bluuebunny/crossref_metadata_2025_split huggingface dataset:
Calcul des Unités Gros Bétails : proposition d’une méthode basée sur les besoins énergétiques pour affiner l'étude des systèmes d'élevage
Authors: BENOIT Marc , VEYSSET Patrick 
Published: October 2021

Originellement basé sur l’ingestion de fourrages par les herbivores et par extension sur leurs besoins énergétiques, le concept d'Unité de Gros Bétail (UGB) est largement utilisé, sans être toujours précisément défini, pour décrire et évaluer les systèmes d'élevage ou pour calculer des statistiques. Cependant, le calcul de la valeur UGB d'un animal donné peut être considéré comme très frustre dans la mesure où il ne tient compte ni de sa taille, ni de son niveau de production, ni de ses conditions d'élevage, dont dépendent ses besoins énergétiques et donc alimentaires. Nous proposons une méthode de calcul de l'UGB adaptée aux espèces bovines, ovines et caprines basée sur les besoins énergétiques nets des animaux, en utilisant les équations et paramètres fournis par le GIEC (« Groupe d'experts Intergouvernemental sur l'Évolution du Climat »). Grâce à une analyse de sensibilité vis-à-vis des variables utilisées, nous confirmons que le poids des animaux et leur niveau de production sont déterminants de ces besoins. En intégrant ces deux paramètres, nous proposons deux types d'équations qui diffèrent par leur niveau de précision et par la disponibilité des données requises. Sur la base d'une équivalence de 29 000 MJ pour une UGB correspondant à une vache laitière de 600 kg produisant 3 000 kg de lait par an à un taux butyreux de 4 %, nous conservons la notion d'animal de référence pour une UGB. La méthode proposée permet d’approcher, à travers la taille des animaux, leur niveau de production et leur génotype, la variabilité des besoins alimentaires des animaux. Les coefficients UGB ainsi calculés peuvent très significativement différer des coefficients historiques et ainsi modifier les conclusions de comparaisons de systèmes de production entre eux ou dans le temps.
Assessment of Seismic Risk in Geothermal and Hydrocarbon Reservoirs Using an Exact Analytical Solution of Stress Change
Authors: van den Hoek Paul Jacob , Poessé Jorik Willem 
Published: October 2021

Abstract
Both for the oil & gas and geothermal industry, induced seismicity caused by field development and operation can pose a risk, in particular when the reservoir (or overburden / underburden) is intersected by faults. The mechanisms by which faults can be reactivated (potentially leading to seismicity) include pressure effects (reservoir depletion, or pressure rise over large areas as a result of injection) or thermal effects (cooling such as in geothermal operations or heating such as in steam flooding).
Earlier, we proposed a simple methodology to assess seismic risk for geothermal reservoirs that can also be applied to hydrocarbon reservoirs. This methodology uses an elastoplastic finite element model of the reservoir in question. However, its application turned out to be laborious. Therefore, we developed an exact analytical solution for the stress changes induced by cooling, depletion and /or pressurization along (a) representative fault(s). This solution is a generalisation of the Goodier analytical solution for the situation of non-vertical faults.
The analytical solution can be used to quickly evaluate a number of different scenarios related to temperature and /or pressure distributions in the reservoir. In the case of fault activation, maximum fault displacements (slip) can be computed by linking the results to elastic finite element calculations for similar load conditions. Using published standard correlations, the seismic magnitude can subsequently be estimated from the computed fault displacements.
The analytical model was applied to different fault geometries, reservoir temperature distributions and depletions. It turns out that certain fault geometries (dip angles, offsets) are far more prone to activation than other fault geometries. An explanation of this result is provided. Furthermore, for non-critically stressed faults, the risk of activation is far less for geothermal operations than for situations where large parts of the reservoir are depleted or pressurized. This can be explained by the fact that the extent of the cooled zone in geothermal operations is generally limited, even after 30 years of operation. Consequently, cooling-induced stress changes along the fault are significantly reduced because of arching by the adjacent non-cooled areas.
Finally, one geothermal field example in The Netherlands is presented where the above methodology was applied to demonstrate that there exists no seismic risk over the entire field life.
A Practical Approach to Expedite a Pore to Process Simulation Model for a Rich Gas Condensate Reservoir – Case Study
Authors: Mohamed Mohamed Ibrahim , El-Menoufi Ahmed Mahmoud , Ezz El-Regal Eman Abed , Ali Ahmed Mohamed , Mansour Khaled Mohamed , Negm Mohamed Nagy , Hussein Hatem Mohamed 
Published: October 2021

Abstract
Field development planning of gas condensate fields using numerical simulation has many aspects to consider that may lead to a significant impact on production optimization. An important aspect is to account for the effects of network constraints and process plant operating conditions through an integrated asset model. This model should honor proper representation of the fluid within the reservoir, through the wells and up to the network and facility.
Obaiyed is one of the biggest onshore gas field in Egypt, it is a highly heterogeneous gas condensate field located in the western desert of Egypt with more than 100 wells. Three initial condensate gas ratios are existing based on early PVT samples and production testing. The initial CGRs as follows;160, 115 and 42 STB/MMSCF. With continuous pressure depletion, the produced hydrocarbon composition stream changes, causing a deviation between the design parameters and the operating parameters of the equipment within the process plant, resulting in a decrease in the recovery of liquid condensate. Therefore, the facility engineers demand a dynamic update of a detailed composition stream to optimize the system and achieve greater economic value. The best way to obtain this compositional stream is by using a fully compositional integrated asset model.
Utilizing a fully compositional model in Obaiyed is challenging, computationally expensive, and impractical, especially during the history match of the reservoir numerical model.
In this paper, a case study for Obaiyed field is presented in which we used an alternative integrated asset modeling approach comprising a modified black-oil (MBO) that results in significant timesaving in the full-field reservoir simulation model. We then used a proper de-lumping scheme to convert the modified black oil tables into as many components as required by the surface network and process plant facility. The results of proposed approach are compared with a fully compositional approach for validity check. The results clearly identified the system bottlenecks. The model can be used to propose the best tie-in location of future wells in addition to providing first-pass flow assurance indications throughout the field's life and under different network configurations. The model enabled the facility engineers to keep the conditions of the surface facility within the optimized operating envelope throughout the field's lifetime.
Systematic Application of Pressure and Temperature Transient Analysis in an Oil Field: A Case Study
Authors: Muradov Khafiz , Dada Akindolu , Djabbarov Sultan 
Published: October 2021

Abstract
Pressure Transient Analysis (PTA) methodology has long enabled well testing to become a standard routine. Modern, well and reservoir monitoring and management practices are now unthinkable without the well test-derived estimates of KH products, skin factors, radii of reservoir boundaries, etc. Temperature data, measured together with the pressure, is widely available. Multiple methods for Temperature Transient Analysis (TTA) have also been developed, but have not yet gained due recognition.
Few examples of a systematic application of PTA and TTA (or, in general, Pressure and Temperature Transient Analysis PTTA) on a field scale have been published. Given that the TTA radius of investigation is much smaller than that for PTA, the TTA tends to explore the near-wellbore properties including the near-wellbore permeability profile, depth of damage, multi-layer parameters, fluid properties, etc. This complements the far-field estimates made by PTA, resulting in the PTTA providing a more holistic and complete picture of the state of the reservoir and fluids around the wellbore.
This work demonstrates a case study of a systematic application of PTTA methods to wells in a green, oil field. The wells are equipped with a state-of-the-art, downhole, permanent monitoring equipment. A user-friendly, bespoke toolbox has been developed to carry out PTTA analysis in this field. Dozens of transient events that occurred in the first few years of the field production life have been analyzed using PTTA. There are multiple examples of this PTTA analysis demonstrating improved characterization of the reservoir, near-wellbore, fluid, and multi-layer properties.
This work will be insightful to those looking to find out what additional, useful information (like reservoir and fluid properties) can be extracted from the traditional well-test, transient pressure and temperature measurements at no extra cost.
Study on Formulation with Lower Initial Viscosity Gel/Alkali/Surfactant/Polymer After Polymer Flooding and Scenario Design of Field Test
Authors: Cheng Cheng Jie , Liu Bo Hai , Gao Qian , Yan Wei , Chen Lin Wen , Liu Chao Guo , Zhou Quan , Han Hui Pei 
Published: October 2021

Abstract
The blocks of polymer flooding have gradually entered into the stage of chase water flooding after polymer flooding in Daqing Oilfield and the comprehensive water cut is close to the exploitation limit of 98%. So it is urgent to develop some new technologies to further enhance oil recovery after polymer flooding. On the basis of laboratory research, a field test of alkali/surfactant/polymer flooding was carried out after polymer flooding in Daqing Oilfield in 2015, which achieved good development effect, but the polymer concentration was relatively large. Based on the field test of alkali/surfactant/polymer flooding, a new technology of the lower initial viscosity gel/alkali/surfactant/polymer flooding has further been studied according to the technical route combining plugging, adjusting and displacing, which can reduce the polymer dosage greatly under the premise of ensuring good oil displacing effect. In this paper, some laboratory studies are carried out, which realize significant technology breakthrough. Firstly, the adjusting and plugging agent of lower initial viscosity gel is screened out, which can be injected into the high permeability layers of low flow resistance like the water and migrate to the deep location of the high permeability layers and then gelatinize on spot. Therefore it can plug high permeability layers effectively and does not pollute the middle and low permeability layers at the same time. Secondly, the injection parameters of lower initial viscosity gel/alkali/surfactant/polymer system are optimized. The results of laboratory experiments show that the lower initial viscosity gel/alkali/surfactant/polymer system can enhance oil recovery by 13.5% OOIP under the optimal injection parameters, which is 1.2% OOIP more than that of the alkali/surfactant/polymer flooding and can save polymer dosage by 17.8%. In view of the good results obtained in laboratory experiments, the scenario design of field test is carried out and the incremental oil recovery is 10.1% OOIP predicted by numerical simulation. The field test is expected to start chemical flooding in 2021.
Season Cycling Gas Storage in Stogit Fields. A Real-Time Data Transmission System
Authors: Naveda Graciela Eva , Louie France Dominique , Locatelli Corinna , Davard Julien , Fragassi Sara , Basile Alessio , Delon Emanuele 
Published: October 2021

Abstract
Natural gas has become one of the major sources of energy for homes, public buildings and businesses, therefore gas storage is particularly important to ensure continuous provision compensating the differences between supply and demand.
Stogit, part of Snam group, has been carrying out gas storage activities since early 1960's. Natural gas is usually stored underground, in large storage reservoirs. The gas is injected into the porous rock of depleted reservoirs bringing the reservoir nearby to its original condition. Injected gas can be withdrawn depending on the need. Gas market demands for industries and homes in Italy are mostly guaranteed from those Stogit reservoirs even in periods when imports are in crisis.
Typically, from April to October, the gas is injected in these natural reservoirs that are "geologically tested"; while from November to March, gas is extracted from the same reservoirs and pumped into the distribution networks to meet the higher consumer demand.
Thirty-eight (38) wells, across nine (9) depleted fields, are completed with downhole quartz gauges and some of them with fiber-optics gauges. Downhole gauges are installed to continuously measure and record temperature and pressure from multiple reservoirs.
The Real Time data system installed for 29 wells is used to collect, transmit and make available downhole data to Stogit (Snam) headquarter office. Data is automatically collected from remote terminal units (RTUs) and transferred over Stogit (Snam) network. The entire system works autonomously and has the capability of being remotely managed from anywhere over the corporate Stogit (Snam) IT network.
Historical trends, including fiber optics gauges ones, are visualized and data sets could be retrieved using a fast and user-friendly software that enables data import into interpretation and reservoir modeling software. The use of this data collection and transmission system, versus the traditional manual download, brought timely data delivery to multiple users, coupled with improved personnel safety since land travels were eliminated. The following pages describe the case study, lessons learned, and integrated new practices used to improve the current and future data transmission deployments.
The Potential Impact of Surfactant and Polymer Production on Separated Water Quality
Authors: Almorihil Jawaher , Mouret Aurélie , Marsiglia Marie , Mirallès Vincent , AlSofi Abdulkareem 
Published: October 2021

Abstract
In previous work, we demonstrated that EOR chemicals had minor effect on topside processes in terms of separation, corrosion and scale inhibition. Regarding the oil/water separation, the most noticeable effect was a deterioration in separated water quality that was deemed manageable. This paper will further investigate the impact of produced polymer and surfactant on the quality of separated water.
To mimic the separation plant potential feed and operations, experimental work has been carried out by preparing oil/brine mixtures at different surfactant/polymer concentrations with oilfield additives. Three main parameters have been varied: surfactant/polymer (SP) concentration, temperature, and water cut. The final test matrix consists of 24 tests. We first assessed the impact of EOR additives on the type of generated emulsions (O/W or W/O). Then we performed bottle tests taking into account the different operating parameters in order to investigate the kinetics of water/oil separation. Finally, we carried out physical-chemical analyses on the separated water in order to evaluate its quality.
In terms of concentration effects, the results suggest that SP concentration had minimal impact on pH and density of the aqueous phase. Bottle tests showed that phase inversion was obtained at intermediate and high SP concentrations for both water cuts. In addition, separated water quality deteriorated in systems of intermediate SP concentration and slightly improved at high concentration at 32 °C. At 54°C, higher SP concentrations resulted in poorer water quality. Kinetics of separation accelerated with higher SP concentrations. In terms of temperature effects, a slight decrease in both viscosity and density of emulsions was observed at higher temperatures. Kinetics of separation also improved with higher temperatures, as did the quality of the separated water. In terms of water cut effects, viscosity and density of the aqueous phase were not impacted. Moreover, phase inversion of the emulsion (from water-in-oil to oil-in-water) occurred when water cut increased from 75 to 85% without SP. With SP, oil-in-water emulsions were observed for both water cuts. Kinetics of oil/water separation increased with the higher water cut; however, no clear tendency on water quality was observed. with water cut.
In conclusion, we reconfirm that SP production, at least for the investigated formulations, will have a negligible effect on separation. The result will lead to deterioration in separated water quality; however, the level of deterioration is manageable and would not affect conventional practices of disposal in oilfields for pressure maintaining purposes. At last, this study layout laboratory protocols to perform such process-assurance.
The Necessity for Collaboration Between Data Scientists and Domain Experts
Authors: Bangert Patrick 
Published: October 2021

Abstract
A practical data science, machine learning, or artificial intelligence project benefits from various organizational and managerial prerequisites. The effective collaboration between various data scientists and domain experts is perhaps the most important, which is discussed here. Based on practical experience, the principal theses put forward here are that (1) data science projects require domain expertise, (2) domain expertise and data science expertise generally cannot be provided by the same individual, (3) effective communication between the various experts is essential for which everyone requires some limited understanding of the others’ expertise and real-world experience, and (4) management must acknowledge these aspects by reserving sufficient project time and budget for communication and change management.
Electrical Submersible Pump Prognostics and Health Monitoring Using Machine Learning and Natural Language Processing
Authors: Ambade Amey , Karnik Saniya , Songchitruksa Praprut , Sinha Rajeev Ranjan , Gupta Supriya 
Published: October 2021

Abstract
Electrical Submersible Pump (ESP) systems efficiently pump high volumes of production fluids from the wellbore to the surface. They are extensively used in the oil and gas industry due to their adaptability, low maintenance, safety, relatively low environmental impact. They require specific operating conditions with respect to the power, fluid level and fluid content. Oilfield operation workflows often require extensive surveillance and monitoring by subject-matter experts (SMEs). Detecting issues like formation of unwanted gas and emulsions in ESPs requires constant analysis of downhole data by SMEs.
The lack of adequate and accurate monitoring of the downhole pumps can lead to low efficiency, high lifting costs, and frequent repair and replacements. There are 3 workflows described in the paper which demonstrate that the maintenance costs of the ESPs can be significantly reduced, and production optimized with the augmentation of machine learning approaches typically unused in ESP surveillance and failure analysis.
Downtime due to ESP failures cause loss of production, disrupt logistics planning and require weeks for replacement. Pull-out due to poor reservoir conditions complicates the inventory issue. Therefore, it is desirable to reduce workover time and optimize production by providing early warning of ESP failures (Fig. 1).
AI for Production Forecasting and Optimization of Gas Wells: A Case Study on a Gas Field in Oman
Authors: Al Lawati Mohammed , Thatcher Jimmy , Rehman Abdul , Gee Ivan , Eldred Morgan 
Published: October 2021

Abstract
Oil & Gas extraction companies are using a vast amount of capital and expertise on production optimization. The scale and diversity of information required for analysis is massive and often leading to a prioritization between time and precision for the teams involved in the process. This paper provides a success story of how artificial intelligence (AI) is used to dynamically and effeciently optimize and predict production of gas wells. In particular, we focus on the application of unsupervised machine learning to identify under different potential constraints the optimal production parameter settings that can lead to maximum production. A machine learning model is supported by a decision support system that can enhance future drilling operations and also help answer important questions such as why a particular well or group of wells is producing differently than others of the same type or what kind of parameters that work on different wells in different conditions. The model can be advanced to optimize within field constraints such as facility handling capacity, quotas, budget or emmisions.
The methods used were a combination of similarity measures and unsupervised machine learning techniques which were effective in identifying wells and clusters of wells that have similar production and behavioral profiles. The clusters of wells were then used to identify the process path (specific drilling and completion, choke size, chemicals, etc processes) most likely to result in optimal production and to identify the most impactful variables on production rate or cumulative production via an additional clustering of the principle charactersitics of the well. The data sets used to build these models include but are not limited to gas production data (daily volume), drilling data (well logs, fluid summary etc.), completion data (frac, cement bond logs), and pre-production testing data (choke, pressure etc.)
Initial results indicate that this approach is a feasible approach, on target in terms of accuracy with traditional methods and represents a novel, data driven, method of identifying optimal parameter settings for desired production levels; with the ability to perform forecasts and optimization scenarios in run-time.
The approach of using machine learning for production forecasting and production optimization in run- time has immense values in terms of the ability to augment domain expertise and create detailed studies in a fraction of the time that is typically required using traditional approaches. Building on same approach to optimise the field to deliver most reliable or most effeciently against a parameter will be an invaluable feature for overall asset optimisation.