bluuebunny/crossref_metadata_2025_split huggingface dataset:
Going Fast Track for Well and Reservoir Evaluations
Authors: Lopez David , Kumar Sanjeev 
Published: October 2019

Abstract
The aim of this paper is to outline a fast and reliable workflow that allows delivering a rapid and high-level evaluation of reservoir performance based on surveillance data. Exploration prospects, single well analysis and quality control of full field models are typical opportunities for the application of fast track evaluations. The integration of data in simplified workflows is a sufficiently robust approach to provide reasonable analyses, mainly where time and resources are limited.
To illustrate the fast track process, petrophysical and well test evaluations are combined to generate sufficient information to enable the prediction of reservoir performance using a very simplistic and optimized workflow. Wireline data and information from permanent downhole pressure and temperature gauges are used to assess well performance and to forecast potential reservoir offtake. To test the effectiveness of the workflow, a case study from a field in Europe has been carried out.
Well production testing is extensively used during the appraisal stage, to build confidence that hydrocarbons can be commercially produced, and during field development, to monitor well productivity performance through time. Using well test analysis (WTA) reservoir characterization, reservoir container volume, depletion mechanisms, reservoir connectivity and distance of wells to boundaries can be addressed, using as input, petrophysical, geological data and fluid properties. Petrophysical data is fundamental to the evaluation of well potential and the identification of possible productivity issues related to well integrity and/or formation damage. A pressure derivative plot against time, with both axes on a logarithmic scale, shows a specific fingerprint associated to geological features such as layering, permeability, faults, fractures and channels.
This paper will be of interest to engineers, geoscientists and asset managers who need a quick evaluation workflow that integrates well and reservoir data to deliver consistent and robust assessments. The ability to complete reliable fast track well test analysis has a direct impact on reduction of cost and deferred production for operators and joint venture partners.
Application of Big Data Techniques in a Greenfield Heavy Oil Asset in Kuwait – a Feasibility Study
Authors: Kharghoria Arun , Gonzalez Santiago , Al-Rabah Abdullah Abdul , Kaushik Alok , Ujjal Manu , Singhal Manu , Montero Jacobo , Gonzalez Gregorio , Cheers Mike , Zijlstra Ellen , Rawnsley Keith 
Published: October 2019

Abstract
The objective of this study was to assess the feasibility of application of analytics techniques in a new heavy oil asset in Kuwait in the following areas: data integration and visualization to support Well, Reservoir and Facility Management (WRFM), understanding well production behavior and their link to reservoir parameters, investigating reasons for sand failure. The study also aimed to highlight focus areas that would facilitate the full field implementation of analytics as a viable WRFM tool.
Due to the green field nature, the current volume of data is relatively small (versus mature assets), and not all the data required is available yet. The study started with formatting, processing and integrating all the available field data into a data management tool. Integrated visualizations were tested to detect early trends of sand and water production. Machine Learning algorithms such as Random Forest, Decision Tree and Neural Network were applied next to the sand prediction problem, focusing on identifying root causes for repetitive sand failures in wells and if possible to predict initial or subsequent sand failures.
The study indicated that integrated visualizations are very promising in support of WRFM in this field in the short term. Early signs of water breakthrough were correlated, particularly in wells with specific combinations of geological features and completion strategies. The sand prediction problem proved to be very challenging to the Machine Learning approaches, with limited success in predicting the historical occurrences. The results indicated that these techniques are likely to be more applicable once the volume of data increases, particularly the higher resolution data (real time data from artificial lift equipment), as well as by incorporating additional data types (sand production measurements during tests, which require additional resources to execute) and other data types available but not tested yet (log data derived parameters). Overall results indicated that analytics should be strongly considered as a valuable tool in the short to medium term in this new field, with efforts in data acquisition and management of the data types identified.
Most of the existing publications on this topic are related to analytics applied in mature fields. This study, conducted in a field still in the early stages, showed the value for early implementation, and highlights that early planning for focused data acquisition can facilitate building initial data-driven models which can be used for prediction purposes. The paper is expected to provide a valuable reference for new heavy oil projects, either under definition or in early production, for the application of data analytics and extend learnings based on machine learning.
A Scalable Approach to Modeling and Integrating a Supergiant Multi-Field Surface Facility Network to Meet Production Optimization Goals - Challenges & Lessons Learned
Authors: Cornwall Rachelle Christine , Al Nuimi Saber Mubarak , Gutierrez Daniel , Tripathi Deepak Tripathi , Hidalgo Melvin Hidalgo , Alkuwaiti Hamda , Soni Sandeep 
Published: October 2019

Abstract
This paper describes the construction, calibration, and analysis of a multi-field integrated asset operations model (IAOM). The strategy is based on a hierarchical approach that introduces process concepts to assure simplicity, flexibility, and scalability as well as integration with external platforms without compromising on accuracy.
As a part of ADNOC Onshore's implementation of the IAOM, well and network models were constructed and calibrated for modelling field scenarios, identifying bottlenecks, and replicating business requirements. The new approach is rooted in concepts to assure maintainability of the network model. A hierarchical approach implemented during construction and calibration was defined in levels from wells to processing facilities, and was later standardized for ease of replication of structures.
Without compromising on accuracy, the new approach delivered ease of construction, calibration, and analysis that was not possible in an earlier field model. The flexibility in the hierarchical construction approach facilitated faster analyses of scenarios such as commingled flow-line effects and the impact of gas lift conversion. Additionally, model convergence time was reduced by half. The embedded scalability features increased efficiency in the modelling process such that the addition of a newly commissioned field to the network model became a simplified task, ensuring the model will be kept up to date. Hierarchy features facilitated ease of scenario definitions at different levels in the network and supported specific analyses.
A nomenclature structure provided flexibility in classifying grouped or related objects for changes in status in defining scenarios. Additional benefits were gained through the ability to easily integrate with other analytical tools such as forecasting software and other intelligent monitoring platforms owing to standardizations built into the network model.
This new approach allows engineers to understand the nuances of modeling and execute associated tasks in a structured and simplified manner, which can be replicated across other assets for increased efficiency and model performance.
Holistic Approach to Estimate Water Breakthrough; A Case Study
Authors: Opoku Daniel , Shehri Dhafer Al , Adjei Stephen 
Published: October 2019

Abstract
Historically, it has been a challenge to analyze and predict water cut or water production. While predicting water production may seem less relevant as compared to oil production, produced water poses many challenges. The difficulty rises when surface facilities cannot handle the water produced and reservoir pressure declines sharply as a result of excess water production.
Multiphase flowmeters (MPFMs) have in the past few decades gradually replaced the conventional approach of metering multiphase flow streams using test separators. However, MPFMs have been faced with several challenges including flow assurance problems and high bottomhole temperatures and pressures. In addition, they have a threshold below which the accuracy of the measurement of water production will be highly questionable due to design and technology limitations. As a matter of economic decisions, it is necessary to detect and estimate early and post water breakthrough trends respectively. Several models to forecast water breakthrough have been developed. Among them is the famous water cut (WC) versus cumulative production (Np) plot.
This paper presents two empirical models to address the inability of MPFM to detect early water breakthrough below a threshold and to provide an alternative technique for modeling post water breakthrough. The models developed in this work predict water breakthrough using fluid volumes, bottomhole pressure and temperature. The first technique predicts early water breakthrough when the plotted function shows a deviation from a straight-line trend. In the second model, the water cut equation is modified for post water breakthrough prediction.
Previous studies of water cut (WC) have focused on the production of water above the MPFMs threshold. The models derived in this paper provide accurate water cut estimates below MPMFs threshold and reliable post water breakthrough analysis.
Innovative Milling Technology Enhances P&A Performance to Save 11 Rig Days
Authors: Stanko Tomasz , Aliyev Mirhuseyn , Maliekkal Harish , Issa Ali , Hamilton Graeme , Stokes David , Gjertsen Ole , White Allen , Den Besten Hendri , Walburg Edward 
Published: October 2019

Abstract
With the increase in Plug and Abandonment activities worldwide, Oil and Gas Exploration and Production Companies are always looking for ways to reduce rig time and overall costs involved in such activities. One of the plug and abandonment objectives is to seal the well and isolate permanently. This requires milling a window inside the casing to set a cement plug to seal the reservoir and prevent hydrocarbon leakage to surface or hydrocarbon movement between different formations, as well as preventing contaminations of aquifers.
In most of the wells, where the cement plug is to be set there are two overlapping casing strings. In standard Plug and Abandonment applications, this will need pilot milling of the inner casing string from surface (couple of 100s to 1000s meters) to the required depth and then section milling a window in the outer casing string, then opening the formation to set a cement plug and seal the reservoir.
Responding to such application, the plug and abandonment research and development team from the service company carried out several studies and conducted multiple simulations to optimize milling performance, which resulted in a new mill cutting structure design called WavEdge technology.
New cutting structure is enhancing the performance of milling by achieving increase in ROP, Durability and getting better swarf quality for easy handling.
A Multi-Disciplinary Approach to Characterize a Heterogeneous Carbonate Reservoir
Authors: Eadulapally Satish Kumar , Al-Murayri Mohammed Taha , Hossam El-Din Ibrahim , Gustin Daniel. P 
Published: October 2019

Abstract
High permeable layers are one of the key factors of heterogeneity in the Mauddud carbonate reservoir. The objective of this study is to use a field scale definitive approach to characterize high permeable layers with limited data control and derisk their presence at pilot scale using multi-disciplinary data sets acquired from EOR pilot wells
An appropriate definition is assigned to characterize high permeable layers based on core and log data. High permeability layers thus defined were indexed as facies logs at well locations. The indexed facies logs were correlated with different data sets in connection with from NMR, FMI, mud logs and tracers. Before incorporating surveillance data, a base case facies model was generated out of multiple distribution runs using facies index logs from pilot wells. The geological uncertainty associated with high permeable facies identified at pilot wells was decreased by incorporating previously acquired PNL logs, C/O logs, tracer, ILTs, PLTs and SNL-HPT data.
It is found that there is no mud loss or any fractured core detected in pilot wells, thus indicating that there is no interference with faults or fracture networks. Tracer test data show short breakthrough times and are correlatable to identified high permeable intervals in the reservoir. The correlation of TDT log saturations, RFT fluid samples and cooling anomalies behind the casing based on SNL-HPT data matches with interpreted Hi Perm layers. This explains a preferential movement of water into the pilot area resulting from potential connection between Hi Perms and surrounding water injector wells outside the pilot area. Finally, the modelling of identified high permeable layers as imprinted facies established quantification of high permeable layers in terms of areal extension, thickness and connectivity between the pilot wells. This multi-disciplinary approach is useful to de-risk the geological uncertainty associated with high permeable intervals in carbonate reservoirs.
Early detection and characterization of high permeable layers within a selected EOR pilot area is beneficial to mitigate preferential sweep and maximize the ultimate oil recovery as a result of chemical EOR depolyment.
Polymer Flood Pilot Design in a Heavy Oil Field North Kuwait
Authors: AlAbbasi Lulwa , Garcia Jose , Zijlstra Ellen , AlMatrook Mohammad , AlRabah Abdullah , Qureshi Faisal , AlMurayri Mohammad 
Published: October 2019

Abstract
This paper presents the design of a polymer flood pilot in a very heterogeneous, sour Heavy Oil reservoir (referred to as HO Reservoir in this paper), which has three times the salinty of sea water with a high mineral content. In addition, the paper illustrates the methodology for the pilot design, location selection, modelling, water source selection, data acquisition plan, and polymer selection and testing.
The purpose of the polymer flood pilot is to derisk a full field development plan, which aims at improving recovery by flooding processes in a shallow Heavy Oil Field with only two years of production data. The aim is to demonstrate polymer flood technical feasibility in the field and to provide data to evaluate economic viability for further expansion to full field. Therefore, the pilot had to be designed in such a way that it leads to representative results and data for the full field development and enables identification of potential risks and reduction of uncertainties. The multi-well pilot is preceded by polymer lab tests and by single-well injection testing.
By a thorough field analysis including static and dynamic modelling, a pilot pattern shape and size were selected and the pilot wells were drilled last year. An extensive data acquisition plan has been made to optimize learnings from the pilot execution phase. Pressure gradients and vertical interference tests measured in open hole on the injector wells show the presence of short scale heterogeneity. This provided also learnings for the variable water cut development observed in other parts of the field. Since produced water was selected as water source for chemical EOR, another challenge that was successfully overcome was the qualification of a HPAM polymer tthat performed stably under highly saline water conditions and high dissolved H2S concentration. This was the result of a comprehensive polymer formulation laboratory program and subsequently followed by a single well chemical tracer test.
Polymer flood in a highly saline, sour reservoir is unprecedented worldwide and needs careful derisking by a pilot. Using produced water instead of seawater will reduce disposal and treatment costs as well as follow KOC's environmental strategy.
Advancing the CFD Simulation of Flashing in Inflow Control Devices
Authors: Nigim Tarek H. , Li Lei , Zhu Da , Lange Carlos F. 
Published: October 2019

Abstract
Flashing flow is a common phenomenon in many industrial applications and, in steam-assisted gravity drainage (SAGD), it is considered a significant process. Flashing may occur if there is a sudden pressure drop in the production well in SAGD, which is a typical in-situ way to extract heavy oil from tar sands. This may change the expected pressure drop across inflow control devices (ICDs), which are the controllers installed within the production well. Flashing is defined as self-boiling of a liquid due to a reduction of pressure, and it is a complex, multiphase flow phenomenon. The main objectives of the present work are to develop and to validate a multiphase computational model that has the ability to predict the thermo-fluid behaviour of the flow during the flashing process inside ICD nozzles, and to assess its effect on the pressure distribution through ICDs in a predictive way. Our computational model is applied to time-averaged, two-phase, adiabatic, turbulent flows. The new computational model can predict the phase changes between liquid and vapour phases based on mechanical effects (pressure). This is achieved by comparing the local pressure in each computational cell with the local vapour pressure. Considering the thermo-fluid complexity together in one model gives such a simulation the potential to be invaluable for a better understanding of roles of the combined mass transfer and the flashing dynamics during the flashing flow process, and to be applied as an industrial inflow control device to choke back steam for SAGD production system. It may assist in obtaining insight and information where measurements would be difficult. Furthermore, the CFD results can be used to generate compact predictive functions of pressure drop within ICDs, and to investigate the effects of both non-condensable gases and solvents in ICDs performance.
Inhibition and Interaction between Iron Sulphide, Zinc Sulphide and Lead Sulphide
Authors: Alharbi Bader , Aljeaban Norah , Graham Alexander , Sorbie Ken 
Published: October 2019

Abstract
Iron sulphide (FeS), zinc sulphide (ZnS) and lead sulphide (PbS) scales have been observed in many sour oil and gas wells. Sulphide scales often form alongside other scales such as calcium carbonate and barium sulphate and such scales can be removed using chemicals like hydrochloric acid (HCl) and chelating agents. However, there are several drawbacks associated with the removal of sulphide scales, for example, HCl acid, which outperforms other dissolvers has a high corrosion rate and generates hydrogen sulphide (H2S) gas as a byproduct. Other dissolvers, including chelating agents, have very low dissolution rates. Therefore, FeS inhibition is much preferred to its removal, if this can be achieved efficiently and economically.
The objective of this paper is to investigate the inhibition efficiency of inhibitors/dispersants for preventing FeS, ZnS and PbS scales. Different scale inhibitor chemistries have been examined over a wide range of parameters, including temperature, salinity, pH and concentrations of Fe, Zn, Pb and sulphide. Static formation and inhibition experiments were conducted and the progress of the reaction was monitored by ion tracking using inductively coupled plasma (ICP) analysis and pH monitoring. Also, filter-blocking inhibition tests were carried out to examine the impact of scale inhibitor concentration on the scaling time.
Polymeric scale inhibitors showed a high inhibition efficiency for ZnS while no inhibition was observed for phosophonate based scale inhibitors. Unlike ZnS, none of the tested scale inhibitors inhibited FeS. High molecular weight scale inhibitors performed well even at high temperature and salinity and maintained the particles suspended in solution for several days. However, high scale inhibitor concentrations were required to prevent the deposition of FeS, particularly when the iron concentration was raised to 100 ppm and above. In SI-1 solutions, it was easier to inhibit PbS and ZnS when they formed concurrently rather than forming PbS followed by ZnS. These results are in line with the difference in the MIC (Minimum Inhibitor Concentration) observed in SI-2 solutions.
Based on these results, the tested polymeric scale inhibitors managed to inhibit ZnS and PbS but failed against FeS under the same conditions despite the fact that the solubility of ZnS and PbS is less than FeS. It was also found that, cation displacement and the sequence of the scale formation had a significant impact on the inhibition efficiency. The size of scale particles was dependent on the type of scale inhibitor and concentration and this was reflected in the scaling time in the dynamic inhibition tests.
Potassium Fertilization for Fresh Market Potato Production in Tropical Soils
Authors: Job André L.G. , Soratto Rogério P. , Fernandes Adalton M. , Assunção Natália S. , Fernandes Fabiana M. , Yagi Renato 
Published: October 2019

Potassium (K) is the most taken up and removed nutrient by potato (Solanum tuberosum L.), and has a great influence on tuber yield and quality. This study was performed to evaluate the effects of three rates (100, 200, and 400 kg K2O ha−1) and two timings of application of K (single application at planting furrow and split application of 50% at planting furrow plus 50% at hilling), as potassium chloride, as well as a control (without K application) on the plant nutrition and tuber yield and quality of potato ‘Agata’ grown in tropical clay soils. The split application of K fertilizer had little influence on plant nutrition and tuber yield and quality. The influence of K fertilization on increasing K and reducing Ca and Mg concentrations in the leaf was more significant in soil with low exchangeable K. In this soil, the maximum tuber yield (33.6 Mg ha−1; 107% higher than the control) was obtained with an estimated rate of 325 kg K2O ha−1, while in the soils with medium and high exchangeable K, the tuber yield was increased between 22 and 34% and only up to a rate of 200 kg K2O ha−1. The critical leaf K concentration to reach 95% of the maximum yield was 29.3 g K kg−1, but there was an extreme increase in the tuber yield even with K rates that provided leaf K concentrations above this limit. Potassium fertilization increased the firmness and reduced soluble solids and protein in tubers.Core Ideas

K is the nutrient that is most taken up and removed by the potato crop.
In clay soils, the splitting of K fertilization has little influence on potato tuber yield and quality.
K fertilization increases tuber yield more, and up to higher rates in soil with low K concentration.
K fertilization increases the tuber firmness, but reduces the soluble solids and protein concentration.