bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluating Performance of Foam-Assisted Lift in Sultanate of Oman by Dedicated Field Testing
Authors: Veeken Kees , Hinai Khalid , Shanfari Abdul Aziz , Yamadi Ahmed , Zadjali Abdullah , Saidi Ali , Hadhrami Hatim , Daraai Thabit , Mans Jeroen , Yousfi Idrees , Hinai Hisham , Wahaibi Yousuf , Musalami Khamis , Medhi Rawa , McIntyre Neil , Kindi Ahmed , Kindi Osama , Naabi Hajer , Mabsali Laila , Dhahri Hisham 
Published: November 2017

Abstract
Foam-assisted lift (FAL) is a well-established gas well deliquification technique to prolonge stable production from depleting, liquid loading gas wells. Field tests were carried out to delineate the operating envelope of FAL in PDO liquid loading gas wells. The field tests in five different wells consisted of step-down tests with and without continuous downhole injection of liquid foamer, to establish the associated reduction of the minimum stable gas rate. At the same time, the produced fluids were sampled and analyzed to diagnose potential impact on surface processing. The step-down tests were carried out using five different foamers that passed screening based on laboratory testing. FAL reduced the minimum stable rate by more than 40% independent of water cut i.e. for BS&W ranging between 14% and 96%. The step-down procedure required a surface choke which resulted in a significant reduction of the minimum stable gas rate, independent of the application of FAL.
Joint Inversion Facies Seismic Inversion to Delineate Thin Multi Layered Heterogeneous Reservoirs Offshore Abu Dhabi
Authors: Goraya Yassar , Bale Sean , Waters Kester , Sams Mark , Khalifa al Nemaimi Ahmed , Al Felasi Ali , Khemissa Hocine , Akram Fazeel , Al-Dhafari Bader 
Published: November 2017

Abstract
Today, the major challenge in reservoir management is improving reservoir characterization to better understand variations in rock properties and distribution of fluids away from the wells. A further challenge is the proper characterziation of thin multi-layered heterogeneous reservoirs. In this paper we present a new workflow to delineate reservoirs at and below seismic resolution.
A reservoir characterization study is performed for the Reservoir Z of the Giant field. The reservoir seqeuce consists of multiple reservoir units (1, 2, and 3) with variable average porosities between 11 and 28%. The study attempts to delineate separate units through an innovative Bayesian inversion technique that jointly solves for impedances and facies (Kemper and Gunning, 2014). Four different seismic facies were determined through an inversion feasibility study from variations in mineralogy and porosity. Elastic property trends as a function of time were built from the petrophysics of two wells. The low frequency component was driven by these trends and prior facies distributions were specified to constrain the results to be geologically reasonable. Three angle stacks were simultaneously inverted using extracted wavelets, each with specified noise estimates. An inversion was run using this technique on post stack data, as well as a coloured inversion for comparison.
Despite varaible data quality, the pre-stack facies based Bayesian inversion was able to invert for three separate layers of high porosity in the reservoir sequence in a number of blind wells. This was a noteable improvement on both the coloured inversion and post-stack inversion. It was concluded that the higher frequenices in the near stack data combined with increased rock physics contraints of the inversion resulted in better thin layer detection.
Produced Water Analysis by X-Ray Fluorescence with and without the Presence of Crude Oil
Authors: Yan C. , Guraieb P. , Ghorbani N. , Tomson R. C. , Abdallah D. , Aouda A. Ben , Odeh N. M. , Al Daghar T. A. 
Published: November 2017

Abstract
Scaling and corrosion are two common problems during oil and gas production which can cause severe safety issues and significant decrease in production. Analysis of produced water is widely applied to evaluate scaling and corrosion by monitoring parameters such as pH, alkalinity, cation concentrations, sulfate concentration, total dissolved solids (TDS) and conductivity/resistivity. Produced water sampling is usually performed at the wellhead for on-shore wells and can be done relatively easily if unpressurized samples are required. However, temperature and pressure drop as well as CO2 degassing during sampling dramatically affect pH and alkalinity measurements. As a result, carbonate scales may precipitate during degassing. For offshore wells, produced water sampling on site is costly and time consuming and mainly performed after abnormal observation of production rate. Thus, scale monitoring and prediction based on analysis of surface produced water is not reliable and analysis under temperature and pressure is required for accurate results.
In this paper, X-ray fluorescence (XRF) is used for monitoring water composition for scale and corrosion prediction with or without the presence of crude oil. To our best knowledge, XRF has not been reported as an analytical approach for water analysis with the presence of crude oil. This technique measures various metal cation concentration quantitatively in oil and water emulsions. It allows for a fast analysis of the water composition without the need for oil and water separation. Combined with automatic water analysis device developed previously, all important water parameters including, but not limited to, pH, alkalinity, sulfate, total hardness, TDS and cation concentrations can be measured for scale and corrosion monitoring and prediction. Measured results will be transfered and automatically incorporated into scale prediction software for scaling risk evaluation and scale inhibitor selection.
In addition, concentration of other metal ions (Na, Mg, Fe, Mo, Ni, Co, Mn, etc.), heavy metal ions (Pb, Zr, Hg, Cd, etc.) and nonmetal elements (S, P, As, etc.) can also be measured. Concentration of Fe, Mo, Ni, Co and Mn has been accurately measured. In situ measurement of transition metal concentrations can be a useful tool for monitoring potential corrosion during production.
The technology developed in this paper allows for automatic online monitoring of scale and corrosion risks under real production conditions. This system accurately measures various parameters and provides valuable produced water information without user interferences and with little maintenance.
Treatment of Produced Polymer-Containing Water with a Water Treatment Pilot Unit in the Matzen Field, Austria
Authors: Leitenmueller Verena , Wenzina Johannes , Kadnar Rainer , Jamek Karl , Hofstaetter Herbert 
Published: November 2017

Abstract
Nowadays, the injection of dilute hydrolyzed polyacrylamide (HPAM) solutions after water flooding operations is a promising tertiary recovery method. However, the treatment of produced water containing breakthrough polymer plays a challenging aspect in the oil and gas industry. Ensuring good filterability of the produced water for further usage, either pressure maintenance or EOR application, is still a critical issue. Polymer loads in the produced water need to be expected, which can massively influence the separation efficiency of the water treatment system. Especially, the handling of polymer-containing water streams and finding the appropriate technology for the treatment, chemically or mechanically, has a decisive influence on performing a full-field roll out of polymer flooding activities.
Aim of this work was to study the impact of back-produced polymer on the water treatment process and to reach the desired injection water quality. Therefore a water treatment plant in pilot scale was used. The unit simulates the main process steps of the water treatment plant Schönkirchen in the Vienna Basin (corrugated plate interceptor, dissolved gas flotation unit, and nutshell filter). The maximum back-produced polymer concentration, which can be handled within the system, was determined. Two different chemical sets (coagulant and flocculant) were tested, regarding their oil and solids removal ability, in presence of different polymer concentrations.
At the end of the field study, one of these chemical sets was found, having a hydrocarbon removal efficiency of around 99% in presence of 30 ppm HPAM inlet concentration. Using this set, good removal efficiency and no plugging of the nutshell filter was observed even at high polymer concentrations. The other set led to plugging of the filtration system at relative low polymer concentrations of 8 ppm HPAM and the removal efficiency of hydrocarbons as well as polymer was poor. Based on these results, it can be assumed that the processes of the water treatment plant Schönkirchen are not negatively affected in the presence of up to 30 ppm polymer load in the inlet water stream.
Study on the Classification and Formation Mechanism of Microscopic Remaining Oil in High Water Cut Stage Based on Machine Learning
Authors: Zhao Yuyun , Jiang Hanqiao , Li Junjian , Wang Chuan , Gao Yajun , Yu Fuwei , Su Hang 
Published: November 2017

Abstract
The formation mechanism and utilization conditions of the remaining oil in the high water cut period play significant roles in improved tapping potential and enhanced oil recovery. The classification of the remaining oil is a difficult point, meanwhile a burning issue. However, the current classification method is mainly through the manual method to determine the boundaries of classification, time-consuming and has a great subjectivity.
Machine learning and data mining methods in recent years have been widely used in the field of petroleum engineering, such as prediction of the recovery factor and so on, especially the well-known k-means classification algorithm.
The first objective of this paper is to use the semi-supervised learning (SSL) method to realize the classification of remaining oil in the high water cut period, based on the database obtained from experiments of 2D etched glass micro-model, with the help of the technique of quantitative characterization of pore structure and micro-residual oil. The method of principal component analysis (PCA) is used to reduce the dimension of the data. According to the formation causes, remaining oil can be divided into four types: oil film, throat retained oil, heterogeneous multi-pores oil and clustered oil. Two typical blocks are identified manually for each class, with an increased weight coefficients, then the other oil blocks with smaller weights are clustered into their types by the seeded k-means algorithm. The result shows that semi-supervised method is more effective than both supervised learning (with manual boundaries) and unsupervised learning methods.
Based on the classification, the effects on the formation of heterogeneous multi-pores oil and throat retained oil are analyzed by statistical method. All of these quantitative studies can provide theoretical guidance for the use of residual oil in high water cut periods and increased oil recovery.
Understanding the H2S Variations in the Upper Arab Formation in Abu Dhabi
Authors: Nederlof P. , Stearns S. , Kaczorowski N. , Lucas N.B. , Ghassan M. 
Published: November 2017

Abstract
The gas accumulations in the Upper Arab Formation in Abu Dhabi are characterized by tight reservoirs and highly variable H2S concentrations. The H2S concentrations range from 5 to 27%, without a clear vertical or lateral trend. With more than 10 reservoir units in the Upper Arab, it is practically impossible to test or take down-hole fluid samples in each individual reservoir. The exact gas compositions of large parts of the Upper Arab Formation therefore remain unknown. This paper describes a new approach to derive (sour) gas compositions from the geochemical analysis of rock samples, thereby enabling more detailed understanding of spatial fluid compositions. Subsequent integration with geologic and reservoir models will allow for more accurate predictions of the composition of future production streams.
In the tight reservoirs of the Upper Arab Formation, down-hole sampling requires long pumping times, during which (unknown quantities of) H2S will be lost to the drilling fluid. Well tests, on the other hand, tend to be short due to strict HSE regulations and stable flow cannot always be established before fluid samples are taken. Often, multiple reservoir zones are tested simultaneously averaging out any variability that exist between the individual zones. The limitations of the sampling techniques, together with the inherent fluid variabilities, create uncertainties for the reservoirs that have been sampled, while no information whatsoever is available from the reservoir units that have not been sampled. To address the uncertainty in reservoir fluid composition, we are developing geochemical technology that allows fluid compositions to be derived from core, sidewall core, and cuttings samples. Rock samples from gas reservoirs contain small amounts of adsorbed gas and condensate, which can be thermally extracted and analyzed. The extracted condensates are enriched in organo-sulfur compounds, which can be used as proxies for the Thermochemical Sulfate Reduction (TSR) reaction and thus for the H2S and CO2 contents of the reservoir fluid. The composition of the gas that was originally trapped in the pore space can thus be mapped away from the down-hole sampling points, also in reservoir units from which no fluids are available.
Based on available PVT data, the organo-sulfur compounds in the condensate and the carbon isotope ratios of the adsorbed gas, we were able to get some insight into the filling history of the Upper Arab reservoirs. Our data suggest that the TSR reaction that generates the H2S and CO2, is not an ‘in-situ’ process. Instead, the H2S and CO2 appear to migrate into the reservoirs with gradually increasing H2S concentrations, following the progress of the TSR reactions.
High-Resolution Porosity-Permeability Logs Driven by Macro-to-Nano Structures and PVT-Pyro-Geochemistry in a Middle East Unconventional Formation
Authors: Al-Shamali Adnan , Mishra P. K. , Verma Naveen K. , Quttainah Riyad , Al Jallad Osama , Grader Avrami , Walls Joel , Koronfol Safouh , Morcote Anyela 
Published: November 2017

Abstract
In Kuwait, the Najmah source rock is characterized by a complex diagenetic history and depositional variability. Accurate determination of the porosity and permeability logs is essential for improved petrophysical evaluation, which may not be properly performed using conventional methods. This complexity makes the conventional evaluation methods alone insufficient to determine porosity and permeability logs accurately. A major goal of this study was to produce high-resolution porosity-permeability logs for Najmah Formation using advanced digital analysis and geochemistry measurements.
Sixty (60) feet of continuous core were analyzed from an oil field in southwest Kuwait. The analysis started with dual-energy x-ray CT scanning of full-diameter whole core and core gamma logging. Plug-size samples were selected to represent the varying porosity and organic matter content along the entire core length. Two-dimensional Scanning Electron Microscopy (2D SEM) and three-dimensional Focused Ion Beam (3D FIB-SEM) images were acquired and analyzed to accurately determine the organic matter content and porosity. Matrix permeability was directly computed from the 3D FIB-SEM images using the Lattice Boltzmann method. The SEM porosity was calibrated by determining the amount of movable hydrocarbons at in-situ reservoir conditions based on geochemical analyses (XRF, XRD and LECO), pyrolysis indices, PVT data and adsorption isotherm experiments.
The digitally obtained porosity and permeability data showed a unique trend that was used to produce permeability at the core level. The integration between digital analysis and geochemistry data increased the estimated porosity and confirmed higher mobile hydrocarbon in the reservoir in comparison with the measured data at the surface. This produced a new porosity-permeability trend that was more representative of the reservoir conditions and caused a significant increase in the rock permeability.
The integration between the digital SEM analysis and the geochemical measurements was critical to estimate in-situ porosity and permeability characteristics of the tight formation under study. Moreover, this analysis provided an important tool for obtaining different high-resolution porosity and permeability logs based on various porosity considerations (effective, organic, inorganic, clay). This would lead to higher accuracy in determining reservoir properties for improved quantification of reserves and productivity.
Modeling Early Time Rate Decline in Unconventional Reservoirs Using Machine Learning Techniques
Authors: Vyas Aditya , Datta-Gupta Akhil , Mishra Srikanta 
Published: November 2017

Abstract
Decline curves are fast methods to predict production behavior in oil and gas wells. Some of the notable decline curve methods are Arp’s, SEDM (Stretched Exponential Decline Model), Duong’s Model and Weibull decline curves. Available production history data can be used to fit any of these equations and future production decline can thus be extrapolated. However, when limited production data is available during early periods of well history, these equations could be fitted using inaccurate parameters leading to erroneous predictions. Also, the traditional decline curve analysis approach does not account for the complexities related to reservoir description and well completions.
This study utilizes publicly available databases of the Eagle Ford formation to develop a novel predictive modeling methodology linking decline curve model parameters to well completion related variables that allows for the rapid generation of synthetic decline curves at potential new well locations. Modern machine learning algorithms such as Random Forests (RF), Support Vector Machines (SVM) and Multivariate Adaptive Regression Splines (MARS) can then be used to model the well decline behavior. Cross-Validation technique such as k-fold cross-validation can be used to quantify the predictive accuracy of these models when applied to new wells.
First, production data are fitted to decline curve models to estimate the corresponding model parameters. Next, machine learning models are built for these parameters as a function of initial flow rate, various well completion parameters (i.e., number of hydraulic fracture stages, completed lengths, proppant and fracturing fluid amounts) and well location/depth parameters (i.e., well latitudes, longitudes, total vertical depth of heel and difference between total vertical depths of heel and toe of horizontal wells). These models are used to rapidly predict the decline curves for new or existing wells without the need for costly reservoir simulators. It has been found that accurate prediction of rate decline of new wells can be predicted using this methodology. This method can also predict ultimate recovery of a new well based on data collected from previous wells.
To our knowledge, this is the first time machine learning algorithms have been used to predict the decline curve parameters and examine the relative performance of various decline curve models. The power and utility of our approach are demonstrated by successful prediction of the decline behavior of blind wells that were not incorporated in the analysis. We also examine the relative influences of various well design and location variables to determine the hidden correlations or interactions among them which are hard to decipher with other methods.
Smart Automated Designs for better HSE Performance
Authors: Akhtar Muhammad Adnan 
Published: November 2017

Abstract
The ADGAS plant facilities include three LNG trains (Trains-1 & 2 commissioned in 1977 and Train-3 commissioned in 1994), Offshore Associated Gas (OAG) and Integrated Gas Development (IGD) facilities along with utilities and other associated facilities such as storage tankage, jetties for export of the products, etc. The gas feedstock comes from various offshore oil and gas fields surrounding Das Island. The feed gas, delivered to the plant at different pressure levels is compressed through different stages and purified before liquefaction. The feed gas contains impurities like CO2 (Carbon Di Oxide) and H2S (Hydrogen Sulphide), which are removed by gas sweetening process. H2S removed by the sweetening process is converted into liquid Sulphur in the Sulphur Recovery Units. After sweetening, the hydrocarbon gas mixture is cooled in stages followed by fractionation and liquefaction to produce different hydrocarbon products ranging from LNG (a mixture of methane and ethane) to Paraffinic Naphtha.
ADGAS commissioned the OAG and IGD facilities in 2010 and 2013 respectively, enabling gas export from Das Island to onshore Habshan facilities through a subsea pipeline. The primary objective of OAG facility is to process additional quantities of LP Gases that has become available at Das Island from ADMA-OPCO, following increase in oil production. The process involves compression of LP Gases, Dehydrate and dispatch 211 mmscfd of Gas & Condensate through marine pipeline to GASCO facility at Habshan. OAG (two processing trains) processes about 200mmscfd gas whereas The IGD was commissioned in the year 2013. This comprises of three Trains capable of delivering 900 mmscfd of dry compressed HP Gas at 155 Bara through marine pipeline to GASCO facility at Habshan.
Using Casedhole Formation Evaluation to Bridge the Gap when Openhole Logging is Not Feasible
Authors: Ramamoorthy Raghu , Al Aryani Fatima Mohamed , Mavromatidis Angelos 
Published: November 2017

Abstract
During drilling, openhole logs provide valuable data to identify the location of hydrocarbon resources, characterize the host formation, and quantify the asset in terms of size and producibility. Such data acquisition during field appraisal plays a critical part in the preparation of the field development plan. However, openhole logs may not be available or may not be possible due to various constraints. In some cases, data that is essential for petrophysical evaluation has been overlooked during the planning of the openhole log acquisition. In other cases, wellbore conditions while drilling may preclude openhole logging in favor of securing the well. This latter scenario can often occur in appraisal wells when knowledge of the pressure regimes in the different formations in the field is still incomplete.
Several logging services are available for acquiring formation information through casing. This capability can provide an excellent option to acquire the necessary formation evaluation data through casing, thereby completing the input to the decision-making process.
We present three separate examples in which casedhole formation evaluation was used to augment data acquired in the open hole for an improved formation evaluation. The applications range from providing reliable density and sonic data for seismic tie-in to providing more accurate estimates of porosity and saturation for selection of test intervals or for input to the static geological model. The examples demonstrate the use of various through-casing formation evaluation technologies including density, neutron, resistivity, acoustic compressional and shear slowness, and pulsed neutron capture and inelastic spectroscopy logs. We show that through a judicious combination of logs the primary evaluation objectives can be fulfilled.
We discuss the challenges in data acquisition, the steps for quality assessment of the data acquired through casing, and interpretation procedures to integrate all the data in an answer product. The substantial benefit of such an option to the operator is also discussed.