene, xylene (BTX) and their derivatives have been successfully applied for asphaltene removal from downhole and surface facilities. These solvents are considered non-environmentally friendly due to their associated health and safety concerns including high toxicity, low biodegradability and low flash point. Currently, more attention has been given in the oil industry to develop environmentally friendly asphaltene solvents.
This paper examines several environmentally friendly solvents derived from natural precursors to dissolve asphaltene, wax and combined asphaltene/paraffin organic deposit. A group of plant-derived and terpene-based asphaltene solvents with flash points ranging from 50.5 to 136 °C was examined in this study. Extracted asphaltene from a crude oil and wax obtained from distillation were used to assess solvency power of these solvents. Solubility of organic deposits containing more 42 wt% asphaltene with associated paraffin was evaluated in these solvents. The performance of these solvents was examined as a function of soaking time and temperature. These environmentally friendly solvents showed comparable solvency power to toluene. The lowest flash point solvent exhibited the highest solvency power for asphaltene while the opposite relation was observed for the wax sample. The lowest flash point (50.5 °C) solvent was able to dissolve 91 wt% of the asphaltene sample after soaking for 2 hours at ambient temperature compared with the highest flash point solvent (136 °C), which dissolved only 7.4 wt% at the same conditions. For wax, the solvent with the second highest flash point (132 °C) was able to dissolve 97 wt% of the wax while the solvent with a flash point of 50.5 °C was able to dissolve 85.5 wt% at ambient temperature and after 2 hours. Some of the examined environmentally friendly solvents showed very high dissolution power to organic deposits composed of asphaltene and paraffin where a solubility of 96 wt% was obtained at 80 °C and after a soaking time of 6 hours. The paper will discuss these results in detail.
Lean Logistics for Green Field Exploration Activities
Authors: Niccolini Federico , Chisari Luca Antonio , Maggioni Bruno 
Published: November 2019

Abstract
The necessity to reduce the costs incurred in the oil and gas project lifecycle pushes the oil Companies to evaluate alternative strategies with respect to the standard logistic approach. A case study for offshore logistic optimization and cost reduction is presented for a remote (138 nm from the operating port) deep-water offshore green field exploration project in Morocco (2018).
Green field exploration represents a challenging context of operations for logistics due to limited availability of already contracted services and/or infrastructures and possible limited familiarity with O&G of the hosting country. Following main elements of cost saving and optimization opportunities have been identified and pursued: i) exploitation of synergies with other company operated projects, ii) drillship storage capacity exploitation, iii) redeployment of material to reduce company stock and improve spud-in availability, iv) circular economy, v) transportation fleet sizing optimization, vi) flexible services and prompt release after operations conclusion.
In presence of a drillship deployed for multiple back-to-back projects, the possibility to load the rig with required materials for the spud-in of subsequent projects represents an opportunity for overall logistic optimization. For the presented case study, the contracted drillship has been loaded at Cyprus, after relevant exploration, with redeployment materials and consumables requested for the spud-in of Morocco exploration. Demobilization from Cyprus has been exploited as mobilization for the Moroccan campaign with the drillship reaching the well site in a status of readiness for spud-in.
The materials availability at rig site form the initial project stages determined an optimization of the related transportation fleet and the reduction below 5,000 sqm of the required storage areas footprint within a port that showed limited availability to dedicate existing storage areas to O&G activities. The marine base setup has been further optimized by implementing flexible contractual solutions foreseeing prompt release of unused services.
Moving ahead of the standard interpretation of the drillship as only the final delivery point of the supply chain, a new approach has been analyzed and implemented to make efficient use of the drillship's storage capacity by the means of the identification and exploitation of operational logistic opportunities. Overall logistics costs incurred for the Moroccan exploration campaign were of 6.5 M$, meeting the target of capital exposure minimization before commercial discovery and the additional target to avoid binding agreements for infrastructure usage and/or refurbishment.
Challenges with the Mega-Projects Development in Russia and Opportunities for International Cooperation
Authors: Akimova Irina 
Published: November 2019

Abstract
Oil and gas industry in the Russian Federation contribute more than 40% of federal budget revenues and thus its so crutial to find best solutions to its main challenges along with new opportunities for growth. One of the main challenges for the Russian oil and gas industry right now is to maintain and even develop its hydrocarbon resourse base in the regard of sustainable development of the country and its regions.
According to the latest data of the State balance of natural resources of the Russian Federation from 01 of January 2016, during 140 years of oil and gas commissioning in Russia 3454 fields were discovered, among them 2462 oilfields, 572 oil and gas fields, 420 gas and gascondencate fields. Leading production area in Russia is Western-Siberian oil and gas megaprovince.